")
def chat(room_name):
# Query all rooms so that newest is first.
rooms = Room.query.order_by(Room.id.desc()).all()
# Get username from query parameters
username = request.args.get("username", "guest")
# Pass username and rooms into the template
return render_template(
"chat.html", room_name=room_name, rooms=rooms, username=username
)
@app.route("/download_chat_history", methods=["GET"])
def download_chat_history():
room_name = request.args.get("room_name")
room = get_room(room_name)
if not room:
return jsonify({"error": "Room not found"}), 404
messages = Message.query.filter_by(room_id=room.id).all()
if not messages:
return jsonify({"error": "No messages found"}), 404
chat_history = [
{
"role": "system" if message.username in system_users else "user",
"content": message.content,
}
for message in messages
if not message.is_base64_image()
]
if not chat_history:
return jsonify({"error": "No valid messages found"}), 404
response = Response(
response=json.dumps(chat_history, indent=2),
status=200,
mimetype="application/json",
)
response.headers["Content-Disposition"] = f"attachment; filename={room.name}.json"
return response
@app.route("/download_chat_history_md", methods=["GET"])
def download_chat_history_md():
room_name = request.args.get("room_name")
room = get_room(room_name)
if not room:
return jsonify({"error": "Room not found"}), 404
messages = Message.query.filter_by(room_id=room.id).all()
if not messages:
return jsonify({"error": "No messages found"}), 404
# Access system users from the existing context
chat_history_md = []
toc = []
for index, message in enumerate(messages):
if not message.is_base64_image(): # Correctly call the method
role = "System" if message.username in system_users else "User"
header = f"### {role}: {message.username} (Turn {index + 1})"
toc.append(
f"- [{role}: {message.username} (Turn {index + 1})](#{role.lower()}-{message.username.lower().replace(' ', '-')}-turn-{index + 1})"
)
chat_history_md.append(f"{header}\n\n{message.content}\n\n---\n")
if not chat_history_md:
return jsonify({"error": "No valid messages found"}), 404
markdown_content = (
f"# Chat History for {room.name}\n\n## Table of Contents\n"
+ "\n".join(toc)
+ "\n\n"
+ "\n".join(chat_history_md)
)
response = Response(response=markdown_content, status=200, mimetype="text/markdown")
response.headers["Content-Disposition"] = f'attachment; filename="{room.name}.md"'
return response
@app.route("/search")
def search_page():
# Query all rooms so that newest is first.
rooms = Room.query.order_by(Room.id.desc()).all()
keywords = request.args.get("keywords", "")
username = request.args.get("username", "guest")
if not keywords:
return render_template(
"search.html",
rooms=rooms,
keywords=keywords,
results=[],
username=username,
error="Keywords are required",
)
# Call the function to search messages
search_results = search_messages(keywords)
return render_template(
"search.html",
rooms=rooms,
keywords=keywords,
results=search_results,
username=username,
error=None,
)
def search_messages(keywords):
search_results = {}
# Split the keywords by spaces
keyword_list = keywords.lower().split()
# Search for messages containing any of the keywords
messages = Message.query.filter(
db.or_(*[Message.content.ilike(f"%{keyword}%") for keyword in keyword_list])
).all()
for message in messages:
room = Room.query.get(message.room_id)
if room:
# Calculate the score based on the number of occurrences of all keywords
score = sum(
message.content.lower().count(keyword) for keyword in keyword_list
)
if room.id not in search_results:
search_results[room.id] = {
"room_id": room.id,
"room_name": room.name,
"room_title": room.title,
"score": 0,
}
search_results[room.id]["score"] += score
# Convert the dictionary to a list and sort results by score in descending order
search_results_list = list(search_results.values())
search_results_list.sort(key=lambda x: x["score"], reverse=True)
return search_results_list
@socketio.on("join")
def on_join(data):
room_name = data["room_name"]
room = get_room(room_name)
# this makes the client start listening for new events for this room.
join_room(room_name)
# update the title bar with the proper room title, if it exists for just this new client.
if room.title:
socketio.emit("update_room_title", {"title": room.title}, room=request.sid)
# Fetch previous messages from the database
previous_messages = Message.query.filter_by(room_id=room.id).all()
# count the number of tokens in this room.
total_token_count = 0
# Send the history of messages only to the newly connected client.
# The reason for using `request.sid` here is to target the specific session (or client) that
# just connected, so only they receive the backlog of messages, rather than broadcasting
# this information to all clients in the room.
for message in previous_messages:
if not message.is_base64_image():
total_token_count += message.token_count
emit(
"previous_messages",
{
"id": message.id,
"username": message.username,
"content": message.content,
},
room=request.sid,
)
message_count = len(previous_messages)
if room.title is None and message_count >= 6:
room.title = gpt_generate_room_title(previous_messages)
db.session.add(room)
db.session.commit()
socketio.emit("update_room_title", {"title": room.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)
# Broadcast to all clients in the room that a new user has joined.
# Here, `room=room` ensures the message is sent to everyone in that specific room.
emit(
"chat_message",
{"id": None, "content": f"{data['username']} has joined the room."},
room=room.name,
)
emit(
"user_joined",
{"username": data["username"]},
room=room.name,
)
emit(
"chat_message",
{
"id": None,
"content": f"Estimated {total_token_count} total tokens in conversation.",
},
room=request.sid,
)
@socketio.on("chat_message")
def handle_message(data):
room_name = data["room_name"]
room = get_room(room_name)
# Save the message to the database
new_message = Message(
username=data["username"],
content=data["message"],
room_id=room.id,
)
db.session.add(new_message)
db.session.commit()
emit(
"chat_message",
{
"id": new_message.id,
"username": data["username"],
"content": data["message"],
},
room=room.name,
)
# detect and process special commands.
commands = data["message"].splitlines()
for command in commands:
if command.startswith("/help"):
# Emit the help message
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, data["username"])
# Exit early since we're canceling the activity
return
if command.startswith("/activity info"):
gevent.spawn(display_activity_info, room_name, data["username"])
# Exit early since we're displaying activity info
return
if command.startswith("/activity metadata"):
gevent.spawn(display_activity_metadata, room_name, data["username"])
# Exit early since we're displaying activity metadata
return
if command.startswith("/activity"):
s3_file_path = command.split(" ", 1)[1].strip()
gevent.spawn(start_activity, room_name, s3_file_path, data["username"])
# Exit early since we're starting an activity
return
if command.startswith("/s3 ls"):
# Extract the S3 file path pattern
s3_file_path_pattern = command.split(" ", 2)[2].strip()
# List files from S3 and emit their names
gevent.spawn(
list_s3_files, room.name, s3_file_path_pattern, data["username"]
)
if command.startswith("/s3 load"):
# Extract the S3 file path
s3_file_path = command.split(" ", 2)[2].strip()
# Load the file from S3 and emit its content
gevent.spawn(load_s3_file, room_name, s3_file_path, data["username"])
if command.startswith("/s3 save"):
# Extract the S3 key path
s3_key_path = command.split(" ", 2)[2].strip()
# Save the most recent code block to S3
gevent.spawn(
save_code_block_to_s3, room_name, s3_key_path, data["username"]
)
if command.startswith("/title new"):
gevent.spawn(generate_new_title, room_name, data["username"])
if command.startswith("/cancel"):
# Cancel the most recent generation request
gevent.spawn(cancel_generation, room_name)
# Check if the user is in activity mode
activity_state = ActivityState.query.filter_by(room_id=room.id).first()
if activity_state:
gevent.spawn(
handle_activity_response, room_name, data["message"], data["username"]
)
if "dall-e-3" in data["message"]:
# Use the entire message as the prompt for DALL-E 3
# Generate the image and emit its URL
gevent.spawn(
generate_dalle_image, data["room_name"], data["message"], data["username"]
)
if (
"claude-" in data["message"]
or "gpt-" in data["message"]
or "mistral-" in data["message"]
or "together/" in data["message"]
or "localhost/" in data["message"]
or "vllm/" in data["message"]
or "ollama/" in data["message"]
or "groq/" in data["message"]
or "grok-beta" in data["message"]
or "gemini-" in data["message"]
):
# Emit a temporary message indicating that the llm is processing
emit(
"chat_message",
{"id": None, "content": "Processing..."},
room=room.name,
)
if "claude-haiku" in data["message"]:
gevent.spawn(
chat_claude,
data["username"],
room.name,
model_name="anthropic.claude-3-haiku-20240307-v1:0",
)
if "claude-sonnet" in data["message"]:
gevent.spawn(chat_claude, data["username"], room.name)
if "claude-opus" in data["message"]:
gevent.spawn(
chat_claude,
data["username"],
room.name,
model_name="anthropic.claude-3-opus-20240229-v1:0",
)
if "gpt-3" in data["message"]:
gevent.spawn(chat_gpt, data["username"], room.name)
if "gpt-4o-2024-08-06" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
model_name="gpt-4o-2024-08-06",
)
elif "gpt-4" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
# model_name="gpt-4o",
model_name="gpt-4o-2024-08-06",
)
if "gpt-o1-mini" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
model_name="o1-mini",
)
if "gpt-mini" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
model_name="gpt-4o-mini",
)
if "grok-beta" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
model_name="grok-beta",
)
if "gemini-flash" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
model_name="gemini-1.5-flash-002",
)
if "gemini-flash-8b" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
model_name="gemini-1.5-flash-8b",
)
if "gemini-pro" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
model_name="gemini-1.5-pro-002",
)
if "mistral-tiny" in data["message"]:
gevent.spawn(
chat_mistral,
data["username"],
room.name,
model_name="mistral-tiny",
)
if "mistral-small" in data["message"]:
gevent.spawn(
chat_mistral,
data["username"],
room.name,
model_name="mistral-small",
)
if "mistral-medium" in data["message"]:
gevent.spawn(
chat_mistral,
data["username"],
room.name,
model_name="mistral-medium",
)
if "mistral-nemo" in data["message"]:
gevent.spawn(
chat_mistral,
data["username"],
room.name,
model_name="open-mistral-nemo",
)
if "mistral-large" in data["message"]:
gevent.spawn(
chat_mistral,
data["username"],
room.name,
model_name="mistral-large-latest",
)
if "together/openchat" in data["message"]:
gevent.spawn(
chat_together,
data["username"],
room.name,
model_name="openchat/openchat-3.5-1210",
stop=["<|end_of_turn|>", ""],
)
if "together/mixtral" in data["message"]:
gevent.spawn(
chat_together,
data["username"],
room.name,
model_name="mistralai/Mixtral-8x7B-v0.1",
)
if "together/mistral" in data["message"]:
gevent.spawn(
chat_together,
data["username"],
room.name,
model_name="mistralai/Mistral-7B-Instruct-v0.1",
)
if "together/solar" in data["message"]:
gevent.spawn(
chat_together,
data["username"],
room.name,
model_name="upstage/SOLAR-10.7B-Instruct-v1.0",
stop=["###", ""],
)
if "groq/mixtral" in data["message"]:
gevent.spawn(
chat_groq,
data["username"],
room.name,
model_name="mixtral-8x7b-32768",
)
if "groq/llama2" in data["message"]:
gevent.spawn(
chat_groq,
data["username"],
room.name,
model_name="llama2-70b-4096",
)
if "groq/llama3" in data["message"]:
gevent.spawn(
chat_groq,
data["username"],
room.name,
model_name="llama3-70b-8192",
)
if "groq/gemma" in data["message"]:
gevent.spawn(
chat_groq,
data["username"],
room.name,
model_name="gemma-7b-it",
)
if "vllm/openchat" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
model_name="openchat/openchat-3.5-0106",
)
if "vllm/hermes" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
model_name="NousResearch/Hermes-3-Llama-3.1-8B",
)
if "ollama/hermes" in data["message"]:
gevent.spawn(
chat_gpt,
data["username"],
room.name,
model_name="hf.co/NousResearch/Hermes-3-Llama-3.1-8B-GGUF:Q8_0",
)
if "localhost/mistral" in data["message"]:
gevent.spawn(
chat_llama,
data["username"],
room.name,
model_name="mistral-7b-instruct-v0.2.Q3_K_L.gguf",
)
if "localhost/mistral-code" in data["message"]:
gevent.spawn(
chat_llama,
data["username"],
room.name,
model_name="mistral-7b-instruct-v0.2-code-ft.Q3_K_L.gguf",
)
if "localhost/openhermes" in data["message"]:
gevent.spawn(
chat_llama,
data["username"],
room.name,
model_name="openhermes-2.5-mistral-7b.Q6_K.gguf",
)
@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,
)
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 get_openai_client_and_model(model_name="NousResearch/Hermes-3-Llama-3.1-8B"):
vllm_endpoint = os.environ.get("VLLM_ENDPOINT")
vllm_api_key = os.environ.get("VLLM_API_KEY", "not-needed")
xai_api_key = os.environ.get("XAI_API_KEY")
google_api_key = os.environ.get("GOOGLE_API_KEY")
is_openai_model = "gpt" in model_name.lower() or "o1" in model_name.lower()
is_xai_model = "grok-" in model_name.lower()
is_google_model = "gemini-" in model_name.lower()
is_ollama_model = "hf.co" in model_name.lower()
is_vllm_model = True
if is_openai_model or is_xai_model or is_google_model or is_ollama_model:
is_vllm_model = False
if is_vllm_model:
openai_client = OpenAI(base_url=vllm_endpoint, api_key=vllm_api_key)
elif is_ollama_model:
openai_client = OpenAI(base_url="http://127.0.0.1:11434/v1", api_key=vllm_api_key)
elif is_xai_model:
openai_client = OpenAI(base_url="https://api.x.ai/v1", api_key=xai_api_key)
elif is_google_model:
openai_client = OpenAI(
base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
api_key=google_api_key,
)
else:
openai_client = OpenAI()
return openai_client, model_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
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:
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_mistral(username, room_name, model_name="mistral-tiny"):
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(50)
.all()
)
chat_history = []
combined_content = ""
last_role = None
# Iterate over messages to combine consecutive assistant messages
for msg in reversed(last_messages):
if msg.is_base64_image():
continue
current_role = "assistant" if msg.username in system_users else "user"
formatted_content = f"{msg.username}: {msg.content}"
chat_history.append({"role": current_role, "content": formatted_content})
# Initialize the Mistral client
mistral_client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
buffer = "" # Content buffer for accumulating the chunks
# Save an empty message to get an ID for the chunks
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:
# Use the Mistral client to stream the chat completion
for chunk in mistral_client.chat.stream(
model=model_name, messages=chat_history
):
content_chunk = chunk.data.choices[0].delta.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:
buffer += content_chunk # Accumulate content
if first_chunk:
socketio.emit(
"message_chunk",
{
"id": msg_id,
"content": f"**{username} ({model_name}):**\n\n{content_chunk}",
},
room=room.name,
)
first_chunk = False
else:
socketio.emit(
"message_chunk",
{"id": msg_id, "content": content_chunk},
room=room.name,
)
socketio.sleep(0) # Force immediate handling
except Exception as e:
with app.app_context():
message_content = f"Mistral 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,
)
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_together(
username,
room_name,
message,
model_name="mistralai/Mixtral-8x7B-Instruct-v0.1",
stop=["[/INST]", ""],
):
together.api_key = os.environ["TOGETHER_API_KEY"]
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(15)
.all()
)
chat_history = [
f"{msg.username}: {msg.content}"
for msg in reversed(last_messages)
if not msg.is_base64_image()
]
if "mistralai" in model_name:
chat_history_str = "\n\n".join(chat_history)
elif "solar" in model_name:
chat_history_str = "### \n\n".join(chat_history)
chat_history_str += "### Assistant:"
else:
chat_history_str = "<|end_of_turn|>\n\n".join(chat_history)
chat_history_str += "<|end_of_turn|>Math Correct Assistant:"
buffer = "" # Content buffer for accumulating the chunks
# Save an empty message to get an ID for the chunks
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:
# Use the Together client to stream the chat completion
prompt = f"{chat_history_str}"
if "mistralai" in model_name:
prompt = f"[INST] {chat_history_str} [/INST]"
if "solar" in model_name:
prompt = f"{revised_prompt}
' 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 open(file_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 = f"Question: {step['question']}" translated_question_content = translate_text( question_content, user_language ) new_message = Message( username="System", 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 ) 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, ) 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: # 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 processing script if it exists if "processing_script" in step and transition.get( "run_processing_script", False ): result = execute_processing_script( activity_state.dict_metadata, step["processing_script"] ) 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 the result contains a plot image if plot_image_base64: plot_image_html = f'
'
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
feedback = provide_feedback(
transition,
category,
step["question"],
feedback_tokens_for_ai,
user_response,
user_language,
username,
activity_state.json_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", content=feedback, room_id=room.id
)
db.session.add(new_message)
db.session.commit()
socketio.emit(
"chat_message",
{
"id": new_message.id,
"username": "System",
"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
):
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 = f"Question: {step['question']}"
translated_question_content = translate_text(
question_content, user_language
)
new_message = Message(
username="System",
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])
messages = [
{
"role": "system",
"content": f"{tokens_for_ai} Categorize the following response into one of the following buckets: {bucket_list}. Return ONLY a bucket label.",
},
{
"role": "user",
"content": f"Question: {question}\nResponse: {response}\n\nCategory:",
},
]
try:
completion = openai_client.chat.completions.create(
model=model_name,
messages=messages,
n=1,
max_tokens=10,
temperature=0,
)
category = (
completion.choices[0].message.content.strip().lower().replace(" ", "_")
)
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\nAI Feedback: {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()
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",
)
args = parser.parse_args()
# Set profile_name as a global attribute of the app object
app.config["PROFILE_NAME"] = args.profile
app.config["LOCAL_ACTIVITIES"] = args.local_activities
socketio.run(app, host="0.0.0.0", port=5001, use_reloader=True)