Russell Ballestrini
e28dc11f04
- Fix battleship feedback perspective confusion with better Hermes prompting - Add auto-play TTS button with localStorage persistence and queueing system - Move activity controls below model/voice selectors in sidebar - Add activity controls to mobile hamburger menu - Fix model/activity dropdowns to stay within container bounds - Filter activities API to only show .yaml/.yml files - Clean up system message labels by moving to usernames (System (Feedback), System (Question)) - Apply black formatting to app.py
2643 lines
93 KiB
Python
2643 lines
93 KiB
Python
# import eventlet
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# eventlet.monkey_patch()
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import gevent
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from gevent import monkey
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monkey.patch_all()
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import json
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import yaml
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import os
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import random
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import boto3
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import together
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from flask import (
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Flask,
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render_template,
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request,
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send_from_directory,
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jsonify,
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Response,
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)
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from flask_socketio import SocketIO, emit, join_room, leave_room
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from flask_sqlalchemy import SQLAlchemy
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from sqlalchemy.exc import InvalidRequestError
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from models import db, Room, UserSession, Message, ActivityState
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app = Flask(__name__, instance_relative_config=True)
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app.config["SECRET_KEY"] = os.environ.get("SECRET_KEY", "dev-key-change-in-production")
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app.config["SQLALCHEMY_DATABASE_URI"] = (
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f"sqlite:///{os.path.join(app.instance_path, 'chat.db')}"
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)
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app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False
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db.init_app(app)
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from flask_migrate import Migrate
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migrate = Migrate(app, db)
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# socketio = SocketIO(app, async_mode="eventlet")
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socketio = SocketIO(app, async_mode="gevent")
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# Global dictionary to keep track of cancellation requests
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cancellation_requests = {}
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from openai import OpenAI
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# Build a list of endpoints dynamically.
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ENDPOINTS = []
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MAX_ENDPOINTS = 1000
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for i in range(MAX_ENDPOINTS):
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endpoint = os.environ.get(f"MODEL_ENDPOINT_{i}")
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if not endpoint:
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continue
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# API key is optional; if not provided, use a default.
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api_key = os.environ.get(f"MODEL_API_KEY_{i}", "not-needed")
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ENDPOINTS.append(
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{
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"base_url": endpoint,
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"api_key": api_key,
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}
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)
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if not ENDPOINTS:
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raise Exception("No MODEL_ENDPOINT_x environment variables found!")
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# Build a dynamic model map by querying each endpoint.
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MODEL_CLIENT_MAP = {}
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SYSTEM_USERS = []
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def get_client_for_endpoint(endpoint, api_key):
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# All providers use the OpenAI client; no endpoint URLs are hardcoded here.
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return OpenAI(api_key=api_key, base_url=endpoint)
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def initialize_model_map():
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global MODEL_CLIENT_MAP, SYSTEM_USERS
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MODEL_CLIENT_MAP.clear()
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for ep_config in ENDPOINTS:
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base_url = ep_config["base_url"]
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api_key = ep_config["api_key"]
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client = get_client_for_endpoint(base_url, api_key)
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try:
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response = client.models.list()
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model_list = response.data # Assume each model object has an 'id' attribute
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print(f"[DEBUG] {base_url} returned models: {[m.id for m in model_list]}")
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except Exception as e:
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print(f"[WARN] Could not list models for endpoint '{base_url}': {e}")
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continue
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for m in model_list:
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model_id = m.id
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if model_id and model_id not in MODEL_CLIENT_MAP:
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MODEL_CLIENT_MAP[model_id] = (client, base_url)
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# Populate SYSTEM_USERS with dynamically loaded models.
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SYSTEM_USERS = list(MODEL_CLIENT_MAP.keys())
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print("Loaded models:", list(MODEL_CLIENT_MAP.keys()))
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if MODEL_CLIENT_MAP:
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pass
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else:
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initialize_model_map()
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# 4) Lookup function: get an OpenAI client for a given model name
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def get_client_for_model(model_name: str):
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"""
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If the model name is known, return its dedicated client.
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Otherwise, return None and LOL at the user when everything breaks.
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"""
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if model_name in MODEL_CLIENT_MAP:
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print(f"Completion Endpoint Processing: {MODEL_CLIENT_MAP[model_name][1]}")
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return MODEL_CLIENT_MAP[model_name][0]
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def get_openai_client_and_model(
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model_name="adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic",
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):
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return get_client_for_model(model_name), model_name
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HELP_MESSAGE = """
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**Available Commands:**
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- `/activity [s3_file_path]`: Start an activity from the specified S3 file path.
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- `/activity cancel`: Cancel the current activity.
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- `/activity info`: Display information about the current activity.
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- `/activity metadata`: Display metadata for the current activity.
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- `/s3 ls [s3_file_path_pattern]`: List files in S3 matching the pattern.
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- `/s3 load [s3_file_path]`: Load a file from S3.
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- `/s3 save [s3_key_path]`: Save the most recent code block from the chatroom to S3.
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- `/title new`: Generates a new title which reflects conversation content for the current chatroom.
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- `/cancel`: Cancel the most recent chat completion from streaming into the chatroom.
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- `/help`: Display this help message.
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**Interacting with AI Models:**
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- Select a model from the dropdown menu above the chat input. Available models are dynamically loaded from configured endpoints and include options like `gpt-4o-mini`, `llama3-70b-8192`, `anthropic.claude-3-sonnet-20240229-v1:0`, and `dall-e-3` for image generation.
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- Type your message and send it. The selected model will respond if it's not "None".
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- For image generation, select `dall-e-3` and provide a prompt (e.g., "A futuristic cityscape").
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**Getting Started:**
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Welcome to the chatroom! Here, you can explore various AI models and engage in interactive activities. Here's how you can get started:
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1. **Explore the Chatroom:**
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- Join a chatroom by navigating to its unique URL. You can see the list of available chatrooms on the main page.
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- Once inside, you can start a conversation by typing your message in the chatbox.
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2. **Start an Activity:**
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- To begin an educational activity, use the `/activity` command followed by the path to the activity YAML file. For example:
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**Getting Started:**
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Welcome to the chatroom! Here, you can explore various AI models and engage in interactive activities. Here's how you can get started:
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1. **Explore the Chatroom:**
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- Join a chatroom by navigating to its unique URL. You can see the list of available chatrooms on the main page.
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- Once inside, you can start a conversation by typing your message in the chatbox.
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2. **Start an Activity:**
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- To begin an educational activity, use the `/activity` command followed by the path to the activity YAML file. For example:
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```
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/activity research/activity0.yaml
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```
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- The AI will guide you through the activity, providing feedback and information as you progress.
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3. **Interact with AI Models:**
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- To interact with a specific AI model, simply type the model's command followed by your prompt. For example:
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```
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gpt-4 What is the capital of France?
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```
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- The system will process your message and provide a response from the selected model.
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4. **Manage Files with S3:**
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- Use the `/s3` commands to load, save, or list files in your S3 bucket. For example, to list all files, use:
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```
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/s3 ls *
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```
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5. **Get Help:**
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- If you need assistance or want to see a list of available commands, type `/help` to display this message.
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Feel free to explore and experiment with different commands and models. Enjoy your time in the chatroom!
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"""
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def get_room(room_name):
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"""Utility function to get room from room name."""
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room = Room.query.filter_by(name=room_name).first()
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if room:
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return room
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else:
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# Create a new room since it doesn't exist
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new_room = Room()
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new_room.name = room_name
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db.session.add(new_room)
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db.session.commit()
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return new_room
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def get_s3_client():
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"""Utility function to get the S3 client with the appropriate profile."""
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if app.config.get("PROFILE_NAME"):
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session = boto3.Session(profile_name=app.config["PROFILE_NAME"])
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s3_client = session.client("s3")
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else:
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s3_client = boto3.client("s3")
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return s3_client
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@app.route("/favicon.ico")
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def favicon():
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return send_from_directory(os.path.join(app.root_path, "static"), "favicon.ico")
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@app.route("/")
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def index():
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return render_template("index.html")
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@app.route("/models", methods=["GET"])
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def get_models():
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# Optionally refresh or reinitialize the model map here.
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# For now we simply return the keys.
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return jsonify({"models": list(MODEL_CLIENT_MAP.keys())})
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@app.route("/api/activities", methods=["GET"])
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def get_activities():
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"""Return the list of available activities."""
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activities = []
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if app.config.get("LOCAL_ACTIVITIES"):
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# List local activity files from research directory
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import os
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research_dir = "research"
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if os.path.exists(research_dir):
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for filename in sorted(os.listdir(research_dir)):
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if filename.endswith((".yaml", ".yml")):
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activities.append(f"research/{filename}")
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else:
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# For S3 activities, you would list from S3
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# This is a placeholder - you'd need to implement S3 listing
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pass
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return jsonify({"activities": activities})
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@app.route("/chat/<room_name>")
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def chat(room_name):
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# Query all rooms so that newest is first.
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rooms = Room.query.order_by(Room.id.desc()).all()
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# Get username from query parameters
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username = request.args.get("username", "guest")
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# Pass username and rooms into the template
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return render_template(
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"chat.html", room_name=room_name, rooms=rooms, username=username
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)
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@app.route("/download_chat_history", methods=["GET"])
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def download_chat_history():
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room_name = request.args.get("room_name")
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room = get_room(room_name)
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if not room:
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return jsonify({"error": "Room not found"}), 404
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messages = Message.query.filter_by(room_id=room.id).all()
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if not messages:
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return jsonify({"error": "No messages found"}), 404
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chat_history = [
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{
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"role": "system" if message.username in SYSTEM_USERS else "user",
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"content": message.content,
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}
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for message in messages
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if not message.is_base64_image()
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]
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if not chat_history:
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return jsonify({"error": "No valid messages found"}), 404
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response = Response(
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response=json.dumps(chat_history, indent=2),
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status=200,
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mimetype="application/json",
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)
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response.headers["Content-Disposition"] = f"attachment; filename={room.name}.json"
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return response
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@app.route("/download_chat_history_md", methods=["GET"])
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def download_chat_history_md():
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room_name = request.args.get("room_name")
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room = get_room(room_name)
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if not room:
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return jsonify({"error": "Room not found"}), 404
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messages = Message.query.filter_by(room_id=room.id).all()
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if not messages:
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return jsonify({"error": "No messages found"}), 404
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# Access system users from the existing context
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chat_history_md = []
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toc = []
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for index, message in enumerate(messages):
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if not message.is_base64_image(): # Correctly call the method
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role = "System" if message.username in SYSTEM_USERS else "User"
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header = f"### {role}: {message.username} (Turn {index + 1})"
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toc.append(
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f"- [{role}: {message.username} (Turn {index + 1})](#{role.lower()}-{message.username.lower().replace(' ', '-')}-turn-{index + 1})"
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)
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chat_history_md.append(f"{header}\n\n{message.content}\n\n---\n")
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if not chat_history_md:
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return jsonify({"error": "No valid messages found"}), 404
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markdown_content = (
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f"# Chat History for {room.name}\n\n## Table of Contents\n"
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+ "\n".join(toc)
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+ "\n\n"
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+ "\n".join(chat_history_md)
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)
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response = Response(response=markdown_content, status=200, mimetype="text/markdown")
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response.headers["Content-Disposition"] = f'attachment; filename="{room.name}.md"'
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return response
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@app.route("/search")
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def search_page():
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# Query all rooms so that newest is first.
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rooms = Room.query.order_by(Room.id.desc()).all()
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keywords = request.args.get("keywords", "")
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username = request.args.get("username", "guest")
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if not keywords:
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return render_template(
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"search.html",
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rooms=rooms,
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keywords=keywords,
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results=[],
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username=username,
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error="Keywords are required",
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)
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# Call the function to search messages
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search_results = search_messages(keywords)
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return render_template(
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"search.html",
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rooms=rooms,
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keywords=keywords,
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results=search_results,
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username=username,
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error=None,
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)
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def search_messages(keywords):
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search_results = {}
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# Split the keywords by spaces and sanitize
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keyword_list = keywords.lower().split()
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# Sanitize keywords to prevent SQL injection
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sanitized_keywords = []
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for keyword in keyword_list:
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# Remove potentially dangerous characters and limit length
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sanitized_keyword = "".join(
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c for c in keyword if c.isalnum() or c.isspace() or c in "-_"
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)[:50]
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if sanitized_keyword.strip(): # Only add non-empty keywords
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sanitized_keywords.append(sanitized_keyword.strip())
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if not sanitized_keywords:
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return {}
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# Search for messages containing any of the sanitized keywords using parameterized query
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messages = Message.query.filter(
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db.or_(
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*[Message.content.ilike(f"%{keyword}%") for keyword in sanitized_keywords]
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)
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).all()
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for message in messages:
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room = Room.query.get(message.room_id)
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if room:
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# Calculate the score based on the number of occurrences of all keywords
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score = sum(
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message.content.lower().count(keyword) for keyword in keyword_list
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)
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if room.id not in search_results:
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search_results[room.id] = {
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"room_id": room.id,
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"room_name": room.name,
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"room_title": room.title,
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"score": 0,
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}
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search_results[room.id]["score"] += score
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# Convert the dictionary to a list and sort results by score in descending order
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search_results_list = list(search_results.values())
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search_results_list.sort(key=lambda x: x["score"], reverse=True)
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return search_results_list
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# Handle user joining a room
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@socketio.on("join")
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def on_join(data):
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room_name = data["room_name"]
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username = data["username"]
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room = get_room(room_name)
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# Add the user to the active users list
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room.add_user(username)
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# Store session data in the database
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user_session = UserSession(
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session_id=request.sid, username=username, room_name=room_name, room_id=room.id
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)
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db.session.add(user_session)
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db.session.commit()
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# Emit the active and inactive users list to the new joiner
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emit(
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"active_users",
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{
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"active_users": room.get_active_users(),
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"inactive_users": room.get_inactive_users(),
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},
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room=request.sid,
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)
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# Emit the active and inactive users list to everyone in the room
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emit(
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"active_users",
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{
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"active_users": room.get_active_users(),
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"inactive_users": room.get_inactive_users(),
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},
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room=room_name,
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include_self=False,
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)
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# This makes the client start listening for new events for this room.
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join_room(room_name)
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# update the title bar with the proper room title, if it exists for just this new client.
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if room.title:
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socketio.emit("update_room_title", {"title": room.title}, room=request.sid)
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# Fetch previous messages from the database
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previous_messages = Message.query.filter_by(room_id=room.id).all()
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# count the number of tokens in this room.
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total_token_count = 0
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# Send the history of messages only to the newly connected client.
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for message in previous_messages:
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if not message.is_base64_image():
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total_token_count += message.token_count
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emit(
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"previous_messages",
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{
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"id": message.id,
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"username": message.username,
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"content": message.content,
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},
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room=request.sid,
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)
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message_count = len(previous_messages)
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if room.title is None and message_count >= 6:
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room.title = gpt_generate_room_title(previous_messages)
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db.session.add(room)
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socketio.emit("update_room_title", {"title": room.title}, room=room.name)
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# Emit an event to update this room's title in the sidebar for all users.
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updated_room_data = {"id": room.id, "name": room.name, "title": room.title}
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socketio.emit("update_room_list", updated_room_data, room=None)
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# commit session & active user list and title to database.
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db.session.commit()
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# Broadcast to all clients in the room that a new user has joined.
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emit(
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"chat_message",
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{"id": None, "content": f"{username} has joined the room."},
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room=room.name,
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)
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emit(
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"chat_message",
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{
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"id": None,
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"content": f"Estimated {total_token_count} total tokens in conversation.",
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},
|
|
room=request.sid,
|
|
)
|
|
|
|
|
|
# Handle user leaving a room
|
|
@socketio.on("disconnect")
|
|
def on_disconnect():
|
|
sid = request.sid
|
|
user_session = UserSession.query.filter_by(session_id=sid).first()
|
|
|
|
if user_session:
|
|
room_name = user_session.room_name
|
|
username = user_session.username
|
|
room = Room.query.filter_by(name=room_name).first()
|
|
room.remove_user(username)
|
|
leave_room(room_name)
|
|
# Broadcast to all clients in the room that a user has left the room.
|
|
# Emit the active and inactive users list to everyone in the room
|
|
emit(
|
|
"active_users",
|
|
{
|
|
"active_users": room.get_active_users(),
|
|
"inactive_users": room.get_inactive_users(),
|
|
},
|
|
room=room.name,
|
|
include_self=False,
|
|
)
|
|
emit(
|
|
"chat_message",
|
|
{"id": None, "content": f"{username} has left the room."},
|
|
room=room.name,
|
|
include_self=False,
|
|
)
|
|
# Remove session data from the database
|
|
db.session.delete(user_session)
|
|
db.session.commit()
|
|
|
|
|
|
@socketio.on("chat_message")
|
|
def handle_message(data):
|
|
room_name = data["room_name"]
|
|
room = get_room(room_name)
|
|
username = data["username"]
|
|
message = data["message"].strip()
|
|
model = data.get("model", "None")
|
|
|
|
new_message = Message(
|
|
username=username,
|
|
content=message,
|
|
room_id=room.id,
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": username,
|
|
"content": message,
|
|
},
|
|
room=room.name,
|
|
)
|
|
|
|
commands = message.splitlines()
|
|
for command in commands:
|
|
if command.startswith("/help"):
|
|
socketio.emit(
|
|
"chat_message",
|
|
{"id": "tmp-1", "username": "System", "content": HELP_MESSAGE},
|
|
room=room_name,
|
|
)
|
|
return
|
|
if command.startswith("/activity cancel"):
|
|
gevent.spawn(cancel_activity, room_name, username)
|
|
return
|
|
if command.startswith("/activity info"):
|
|
gevent.spawn(display_activity_info, room_name, username)
|
|
return
|
|
if command.startswith("/activity metadata"):
|
|
gevent.spawn(display_activity_metadata, room_name, username)
|
|
return
|
|
if command.startswith("/activity"):
|
|
s3_file_path = command.split(" ", 1)[1].strip()
|
|
gevent.spawn(start_activity, room_name, s3_file_path, username)
|
|
return
|
|
if command.startswith("/s3 ls"):
|
|
s3_file_path_pattern = command.split(" ", 2)[2].strip()
|
|
gevent.spawn(list_s3_files, room.name, s3_file_path_pattern, username)
|
|
if command.startswith("/s3 load"):
|
|
s3_file_path = command.split(" ", 2)[2].strip()
|
|
gevent.spawn(load_s3_file, room_name, s3_file_path, username)
|
|
if command.startswith("/s3 save"):
|
|
s3_key_path = command.split(" ", 2)[2].strip()
|
|
gevent.spawn(save_code_block_to_s3, room_name, s3_key_path, username)
|
|
if command.startswith("/title new"):
|
|
gevent.spawn(generate_new_title, room_name, username)
|
|
if command.startswith("/cancel"):
|
|
gevent.spawn(cancel_generation, room_name)
|
|
|
|
activity_state = ActivityState.query.filter_by(room_id=room.id).first()
|
|
if activity_state:
|
|
gevent.spawn(handle_activity_response, room_name, message, username)
|
|
return
|
|
|
|
if model != "None":
|
|
emit(
|
|
"chat_message",
|
|
{"id": None, "content": "<span id='processing'>Processing...</span>"},
|
|
room=room.name,
|
|
)
|
|
if "anthropic.claude" in model:
|
|
gevent.spawn(chat_claude, username, room_name, model_name=model)
|
|
if "dall-e" in model:
|
|
gevent.spawn(generate_dalle_image, room_name, message, username)
|
|
else:
|
|
# All other models (Groq, Together, Mistral, etc.) use OpenAI client
|
|
gevent.spawn(chat_gpt, username, room_name, model_name=model)
|
|
|
|
|
|
@socketio.on("delete_message")
|
|
def handle_delete_message(data):
|
|
msg_id = data["message_id"]
|
|
# Delete the message from the database
|
|
message = db.session.query(Message).filter(Message.id == msg_id).one_or_none()
|
|
if message:
|
|
db.session.delete(message)
|
|
db.session.commit()
|
|
|
|
# Notify all clients in the room to remove the message from their DOM
|
|
emit("message_deleted", {"message_id": msg_id}, room=data["room_name"])
|
|
|
|
|
|
@socketio.on("update_message")
|
|
def handle_update_message(data):
|
|
message_id = data["message_id"]
|
|
new_content = data["content"]
|
|
room_name = data["room_name"]
|
|
|
|
# Find the message by ID
|
|
message = Message.query.get(message_id)
|
|
if message:
|
|
# Update the message content
|
|
message.content = new_content
|
|
message.count_tokens()
|
|
db.session.add(message)
|
|
db.session.commit()
|
|
|
|
# Emit an event to update the message on all clients
|
|
emit(
|
|
"message_updated",
|
|
{
|
|
"message_id": message_id,
|
|
"content": new_content,
|
|
"username": message.username,
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
|
|
@socketio.on("get_activity_status")
|
|
def handle_get_activity_status(data):
|
|
"""Get the current activity status for a room."""
|
|
room_name = data["room_name"]
|
|
room = get_room(room_name)
|
|
|
|
if room:
|
|
activity_state = ActivityState.query.filter_by(room_id=room.id).first()
|
|
|
|
if activity_state:
|
|
emit(
|
|
"activity_status",
|
|
{
|
|
"active": True,
|
|
"activity_name": activity_state.s3_file_path,
|
|
"section_id": activity_state.section_id,
|
|
"step_id": activity_state.step_id,
|
|
},
|
|
room=request.sid,
|
|
)
|
|
else:
|
|
emit("activity_status", {"active": False}, room=request.sid)
|
|
|
|
|
|
def group_consecutive_roles(messages):
|
|
if not messages:
|
|
return []
|
|
|
|
grouped_messages = []
|
|
current_role = messages[0]["role"]
|
|
current_content = []
|
|
|
|
for message in messages:
|
|
if message["role"] == current_role:
|
|
current_content.append(message["content"])
|
|
else:
|
|
grouped_messages.append(
|
|
{"role": current_role, "content": " ".join(current_content)}
|
|
)
|
|
current_role = message["role"]
|
|
current_content = [message["content"]]
|
|
|
|
# Append the last grouped message
|
|
grouped_messages.append(
|
|
{"role": current_role, "content": " ".join(current_content)}
|
|
)
|
|
|
|
return grouped_messages
|
|
|
|
|
|
def chat_claude(
|
|
# username, room_name, model_name="anthropic.claude-3-5-sonnet-20240620-v1:0"
|
|
username,
|
|
room_name,
|
|
model_name="anthropic.claude-3-sonnet-20240229-v1:0",
|
|
):
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
# claude has a 200,000 token context window for prompts.
|
|
all_messages = (
|
|
Message.query.filter_by(room_id=room.id).order_by(Message.id.desc()).all()
|
|
)
|
|
|
|
chat_history = []
|
|
for msg in reversed(all_messages):
|
|
if msg.is_base64_image():
|
|
continue
|
|
role = "assistant" if msg.username in SYSTEM_USERS else "user"
|
|
chat_history.append({"role": role, "content": msg.content})
|
|
|
|
# only claude cares about this constrant.
|
|
chat_history = group_consecutive_roles(chat_history)
|
|
|
|
# Initialize the Bedrock client using boto3 and profile name.
|
|
if app.config.get("PROFILE_NAME"):
|
|
session = boto3.Session(profile_name=app.config["PROFILE_NAME"])
|
|
client = session.client("bedrock-runtime", region_name="us-west-2")
|
|
else:
|
|
client = boto3.client("bedrock-runtime", region_name="us-west-2")
|
|
|
|
# Define the request parameters
|
|
params = {
|
|
"modelId": model_name,
|
|
"contentType": "application/json",
|
|
"accept": "*/*",
|
|
"body": json.dumps(
|
|
{
|
|
"messages": chat_history,
|
|
"max_tokens": 4096,
|
|
"temperature": 0,
|
|
"top_k": 250,
|
|
"top_p": 0.999,
|
|
"stop_sequences": ["\n\nHuman:"],
|
|
"anthropic_version": "bedrock-2023-05-31",
|
|
}
|
|
).encode(),
|
|
}
|
|
|
|
# Process the event stream
|
|
buffer = ""
|
|
|
|
# save empty message, we need the ID when we chunk the response.
|
|
with app.app_context():
|
|
new_message = Message(username=model_name, content=buffer, room_id=room.id)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
msg_id = new_message.id
|
|
|
|
try:
|
|
# Invoke the model with response stream
|
|
response = client.invoke_model_with_response_stream(**params)["body"]
|
|
|
|
first_chunk = True
|
|
for event in response:
|
|
content = ""
|
|
|
|
# Check if there has been a cancellation request, break if there is.
|
|
if cancellation_requests.get(msg_id):
|
|
del cancellation_requests[msg_id]
|
|
break
|
|
|
|
if "chunk" in event:
|
|
chunk_data = json.loads(event["chunk"]["bytes"].decode())
|
|
|
|
if chunk_data["type"] == "content_block_delta":
|
|
if chunk_data["delta"]["type"] == "text_delta":
|
|
content = chunk_data["delta"]["text"]
|
|
|
|
if content:
|
|
buffer += content # Accumulate content
|
|
|
|
if first_chunk:
|
|
socketio.emit(
|
|
"message_chunk",
|
|
{
|
|
"id": msg_id,
|
|
"content": f"**{username} ({model_name}):**\n\n{content}",
|
|
},
|
|
room=room.name,
|
|
)
|
|
first_chunk = False
|
|
else:
|
|
socketio.emit(
|
|
"message_chunk",
|
|
{"id": msg_id, "content": content},
|
|
room=room.name,
|
|
)
|
|
socketio.sleep(0) # Force immediate handling
|
|
|
|
except Exception as e:
|
|
with app.app_context():
|
|
message_content = f"AWS Bedrock Error: {e}"
|
|
new_message = (
|
|
db.session.query(Message).filter(Message.id == msg_id).one_or_none()
|
|
)
|
|
if new_message:
|
|
new_message.content = message_content
|
|
new_message.count_tokens()
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": msg_id,
|
|
"username": model_name,
|
|
"content": message_content,
|
|
},
|
|
room=room_name,
|
|
)
|
|
socketio.emit("delete_processing_message", msg_id, room=room.name)
|
|
# exit early to avoid clobbering the error message.
|
|
return None
|
|
|
|
# Save the entire completion to the database
|
|
with app.app_context():
|
|
new_message = (
|
|
db.session.query(Message).filter(Message.id == msg_id).one_or_none()
|
|
)
|
|
if new_message:
|
|
new_message.content = buffer
|
|
new_message.count_tokens()
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"message_chunk",
|
|
{"id": msg_id, "content": "", "is_complete": True},
|
|
room=room.name,
|
|
)
|
|
|
|
socketio.emit("delete_processing_message", msg_id, room=room.name)
|
|
|
|
|
|
def chat_gpt(username, room_name, model_name="gpt-4o-mini"):
|
|
openai_client, model_name = get_openai_client_and_model(model_name)
|
|
|
|
temperature = 0
|
|
limit = 20
|
|
if "gpt-4" in model_name:
|
|
limit = 1000
|
|
if "o1-" in model_name:
|
|
temperature = 1
|
|
if "o3-" in model_name:
|
|
temperature = 1
|
|
if "o4-" in model_name:
|
|
temperature = 1
|
|
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
last_messages = (
|
|
Message.query.filter_by(room_id=room.id)
|
|
.order_by(Message.id.desc())
|
|
.limit(limit)
|
|
.all()
|
|
)
|
|
|
|
chat_history = [
|
|
{
|
|
"role": "assistant" if msg.username in SYSTEM_USERS else "user",
|
|
# "content": f"{msg.username}: {msg.content}",
|
|
"content": msg.content,
|
|
}
|
|
for msg in reversed(last_messages)
|
|
if not msg.is_base64_image()
|
|
]
|
|
|
|
buffer = "" # Content buffer for accumulating the chunks
|
|
|
|
# save empty message, we need the ID when we chunk the response.
|
|
with app.app_context():
|
|
new_message = Message(username=model_name, content=buffer, room_id=room.id)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
msg_id = new_message.id
|
|
|
|
first_chunk = True
|
|
|
|
try:
|
|
if "o3" in model_name:
|
|
# o3 does not support temperature at all!
|
|
chunks = openai_client.chat.completions.create(
|
|
model=model_name,
|
|
messages=chat_history,
|
|
n=1,
|
|
stream=True,
|
|
)
|
|
else:
|
|
chunks = openai_client.chat.completions.create(
|
|
model=model_name,
|
|
messages=chat_history,
|
|
n=1,
|
|
temperature=temperature,
|
|
stream=True,
|
|
)
|
|
except Exception as e:
|
|
with app.app_context():
|
|
message_content = f"{model_name} Error: {e}"
|
|
new_message = (
|
|
db.session.query(Message).filter(Message.id == msg_id).one_or_none()
|
|
)
|
|
if new_message:
|
|
new_message.content = message_content
|
|
new_message.count_tokens()
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": msg_id,
|
|
"username": model_name,
|
|
"content": message_content,
|
|
},
|
|
room=room_name,
|
|
)
|
|
socketio.emit("delete_processing_message", msg_id, room=room.name)
|
|
# exit early to avoid clobbering the error message.
|
|
return None
|
|
|
|
for chunk in chunks:
|
|
# Check if there has been a cancellation request, break if there is.
|
|
if cancellation_requests.get(msg_id):
|
|
del cancellation_requests[msg_id]
|
|
break
|
|
|
|
content = chunk.choices[0].delta.content
|
|
|
|
if content:
|
|
buffer += content # Accumulate content
|
|
|
|
if first_chunk:
|
|
socketio.emit(
|
|
"message_chunk",
|
|
{
|
|
"id": msg_id,
|
|
"content": f"**{username} ({model_name}):**\n\n{content}",
|
|
},
|
|
room=room.name,
|
|
)
|
|
first_chunk = False
|
|
else:
|
|
socketio.emit(
|
|
"message_chunk",
|
|
{"id": msg_id, "content": content},
|
|
room=room.name,
|
|
)
|
|
socketio.sleep(0) # Force immediate handling
|
|
|
|
# Save the entire completion to the database
|
|
with app.app_context():
|
|
new_message = (
|
|
db.session.query(Message).filter(Message.id == msg_id).one_or_none()
|
|
)
|
|
if new_message:
|
|
new_message.content = buffer
|
|
new_message.count_tokens()
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"message_chunk",
|
|
{"id": msg_id, "content": "", "is_complete": True},
|
|
room=room.name,
|
|
)
|
|
|
|
socketio.emit("delete_processing_message", msg_id, room=room.name)
|
|
|
|
|
|
def chat_llama(username, room_name, model_name="mistral-7b-instruct-v0.2.Q3_K_L.gguf"):
|
|
import llama_cpp
|
|
|
|
# https://llama-cpp-python.readthedocs.io/en/latest/api-reference/
|
|
model = llama_cpp.Llama(model_name, n_gpu_layers=-1, n_ctx=32000)
|
|
|
|
limit = 15
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
last_messages = (
|
|
Message.query.filter_by(room_id=room.id)
|
|
.order_by(Message.id.desc())
|
|
.limit(limit)
|
|
.all()
|
|
)
|
|
|
|
chat_history = [
|
|
{
|
|
"role": "system" if msg.username in SYSTEM_USERS else "user",
|
|
"content": f"{msg.username}: {msg.content}",
|
|
}
|
|
for msg in reversed(last_messages)
|
|
if not msg.is_base64_image()
|
|
]
|
|
|
|
buffer = "" # Content buffer for accumulating the chunks
|
|
|
|
# save empty message, we need the ID when we chunk the response.
|
|
with app.app_context():
|
|
new_message = Message(username=model_name, content=buffer, room_id=room.id)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
msg_id = new_message.id
|
|
|
|
first_chunk = True
|
|
|
|
try:
|
|
chunks = model.create_chat_completion(
|
|
messages=chat_history,
|
|
stream=True,
|
|
)
|
|
except Exception as e:
|
|
with app.app_context():
|
|
message_content = f"LLama Error: {e}"
|
|
new_message = (
|
|
db.session.query(Message).filter(Message.id == msg_id).one_or_none()
|
|
)
|
|
if new_message:
|
|
new_message.content = message_content
|
|
new_message.count_tokens()
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": msg_id,
|
|
"username": model_name,
|
|
"content": message_content,
|
|
},
|
|
room=room_name,
|
|
)
|
|
socketio.emit("delete_processing_message", msg_id, room=room.name)
|
|
# exit early to avoid clobbering the error message.
|
|
return None
|
|
|
|
for chunk in chunks:
|
|
# Check if there has been a cancellation request, break if there is.
|
|
if cancellation_requests.get(msg_id):
|
|
del cancellation_requests[msg_id]
|
|
break
|
|
|
|
content = chunk["choices"][0]["delta"].get("content")
|
|
|
|
if content:
|
|
buffer += content # Accumulate content
|
|
|
|
if first_chunk:
|
|
socketio.emit(
|
|
"message_chunk",
|
|
{
|
|
"id": msg_id,
|
|
"content": f"**{username} ({model_name}):**\n\n{content}",
|
|
},
|
|
room=room.name,
|
|
)
|
|
first_chunk = False
|
|
else:
|
|
socketio.emit(
|
|
"message_chunk",
|
|
{"id": msg_id, "content": content},
|
|
room=room.name,
|
|
)
|
|
socketio.sleep(0) # Force immediate handling
|
|
|
|
# Save the entire completion to the database
|
|
with app.app_context():
|
|
new_message = (
|
|
db.session.query(Message).filter(Message.id == msg_id).one_or_none()
|
|
)
|
|
if new_message:
|
|
new_message.content = buffer
|
|
new_message.count_tokens()
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"message_chunk",
|
|
{"id": msg_id, "content": "", "is_complete": True},
|
|
room=room.name,
|
|
)
|
|
|
|
socketio.emit("delete_processing_message", msg_id, room=room.name)
|
|
|
|
|
|
def gpt_generate_room_title(messages):
|
|
"""
|
|
Generate a title for the room based on a list of messages.
|
|
"""
|
|
openai_client, model_name = get_openai_client_and_model()
|
|
|
|
chat_history = [
|
|
{
|
|
"role": "system" if msg.username in SYSTEM_USERS else "user",
|
|
"content": f"{msg.username}: {msg.content}",
|
|
}
|
|
for msg in reversed(messages)
|
|
if not msg.is_base64_image()
|
|
]
|
|
|
|
chat_history.append(
|
|
{
|
|
"role": "system",
|
|
"content": "return a short title for the title bar of this conversation.",
|
|
}
|
|
)
|
|
|
|
# Interaction with LLM to generate summary
|
|
# For example, using OpenAI's GPT model
|
|
response = openai_client.chat.completions.create(
|
|
messages=chat_history,
|
|
model=model_name, # or any appropriate model
|
|
max_tokens=20,
|
|
n=1,
|
|
)
|
|
|
|
title = response.choices[0].message.content
|
|
return title.replace('"', "")
|
|
|
|
|
|
def generate_new_title(room_name, username):
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
# Get the last few messages to generate a title
|
|
last_messages = (
|
|
Message.query.filter_by(room_id=room.id)
|
|
.order_by(Message.id.desc())
|
|
.limit(1000) # Adjust the limit as needed
|
|
.all()
|
|
)
|
|
|
|
# Generate the title using the messages
|
|
new_title = gpt_generate_room_title(last_messages)
|
|
|
|
# Update the room title in the database
|
|
room.title = new_title
|
|
db.session.add(room)
|
|
db.session.commit()
|
|
|
|
# Emit the new title to the room.
|
|
socketio.emit("update_room_title", {"title": new_title}, room=room_name)
|
|
|
|
# Emit an event to update this rooms title in the sidebar for all users.
|
|
updated_room_data = {"id": room.id, "name": room.name, "title": room.title}
|
|
socketio.emit("update_room_list", updated_room_data, room=None)
|
|
|
|
# Optionally, send a confirmation message to the room
|
|
confirmation_message = f"New title created: {new_title}"
|
|
new_message = Message(
|
|
username=username, content=confirmation_message, room_id=room.id
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": username,
|
|
"content": confirmation_message,
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
|
|
def generate_dalle_image(room_name, message, username):
|
|
socketio.emit(
|
|
"chat_message",
|
|
{"id": None, "content": "Processing..."},
|
|
room=room_name,
|
|
)
|
|
|
|
openai_client = OpenAI()
|
|
# Initialize the content variable to hold either the image tag or an error message
|
|
content = ""
|
|
|
|
try:
|
|
# Call the DALL-E 3 API to generate an image in base64 format
|
|
response = openai_client.images.generate(
|
|
model="dall-e-3",
|
|
prompt=message,
|
|
n=1,
|
|
size="1024x1024",
|
|
response_format="b64_json",
|
|
)
|
|
|
|
# Access the base64-encoded image data
|
|
image_data = response.data[0].b64_json
|
|
revised_prompt = response.data[0].revised_prompt
|
|
|
|
# Create an HTML img tag with the base64 data (escape user input for XSS protection)
|
|
import html
|
|
|
|
escaped_message = html.escape(message)
|
|
escaped_prompt = html.escape(revised_prompt)
|
|
content = f'<img src="data:image/jpeg;base64,{image_data}" alt="{escaped_message}"><p>{escaped_prompt}</p>'
|
|
|
|
except Exception as e:
|
|
# Set the content to an error message
|
|
content = f"Error generating image: {e}"
|
|
|
|
# Store the content in the database and emit to the frontend
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
new_message = Message(
|
|
username=username,
|
|
content=content, # Store the img tag or error message as the content
|
|
room_id=room.id, # Make sure you have the room ID available
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
# Emit the message with the content to the frontend
|
|
socketio.emit(
|
|
"chat_message",
|
|
{"id": new_message.id, "username": username, "content": content},
|
|
room=room_name,
|
|
)
|
|
|
|
|
|
def find_most_recent_code_block(room_name):
|
|
with app.app_context():
|
|
# Get the room object from the database
|
|
room = get_room(room_name)
|
|
if not room:
|
|
return None # Room not found
|
|
|
|
# Get the most recent message for the room
|
|
latest_message = (
|
|
Message.query.filter_by(room_id=room.id)
|
|
.order_by(Message.id.desc())
|
|
.offset(1)
|
|
.first()
|
|
)
|
|
|
|
if latest_message:
|
|
# Split the message content into lines
|
|
lines = latest_message.content.split("\n")
|
|
# Initialize variables to store the code block
|
|
code_block_lines = []
|
|
code_block_started = False
|
|
for line in lines:
|
|
# Check if the line starts with a code block fence
|
|
if line.startswith("```"):
|
|
# If we've already started capturing, this fence ends the block
|
|
if code_block_started:
|
|
break
|
|
else:
|
|
# Start capturing from the next line
|
|
code_block_started = True
|
|
continue
|
|
elif code_block_started:
|
|
# If we're inside a code block, capture the line
|
|
code_block_lines.append(line)
|
|
|
|
# Join the captured lines to form the code block content
|
|
code_block_content = "\n".join(code_block_lines)
|
|
return code_block_content
|
|
|
|
# No code block found in the latest message
|
|
return None
|
|
|
|
|
|
def save_code_block_to_s3(room_name, s3_key_path, username):
|
|
# Initialize the S3 client
|
|
s3_client = get_s3_client()
|
|
|
|
# Assuming the bucket name is set in an environment variable
|
|
bucket_name = os.environ.get("S3_BUCKET_NAME")
|
|
|
|
# Find the most recent code block
|
|
code_block_content = find_most_recent_code_block(room_name)
|
|
|
|
# Initialize a variable to hold the message content
|
|
message_content = ""
|
|
|
|
if code_block_content:
|
|
try:
|
|
# Save the code block content to S3
|
|
s3_client.put_object(
|
|
Bucket=bucket_name, Key=s3_key_path, Body=code_block_content
|
|
)
|
|
# Set the success message content
|
|
message_content = f"Code block saved to S3 at {s3_key_path}"
|
|
except Exception as e:
|
|
# Set the error message content if S3 save fails
|
|
message_content = f"Error saving file to S3: {e}"
|
|
else:
|
|
# Set the error message content if no code block is found
|
|
message_content = "No code block found to save to S3."
|
|
|
|
# Save the message to the database and emit to the frontend
|
|
with app.app_context():
|
|
# Get the room object from the database
|
|
room = get_room(room_name)
|
|
if room:
|
|
# Create a new message object
|
|
new_message = Message(
|
|
username=username, content=message_content, room_id=room.id
|
|
)
|
|
# Add the new message to the session and commit
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
# Emit the message to the frontend with the new message ID
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": username,
|
|
"content": message_content,
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
|
|
def load_s3_file(room_name, s3_file_path, username):
|
|
# Initialize the S3 client
|
|
s3_client = get_s3_client()
|
|
|
|
# Assuming the bucket name is set in an environment variable
|
|
bucket_name = os.environ.get("S3_BUCKET_NAME")
|
|
|
|
# Initialize message content variable
|
|
message_content = ""
|
|
|
|
try:
|
|
# Retrieve the file content from S3
|
|
response = s3_client.get_object(Bucket=bucket_name, Key=s3_file_path)
|
|
file_content = response["Body"].read().decode("utf-8")
|
|
|
|
# Format the file content as a code block
|
|
message_content = f"```\n{file_content}\n```"
|
|
|
|
except Exception as e:
|
|
# Handle errors (e.g., file not found, access denied)
|
|
message_content = f"Error loading file from S3: {e}"
|
|
|
|
# Save the message to the database and emit to the chatroom
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
new_message = Message(
|
|
username=username,
|
|
content=message_content,
|
|
room_id=room.id,
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
# Emit the message to the chatroom with the message ID
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": username,
|
|
"content": message_content,
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
|
|
def list_s3_files(room_name, s3_file_path_pattern, username):
|
|
import fnmatch
|
|
from datetime import timezone
|
|
|
|
# Initialize the S3 client
|
|
s3_client = get_s3_client()
|
|
|
|
# Assuming the bucket name is set in an environment variable
|
|
bucket_name = os.environ.get("S3_BUCKET_NAME")
|
|
|
|
# Initialize the list to hold all file information
|
|
files = []
|
|
|
|
# Initialize the pagination token
|
|
continuation_token = None
|
|
|
|
# Loop to handle pagination
|
|
while True:
|
|
# List objects in the S3 bucket with pagination support
|
|
list_kwargs = {
|
|
"Bucket": bucket_name,
|
|
}
|
|
if continuation_token:
|
|
list_kwargs["ContinuationToken"] = continuation_token
|
|
|
|
response = s3_client.list_objects_v2(**list_kwargs)
|
|
|
|
# Process the current page of results
|
|
for obj in response.get("Contents", []):
|
|
key = obj["Key"]
|
|
if s3_file_path_pattern == "*" or fnmatch.fnmatch(
|
|
key, s3_file_path_pattern
|
|
):
|
|
size = obj["Size"]
|
|
last_modified = obj["LastModified"]
|
|
# Convert last_modified to a timezone-aware datetime object
|
|
last_modified = (
|
|
last_modified.replace(tzinfo=timezone.utc)
|
|
.astimezone(tz=None)
|
|
.strftime("%Y-%m-%d %H:%M:%S %Z")
|
|
)
|
|
files.append(
|
|
f"{key} (Size: {size} bytes, Last Modified: {last_modified})"
|
|
)
|
|
|
|
# Check if there are more pages
|
|
if response.get("IsTruncated"):
|
|
continuation_token = response.get("NextContinuationToken")
|
|
else:
|
|
break # No more pages
|
|
|
|
# Format the message content with the list of files and metadata
|
|
message_content = (
|
|
"```\n" + "\n".join(files) + "\n```" if files else "No files found."
|
|
)
|
|
|
|
# Save the message to the database and emit to the chatroom
|
|
with app.app_context():
|
|
room = Room.query.filter_by(name=room_name).first()
|
|
if room:
|
|
new_message = Message(
|
|
username=username,
|
|
content=message_content,
|
|
room_id=room.id,
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
# Emit the message to the chatroom with the message ID
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": username,
|
|
"content": message_content,
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
|
|
def cancel_generation(room_name):
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
# Get the most recent message for the room that is being generated
|
|
latest_message = (
|
|
Message.query.filter_by(room_id=room.id)
|
|
.order_by(Message.id.desc())
|
|
.offset(1)
|
|
.first()
|
|
)
|
|
|
|
if latest_message:
|
|
# Set the cancellation request for the given message ID
|
|
cancellation_requests[latest_message.id] = True
|
|
# Optionally, inform the user that the generation has been canceled
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": f"Generation for message ID {latest_message.id} has been canceled.",
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
|
|
def get_activity_content(file_path):
|
|
"""
|
|
Load the activity content from either S3 or the local filesystem based on the configuration.
|
|
"""
|
|
if app.config["LOCAL_ACTIVITIES"]:
|
|
# Load the activity YAML from a local file with path traversal protection
|
|
import os.path
|
|
|
|
# Normalize the path and ensure it's within the research directory
|
|
normalized_path = os.path.normpath(file_path)
|
|
|
|
# Ensure path doesn't contain dangerous patterns
|
|
if ".." in normalized_path or normalized_path.startswith("/"):
|
|
raise ValueError(f"Invalid file path: {file_path}")
|
|
|
|
# Ensure file is within research directory and has .yaml extension
|
|
if not normalized_path.startswith("research/") or not normalized_path.endswith(
|
|
".yaml"
|
|
):
|
|
raise ValueError(
|
|
f"File must be in research/ directory and end with .yaml: {file_path}"
|
|
)
|
|
|
|
# Additional safety check - ensure resolved path is still in research dir
|
|
full_path = os.path.abspath(normalized_path)
|
|
research_dir = os.path.abspath("research/")
|
|
if not full_path.startswith(research_dir):
|
|
raise ValueError(f"Path traversal attempt detected: {file_path}")
|
|
|
|
with open(normalized_path, "r") as file:
|
|
activity_yaml = file.read()
|
|
else:
|
|
# Load the activity YAML from S3
|
|
s3_client = get_s3_client()
|
|
bucket_name = os.environ.get("S3_BUCKET_NAME")
|
|
response = s3_client.get_object(Bucket=bucket_name, Key=file_path)
|
|
activity_yaml = response["Body"].read().decode("utf-8")
|
|
|
|
return yaml.safe_load(activity_yaml)
|
|
|
|
|
|
def loop_through_steps_until_question(
|
|
activity_content, activity_state, room_name, username
|
|
):
|
|
room = get_room(room_name)
|
|
|
|
current_section_id = activity_state.section_id
|
|
current_step_id = activity_state.step_id
|
|
|
|
# Get the user's language preference from metadata
|
|
user_language = activity_state.dict_metadata.get("language", "English")
|
|
|
|
while True:
|
|
section = next(
|
|
(
|
|
s
|
|
for s in activity_content["sections"]
|
|
if s["section_id"] == current_section_id
|
|
),
|
|
None,
|
|
)
|
|
if not section:
|
|
break
|
|
|
|
step = next(
|
|
(s for s in section["steps"] if s["step_id"] == current_step_id), None
|
|
)
|
|
if not step:
|
|
break
|
|
|
|
# Emit the current step content blocks
|
|
if "content_blocks" in step:
|
|
content = "\n\n".join(step["content_blocks"])
|
|
translated_content = translate_text(content, user_language)
|
|
new_message = Message(
|
|
username="System", content=translated_content, room_id=room.id
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": "System",
|
|
"content": translated_content,
|
|
},
|
|
room=room_name,
|
|
)
|
|
socketio.sleep(0.1)
|
|
|
|
# Check if the current step has a question
|
|
if "question" in step:
|
|
question_content = step["question"]
|
|
translated_question_content = translate_text(
|
|
question_content, user_language
|
|
)
|
|
new_message = Message(
|
|
username="System (Question)",
|
|
content=translated_question_content,
|
|
room_id=room.id,
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": "System",
|
|
"content": translated_question_content,
|
|
},
|
|
room=room_name,
|
|
)
|
|
socketio.sleep(0.1)
|
|
break
|
|
|
|
# Move to the next step
|
|
next_section, next_step = get_next_step(
|
|
activity_content, current_section_id, current_step_id
|
|
)
|
|
|
|
if next_step:
|
|
activity_state.attempts = 0
|
|
activity_state.section_id = next_section["section_id"]
|
|
activity_state.step_id = next_step["step_id"]
|
|
|
|
db.session.add(activity_state)
|
|
db.session.commit()
|
|
|
|
current_section_id = next_section["section_id"]
|
|
current_step_id = next_step["step_id"]
|
|
else:
|
|
# Activity completed
|
|
|
|
# Display activity info before completing
|
|
display_activity_info(room_name, username)
|
|
|
|
db.session.delete(activity_state)
|
|
db.session.commit()
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": "Activity completed!",
|
|
},
|
|
room=room_name,
|
|
)
|
|
break
|
|
|
|
|
|
def start_activity(room_name, s3_file_path, username):
|
|
activity_content = get_activity_content(s3_file_path)
|
|
|
|
with app.app_context():
|
|
# Save the initial state to the database
|
|
room = get_room(room_name)
|
|
initial_section = activity_content["sections"][0]
|
|
initial_step = initial_section["steps"][0]
|
|
|
|
activity_state = ActivityState(
|
|
room_id=room.id,
|
|
section_id=initial_section["section_id"],
|
|
step_id=initial_step["step_id"],
|
|
max_attempts=activity_content.get("default_max_attempts_per_step", 3),
|
|
s3_file_path=s3_file_path, # Save the S3 file path
|
|
)
|
|
db.session.add(activity_state)
|
|
db.session.commit()
|
|
|
|
# Loop through steps until a question is found or the end is reached
|
|
loop_through_steps_until_question(
|
|
activity_content, activity_state, room_name, username
|
|
)
|
|
|
|
# Emit activity status update
|
|
socketio.emit(
|
|
"activity_status",
|
|
{
|
|
"active": True,
|
|
"activity_name": s3_file_path,
|
|
"section_id": initial_section["section_id"],
|
|
"step_id": initial_step["step_id"],
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
|
|
def cancel_activity(room_name, username):
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
activity_state = ActivityState.query.filter_by(room_id=room.id).first()
|
|
|
|
if not activity_state:
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": "No active activity found to cancel.",
|
|
},
|
|
room=room_name,
|
|
)
|
|
return
|
|
|
|
# Delete the activity state
|
|
db.session.delete(activity_state)
|
|
db.session.commit()
|
|
|
|
# Emit a message indicating the activity has been canceled
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": "Activity has been canceled.",
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
# Emit activity status update
|
|
socketio.emit("activity_status", {"active": False}, room=room_name)
|
|
|
|
|
|
def display_activity_metadata(room_name, username):
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
activity_state = ActivityState.query.filter_by(room_id=room.id).first()
|
|
|
|
if not activity_state:
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": "No active activity found.",
|
|
},
|
|
room=room_name,
|
|
)
|
|
return
|
|
|
|
# Pretty print the metadata
|
|
metadata_pretty = json.dumps(activity_state.dict_metadata, indent=2)
|
|
|
|
# Store and emit the metadata
|
|
metadata_message = f"```\n{metadata_pretty}\n```"
|
|
new_message = Message(
|
|
username="System", content=metadata_message, room_id=room.id
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": "System",
|
|
"content": metadata_message,
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
|
|
def execute_processing_script(metadata, script):
|
|
# Prepare the local environment for the script
|
|
local_env = {
|
|
"metadata": metadata,
|
|
"script_result": None,
|
|
}
|
|
|
|
# Execute the script
|
|
exec(script, {}, local_env)
|
|
|
|
# Return the result from the script
|
|
return local_env["script_result"]
|
|
|
|
|
|
def handle_activity_response(room_name, user_response, username):
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
activity_state = ActivityState.query.filter_by(room_id=room.id).first()
|
|
|
|
if not activity_state:
|
|
return
|
|
|
|
# Load the activity content
|
|
activity_content = get_activity_content(activity_state.s3_file_path)
|
|
|
|
try:
|
|
# Find the current section and step
|
|
section = next(
|
|
s
|
|
for s in activity_content["sections"]
|
|
if s["section_id"] == activity_state.section_id
|
|
)
|
|
step = next(
|
|
s for s in section["steps"] if s["step_id"] == activity_state.step_id
|
|
)
|
|
|
|
feedback_tokens_for_ai = step.get("feedback_tokens_for_ai", "")
|
|
|
|
# Check if the step has a question
|
|
if "question" in step:
|
|
# Execute pre-script if it exists (runs before categorization, with user_response available)
|
|
if "pre_script" in step:
|
|
print(f"DEBUG: Executing pre-script")
|
|
# Add user_response to a temporary copy of metadata for pre_script
|
|
temp_metadata = activity_state.dict_metadata.copy()
|
|
temp_metadata["user_response"] = user_response
|
|
pre_result = (
|
|
execute_processing_script(temp_metadata, step["pre_script"])
|
|
or {}
|
|
)
|
|
# Update metadata with pre-script results
|
|
for key, value in pre_result.get("metadata", {}).items():
|
|
activity_state.add_metadata(key, value)
|
|
print(f"DEBUG: Pre-script completed, updated metadata")
|
|
|
|
# Categorize the user's response
|
|
category = categorize_response(
|
|
step["question"],
|
|
user_response,
|
|
step["buckets"],
|
|
step.get("tokens_for_ai", ""),
|
|
)
|
|
|
|
# Initialize transition to None
|
|
transition = None
|
|
|
|
# Determine the transition based on the category
|
|
if category in step["transitions"]:
|
|
transition = step["transitions"][category]
|
|
elif category.isdigit() and int(category) in step["transitions"]:
|
|
transition = step["transitions"][int(category)]
|
|
else:
|
|
if category.lower() in ["yes", "true"]:
|
|
category = True
|
|
elif category.lower() in ["no", "false"]:
|
|
category = False
|
|
if category in step["transitions"]:
|
|
transition = step["transitions"][category]
|
|
|
|
# Emit an error message if no valid transition was found
|
|
if transition is None:
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": f"Error: Unrecognized category '{category}'. Please try again.",
|
|
},
|
|
room=room_name,
|
|
)
|
|
return
|
|
|
|
next_section_and_step = transition.get("next_section_and_step", None)
|
|
counts_as_attempt = transition.get("counts_as_attempt", True)
|
|
|
|
# Emit the category to the frontend
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": f"Category: {category}",
|
|
},
|
|
room=room_name,
|
|
)
|
|
socketio.sleep(0.1)
|
|
|
|
# Check metadata conditions for the current step
|
|
if "metadata_conditions" in transition:
|
|
conditions_met = all(
|
|
activity_state.dict_metadata.get(key) == value
|
|
for key, value in transition["metadata_conditions"].items()
|
|
)
|
|
if not conditions_met:
|
|
# Emit a message indicating the conditions are not met
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": "You do not have the required items to proceed.",
|
|
},
|
|
room=room_name,
|
|
)
|
|
# Remind the user of what they can do in the room
|
|
if "content_blocks" in step or "question" in step:
|
|
content_blocks = step.get("content_blocks", [])
|
|
question = step.get("question", "")
|
|
options_message = (
|
|
"\n\n".join(content_blocks) + "\n\n" + question
|
|
)
|
|
|
|
new_message = Message(
|
|
username="System",
|
|
content=options_message,
|
|
room_id=room.id,
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": "System",
|
|
"content": options_message,
|
|
},
|
|
room=room_name,
|
|
)
|
|
# exit early, the user may not pass ... yet.
|
|
return
|
|
|
|
# this gives the llm context on what changed.
|
|
new_metadata = {}
|
|
|
|
# Track temporary metadata keys that last for a single turn.
|
|
metadata_tmp_keys = []
|
|
|
|
# Update metadata based on user actions
|
|
if "metadata_add" in transition:
|
|
for key, value in transition["metadata_add"].items():
|
|
if value == "the-users-response":
|
|
value = user_response
|
|
elif value == "the-llms-response":
|
|
continue
|
|
elif isinstance(value, str):
|
|
if value.startswith("n+random(") and value.endswith(")"):
|
|
# Extract the range and apply the random increment
|
|
range_values = value[9:-1].split(",")
|
|
if len(range_values) == 2:
|
|
x, y = map(int, range_values)
|
|
value = activity_state.dict_metadata.get(
|
|
key, 0
|
|
) + random.randint(x, y)
|
|
elif value.startswith("n+") or value.startswith("n-"):
|
|
# Extract the numeric part c and apply the operation +/-
|
|
c = int(value[1:])
|
|
if value.startswith("n+"):
|
|
value = activity_state.dict_metadata.get(key, 0) + c
|
|
elif value.startswith("n-"):
|
|
value = activity_state.dict_metadata.get(key, 0) - c
|
|
new_metadata[key] = value
|
|
activity_state.add_metadata(key, value)
|
|
|
|
# Update metadata based on user actions
|
|
if "metadata_tmp_add" in transition:
|
|
for key, value in transition["metadata_tmp_add"].items():
|
|
if value == "the-users-response":
|
|
value = user_response
|
|
elif value == "the-llms-response":
|
|
continue
|
|
elif isinstance(value, str):
|
|
if value.startswith("n+random(") and value.endswith(")"):
|
|
# Extract the range and apply the random increment
|
|
range_values = value[9:-1].split(",")
|
|
if len(range_values) == 2:
|
|
x, y = map(int, range_values)
|
|
value = activity_state.dict_metadata.get(
|
|
key, 0
|
|
) + random.randint(x, y)
|
|
elif value.startswith("n+") or value.startswith("n-"):
|
|
# Extract the numeric part c and apply the operation +/-
|
|
c = int(value[1:])
|
|
if value.startswith("n+"):
|
|
value = activity_state.dict_metadata.get(key, 0) + c
|
|
elif value.startswith("n-"):
|
|
value = activity_state.dict_metadata.get(key, 0) - c
|
|
new_metadata[key] = value
|
|
metadata_tmp_keys.append(key)
|
|
activity_state.add_metadata(key, value)
|
|
|
|
# Update metadata by appending values to lists
|
|
if "metadata_append" in transition:
|
|
for key, value in transition["metadata_append"].items():
|
|
# Determine the value to append
|
|
if value == "the-users-response":
|
|
value_to_append = user_response
|
|
elif value == "the-llms-response":
|
|
continue # Handle this after feedback
|
|
else:
|
|
value_to_append = value
|
|
|
|
# Ensure the key exists and is a list
|
|
current_value = activity_state.dict_metadata.get(key, [])
|
|
if not isinstance(current_value, list):
|
|
current_value = [current_value]
|
|
|
|
# Append the value to the list
|
|
if isinstance(value_to_append, list):
|
|
current_value.extend(value_to_append)
|
|
else:
|
|
current_value.append(value_to_append)
|
|
|
|
# Update the metadata
|
|
activity_state.add_metadata(key, current_value)
|
|
|
|
# Update temporary metadata by appending values to lists
|
|
if "metadata_tmp_append" in transition:
|
|
for key, value in transition["metadata_tmp_append"].items():
|
|
# Determine the value to append
|
|
if value == "the-users-response":
|
|
value_to_append = user_response
|
|
elif value == "the-llms-response":
|
|
continue # Handle this after feedback
|
|
else:
|
|
value_to_append = value
|
|
|
|
# Ensure the key exists and is a list
|
|
current_value = activity_state.dict_metadata.get(key, [])
|
|
if not isinstance(current_value, list):
|
|
current_value = [current_value]
|
|
|
|
# Append the value to the list
|
|
if isinstance(value_to_append, list):
|
|
current_value.extend(value_to_append)
|
|
else:
|
|
current_value.append(value_to_append)
|
|
|
|
# Update the metadata
|
|
activity_state.add_metadata(key, current_value)
|
|
|
|
# Track temporary metadata keys
|
|
metadata_tmp_keys.append(key)
|
|
|
|
if "metadata_remove" in transition:
|
|
for key in transition["metadata_remove"]:
|
|
activity_state.remove_metadata(key)
|
|
|
|
# Handle metadata_random
|
|
if "metadata_random" in transition:
|
|
random_key = random.choice(
|
|
list(transition["metadata_random"].keys())
|
|
)
|
|
random_value = transition["metadata_random"][random_key]
|
|
new_metadata[random_key] = random_value
|
|
activity_state.add_metadata(random_key, random_value)
|
|
|
|
if "metadata_tmp_random" in transition:
|
|
random_key = random.choice(
|
|
list(transition["metadata_tmp_random"].keys())
|
|
)
|
|
random_value = transition["metadata_tmp_random"][random_key]
|
|
new_metadata[random_key] = random_value
|
|
metadata_tmp_keys.append(random_key)
|
|
activity_state.add_metadata(random_key, random_value)
|
|
|
|
# Execute the post-script if it exists (supports both old and new naming)
|
|
post_script = step.get("post_script") or step.get("processing_script")
|
|
if post_script and (
|
|
transition.get("run_post_script", False)
|
|
or transition.get("run_processing_script", False)
|
|
):
|
|
print(f"DEBUG: Executing post-script")
|
|
result = (
|
|
execute_processing_script(
|
|
activity_state.dict_metadata, post_script
|
|
)
|
|
or {}
|
|
)
|
|
|
|
plot_image_base64 = result.pop("plot_image", None)
|
|
|
|
# Add the result to the temporary metadata for use in AI feedback
|
|
metadata_tmp_keys.append("processing_script_result")
|
|
activity_state.add_metadata("processing_script_result", result)
|
|
|
|
# Update metadata with results from the processing script
|
|
for key, value in result.get("metadata", {}).items():
|
|
activity_state.add_metadata(key, value)
|
|
|
|
# Check if processing script wants to override the transition
|
|
if "next_section_and_step" in result:
|
|
next_section_and_step = result["next_section_and_step"]
|
|
print(
|
|
f"DEBUG: Processing script overriding transition to: {next_section_and_step}"
|
|
)
|
|
|
|
# Check if the result contains a plot image
|
|
if plot_image_base64:
|
|
plot_image_html = f'<img alt="Plot Image" src="data:image/png;base64,{plot_image_base64}">'
|
|
|
|
if result.get("set_background", False):
|
|
socketio.emit(
|
|
"set_background",
|
|
{"image_data": plot_image_base64},
|
|
room=room_name,
|
|
)
|
|
socketio.sleep(0.1)
|
|
else:
|
|
# Save the plot image to the database
|
|
new_message = Message(
|
|
username=username,
|
|
content=plot_image_html,
|
|
room_id=room.id,
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
# Emit the plot image to the frontend
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": username,
|
|
"content": plot_image_html,
|
|
},
|
|
room=room_name,
|
|
)
|
|
socketio.sleep(0.1)
|
|
|
|
if (
|
|
"metadata_clear" in transition
|
|
and transition["metadata_clear"] == True
|
|
):
|
|
activity_state.clear_metadata()
|
|
|
|
print(activity_state.dict_metadata)
|
|
|
|
# Commit the changes after the loop
|
|
db.session.add(activity_state)
|
|
db.session.commit()
|
|
|
|
user_language = activity_state.dict_metadata.get("language", "English")
|
|
|
|
# Emit the transition content blocks if they exist
|
|
if "content_blocks" in transition:
|
|
transition_content = "\n\n".join(transition["content_blocks"])
|
|
translated_transition_content = translate_text(
|
|
transition_content, user_language
|
|
)
|
|
new_message = Message(
|
|
username="System",
|
|
content=translated_transition_content,
|
|
room_id=room.id,
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": "System",
|
|
"content": translated_transition_content,
|
|
},
|
|
room=room_name,
|
|
)
|
|
socketio.sleep(0.1)
|
|
|
|
# if "correct" or max_attempts reached.
|
|
# Provide feedback based on the category
|
|
|
|
# Filter metadata for feedback if metadata_feedback_filter is specified
|
|
feedback_metadata = activity_state.dict_metadata
|
|
if "metadata_feedback_filter" in transition:
|
|
filter_keys = transition["metadata_feedback_filter"]
|
|
feedback_metadata = {
|
|
k: v
|
|
for k, v in activity_state.dict_metadata.items()
|
|
if k in filter_keys
|
|
}
|
|
|
|
feedback = provide_feedback(
|
|
transition,
|
|
category,
|
|
step["question"],
|
|
feedback_tokens_for_ai,
|
|
user_response,
|
|
user_language,
|
|
username,
|
|
json.dumps(feedback_metadata),
|
|
json.dumps(new_metadata),
|
|
)
|
|
|
|
# Store and emit the feedback
|
|
if feedback:
|
|
# feedback is metadata language aware, doesn't need to be translated.
|
|
new_message = Message(
|
|
username="System (Feedback)", content=feedback, room_id=room.id
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": "System (Feedback)",
|
|
"content": feedback,
|
|
},
|
|
room=room_name,
|
|
)
|
|
socketio.sleep(0.1)
|
|
|
|
# Add or append the LLM's response to the metadata
|
|
for key, value in transition.get("metadata_add", {}).items():
|
|
if value == "the-llms-response":
|
|
activity_state.add_metadata(key, feedback)
|
|
|
|
for key, value in transition.get("metadata_append", {}).items():
|
|
if value == "the-llms-response":
|
|
# Ensure the key exists and is a list
|
|
current_value = activity_state.dict_metadata.get(key, [])
|
|
if not isinstance(current_value, list):
|
|
current_value = [current_value]
|
|
|
|
# Append the feedback to the list
|
|
current_value.append(feedback)
|
|
activity_state.add_metadata(key, current_value)
|
|
|
|
if (
|
|
category
|
|
not in [
|
|
"partial_understanding",
|
|
"limited_effort",
|
|
"asking_clarifying_questions",
|
|
"set_language",
|
|
"off_topic",
|
|
]
|
|
or activity_state.attempts >= activity_state.max_attempts
|
|
or next_section_and_step # Processing script override takes precedence
|
|
):
|
|
if next_section_and_step:
|
|
(
|
|
current_section_id,
|
|
current_step_id,
|
|
) = next_section_and_step.split(":")
|
|
next_section = next(
|
|
s
|
|
for s in activity_content["sections"]
|
|
if s["section_id"] == current_section_id
|
|
)
|
|
next_step = next(
|
|
s
|
|
for s in next_section["steps"]
|
|
if s["step_id"] == current_step_id
|
|
)
|
|
else:
|
|
# Move to the next step or section
|
|
next_section, next_step = get_next_step(
|
|
activity_content, section["section_id"], step["step_id"]
|
|
)
|
|
|
|
if next_step:
|
|
activity_state.attempts = 0
|
|
activity_state.section_id = next_section["section_id"]
|
|
activity_state.step_id = next_step["step_id"]
|
|
|
|
db.session.add(activity_state)
|
|
db.session.commit()
|
|
|
|
# Loop through steps until a question is found or the end is reached
|
|
loop_through_steps_until_question(
|
|
activity_content, activity_state, room_name, username
|
|
)
|
|
else:
|
|
# the user response is any bucket other than correct.
|
|
if counts_as_attempt:
|
|
activity_state.attempts += 1
|
|
db.session.add(activity_state)
|
|
db.session.commit()
|
|
|
|
# Emit the question again
|
|
question_content = step["question"]
|
|
translated_question_content = translate_text(
|
|
question_content, user_language
|
|
)
|
|
new_message = Message(
|
|
username="System (Question)",
|
|
content=translated_question_content,
|
|
room_id=room.id,
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": "System",
|
|
"content": translated_question_content,
|
|
},
|
|
room=room_name,
|
|
)
|
|
socketio.sleep(0.1)
|
|
|
|
# Check if the activity state still exists before removing temporary metadata
|
|
try:
|
|
# Remove temporary metadata at the end of the turn
|
|
for key in metadata_tmp_keys:
|
|
activity_state.remove_metadata(key)
|
|
|
|
# Commit the changes after removing temporary metadata
|
|
db.session.add(activity_state)
|
|
db.session.commit()
|
|
|
|
except InvalidRequestError:
|
|
# Handle the case where the activity state was deleted
|
|
# print("Activity state was deleted before commit.")
|
|
db.session.rollback()
|
|
|
|
else:
|
|
# Handle steps without a question
|
|
loop_through_steps_until_question(
|
|
activity_content, activity_state, room_name, username
|
|
)
|
|
|
|
except Exception as e:
|
|
import traceback
|
|
|
|
msg = traceback.format_exc()
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": f"Error processing activity response: {e}\n\n{msg}",
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
|
|
def display_activity_info(room_name, username):
|
|
with app.app_context():
|
|
room = get_room(room_name)
|
|
activity_state = ActivityState.query.filter_by(room_id=room.id).first()
|
|
|
|
if not activity_state:
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": "No active activity found.",
|
|
},
|
|
room=room_name,
|
|
)
|
|
return
|
|
|
|
# Load the activity content
|
|
activity_content = get_activity_content(activity_state.s3_file_path)
|
|
|
|
try:
|
|
# Fetch the entire room history
|
|
all_messages = (
|
|
Message.query.filter_by(room_id=room.id)
|
|
.order_by(Message.id.asc())
|
|
.all()
|
|
)
|
|
chat_history = [
|
|
{
|
|
"role": "system" if msg.username in SYSTEM_USERS else "user",
|
|
"username": msg.username,
|
|
"content": msg.content,
|
|
}
|
|
for msg in all_messages
|
|
if not msg.is_base64_image()
|
|
]
|
|
|
|
# Prepare the rubric for grading
|
|
rubric = activity_content.get(
|
|
"tokens_for_ai_rubric",
|
|
"""
|
|
Grade the responses of all users based on the following criteria:
|
|
- Accuracy: How correct is the response?
|
|
- Completeness: Does the response fully address the question?
|
|
- Clarity: Is the response clear and easy to understand?
|
|
- Engagement: Is the response engaging and interesting?
|
|
Provide a score out of 10 for each criterion and an overall grade for each user.
|
|
Finally order each user by who is winning. Number of correct answers and accuracy & include an enumeration of the feats!
|
|
Take into account how many attempts the user took to get a passing answer when ranking.
|
|
Don't just try to give the user a "B" or 35/40, really figure out a good placement considering some people don't know how to type.
|
|
""",
|
|
)
|
|
|
|
# Generate the grading using the AI
|
|
grading_message = generate_grading(chat_history, rubric)
|
|
|
|
# Store and emit the activity info
|
|
info_message = f"Activity Info:\nCurrent Section: {activity_state.section_id}\nCurrent Step: {activity_state.step_id}\nAttempts: {activity_state.attempts}\n\n{grading_message}"
|
|
new_message = Message(
|
|
username="System", content=info_message, room_id=room.id
|
|
)
|
|
db.session.add(new_message)
|
|
db.session.commit()
|
|
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": new_message.id,
|
|
"username": "System",
|
|
"content": info_message,
|
|
},
|
|
room=room_name,
|
|
)
|
|
|
|
except Exception as e:
|
|
socketio.emit(
|
|
"chat_message",
|
|
{
|
|
"id": None,
|
|
"username": "System",
|
|
"content": f"Error displaying activity info: {e}",
|
|
},
|
|
room=room_name,
|
|
)
|
|
# Debugging: Log exception
|
|
print(f"Exception: {e}")
|
|
|
|
|
|
def generate_grading(chat_history, rubric):
|
|
openai_client, model_name = get_openai_client_and_model()
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": f"Using the following rubric, grade the responses in the chat history:\n\n{rubric}",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": f"Chat History:\n\n{json.dumps(chat_history, indent=2)}",
|
|
},
|
|
]
|
|
|
|
try:
|
|
completion = openai_client.chat.completions.create(
|
|
model=model_name,
|
|
messages=messages,
|
|
max_tokens=1000,
|
|
temperature=0.7,
|
|
n=1,
|
|
)
|
|
grading = completion.choices[0].message.content.strip()
|
|
return grading
|
|
except Exception as e:
|
|
return f"Error generating grading: {e}"
|
|
|
|
|
|
def get_next_step(activity_content, current_section_id, current_step_id):
|
|
for section in activity_content["sections"]:
|
|
if section["section_id"] == current_section_id:
|
|
for i, step in enumerate(section["steps"]):
|
|
if step["step_id"] == current_step_id:
|
|
if i + 1 < len(section["steps"]):
|
|
return section, section["steps"][i + 1]
|
|
else:
|
|
# Move to the next section
|
|
next_section_index = (
|
|
activity_content["sections"].index(section) + 1
|
|
)
|
|
if next_section_index < len(activity_content["sections"]):
|
|
next_section = activity_content["sections"][
|
|
next_section_index
|
|
]
|
|
return next_section, next_section["steps"][0]
|
|
return None, None
|
|
|
|
|
|
# Categorize the user's response.
|
|
def categorize_response(question, response, buckets, tokens_for_ai):
|
|
openai_client, model_name = get_openai_client_and_model()
|
|
bucket_list = ", ".join([str(bucket) for bucket in buckets])
|
|
# Check if tokens_for_ai already includes format instructions (ANALYSIS/BUCKET format)
|
|
if "ANALYSIS:" in tokens_for_ai and "BUCKET:" in tokens_for_ai:
|
|
# YAML already specifies output format, don't override
|
|
system_content = f"{tokens_for_ai}"
|
|
user_content = f"Question: {question}\nResponse: {response}"
|
|
else:
|
|
# Use old simple format for backwards compatibility
|
|
system_content = f"{tokens_for_ai} Categorize the following response into one of the following buckets: {bucket_list}. Return ONLY a bucket label."
|
|
user_content = f"Question: {question}\nResponse: {response}\n\nCategory:"
|
|
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": system_content,
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": user_content,
|
|
},
|
|
]
|
|
|
|
try:
|
|
completion = openai_client.chat.completions.create(
|
|
model=model_name,
|
|
messages=messages,
|
|
n=1,
|
|
max_tokens=150, # Increased for ANALYSIS + BUCKET format
|
|
temperature=0,
|
|
)
|
|
full_response = completion.choices[0].message.content.strip()
|
|
print(f"DEBUG BUCKET CATEGORIZATION: Full Hermes response: {full_response}")
|
|
|
|
# Handle both ANALYSIS/BUCKET format and simple bucket response
|
|
if "BUCKET:" in full_response:
|
|
# New ANALYSIS/BUCKET format
|
|
bucket_lines = [
|
|
line for line in full_response.split("\n") if "BUCKET:" in line
|
|
]
|
|
if bucket_lines:
|
|
category = (
|
|
bucket_lines[0]
|
|
.split("BUCKET:")[1]
|
|
.strip()
|
|
.lower()
|
|
.replace(" ", "_")
|
|
)
|
|
else:
|
|
category = full_response.lower().replace(" ", "_")
|
|
elif "ANALYSIS:" in full_response:
|
|
# Has analysis but no explicit BUCKET: line, try to extract from end
|
|
lines = [line.strip() for line in full_response.split("\n") if line.strip()]
|
|
if lines:
|
|
category = lines[-1].lower().replace(" ", "_")
|
|
else:
|
|
category = full_response.lower().replace(" ", "_")
|
|
else:
|
|
# Simple bucket response (old format)
|
|
category = full_response.lower().replace(" ", "_")
|
|
|
|
print(f"DEBUG BUCKET CATEGORIZATION: Extracted category: {category}")
|
|
return category
|
|
except Exception as e:
|
|
return f"Error: {e}"
|
|
|
|
|
|
# Generate AI feedback
|
|
def generate_ai_feedback(
|
|
category,
|
|
question,
|
|
user_response,
|
|
tokens_for_ai,
|
|
username,
|
|
json_metadata,
|
|
json_new_metadata,
|
|
):
|
|
openai_client, model_name = get_openai_client_and_model()
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": f"{tokens_for_ai} Generate a human-readable feedback message based on the following:",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": f"Username: {username}\nQuestion: {question}\nResponse: {user_response}\nCategory: {category}\nMetadata: {json_metadata}\n New Metadata: {json_new_metadata}",
|
|
},
|
|
]
|
|
|
|
try:
|
|
completion = openai_client.chat.completions.create(
|
|
model=model_name, messages=messages, max_tokens=1000, temperature=0.7, n=1
|
|
)
|
|
feedback = completion.choices[0].message.content.strip()
|
|
return feedback
|
|
except Exception as e:
|
|
return f"Error: {e}"
|
|
|
|
|
|
def provide_feedback(
|
|
transition,
|
|
category,
|
|
question,
|
|
tokens_for_ai,
|
|
user_response,
|
|
user_language,
|
|
username,
|
|
json_metadata,
|
|
json_new_metadata,
|
|
):
|
|
feedback = ""
|
|
if "ai_feedback" in transition:
|
|
tokens_for_ai += f" You must provide the feedback in the user's language: {user_language}. {transition['ai_feedback'].get('tokens_for_ai', '')}."
|
|
ai_feedback = generate_ai_feedback(
|
|
category,
|
|
question,
|
|
user_response,
|
|
tokens_for_ai,
|
|
username,
|
|
json_metadata,
|
|
json_new_metadata,
|
|
)
|
|
feedback += f"\n\n{ai_feedback}"
|
|
|
|
return feedback
|
|
|
|
|
|
def translate_text(text, target_language):
|
|
# Guard clause for default language
|
|
target_language = target_language.lower().split()
|
|
|
|
if "english" in target_language:
|
|
return text
|
|
|
|
openai_client, model_name = get_openai_client_and_model()
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": f"Translate the following text to {target_language}. DO NOT add anything else extra to your translation. It should be as close to word for word the dame but translated. Don't start with 'Set_language:' DO NOT try to solve math questions, translate the text around it and use mathmatical notation like normal.",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": text,
|
|
},
|
|
]
|
|
|
|
try:
|
|
completion = openai_client.chat.completions.create(
|
|
model=model_name, messages=messages, max_tokens=2000, temperature=0.7, n=1
|
|
)
|
|
translation = completion.choices[0].message.content.strip()
|
|
return translation
|
|
except Exception as e:
|
|
return f"Error: {e}"
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser(
|
|
description="Run the SocketIO application with optional configurations."
|
|
)
|
|
parser.add_argument("--profile", help="AWS profile name", default=None)
|
|
parser.add_argument(
|
|
"--local-activities",
|
|
action="store_true",
|
|
help="Use local activity files instead of S3",
|
|
)
|
|
parser.add_argument(
|
|
"--port",
|
|
type=int,
|
|
default=5001,
|
|
help="Port number to run the SocketIO server on (default: 5001)",
|
|
)
|
|
args = parser.parse_args()
|
|
# Set profile_name and other configurations as global attributes of the app object
|
|
app.config["PROFILE_NAME"] = args.profile
|
|
app.config["LOCAL_ACTIVITIES"] = args.local_activities
|
|
|
|
# Run the SocketIO server with the specified port
|
|
socketio.run(app, host="0.0.0.0", port=args.port, use_reloader=True)
|