/activity metadata
modified: README.rst modified: app.py modified: research/activity16.yaml
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parent
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3 changed files with 55 additions and 36 deletions
51
app.py
51
app.py
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@ -322,13 +322,17 @@ def handle_message(data):
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commands = data["message"].splitlines()
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for command in commands:
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if command.startswith("/activity cancel"):
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gevent.spawn(cancel_activity, room_name, data["username"])
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# Exit early since we're canceling the activity
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return
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if command.startswith("/activity info"):
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gevent.spawn(display_activity_info, room_name, data["username"])
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# Exit early since we're displaying activity info
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return
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if command.startswith("/activity cancel"):
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gevent.spawn(cancel_activity, room_name, data["username"])
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# Exit early since we're canceling the activity
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if command.startswith("/activity metadata"):
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gevent.spawn(display_activity_metadata, room_name, data["username"])
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# Exit early since we're displaying activity metadata
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return
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if command.startswith("/activity"):
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s3_file_path = command.split(" ", 1)[1].strip()
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@ -1868,6 +1872,45 @@ def cancel_activity(room_name, username):
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)
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def display_activity_metadata(room_name, username):
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with app.app_context():
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room = get_room(room_name)
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activity_state = ActivityState.query.filter_by(room_id=room.id).first()
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if not activity_state:
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socketio.emit(
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"message",
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{
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"id": None,
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"username": "System",
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"content": "No active activity found.",
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},
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room=room_name,
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)
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return
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# Pretty print the metadata
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metadata_pretty = json.dumps(activity_state.dict_metadata, indent=2)
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# Store and emit the metadata
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metadata_message = f"```\n{metadata_pretty}\n```"
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new_message = Message(
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username="System", content=metadata_message, room_id=room.id
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)
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db.session.add(new_message)
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db.session.commit()
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socketio.emit(
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"message",
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{
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"id": new_message.id,
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"username": "System",
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"content": metadata_message,
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},
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room=room_name,
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)
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def handle_activity_response(room_name, user_response, username):
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with app.app_context():
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room = get_room(room_name)
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@ -2292,7 +2335,7 @@ def generate_ai_feedback(category, question, user_response, tokens_for_ai):
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try:
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print(messages)
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completion = openai_client.chat.completions.create(
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model=model_name, messages=messages, max_tokens=2000, temperature=0.7
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model=model_name, messages=messages, max_tokens=1000, temperature=0.7
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)
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feedback = completion.choices[0].message.content.strip()
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return feedback
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