commit 8bfacedad5bcb701945133cdf088ec61d7f9132c
parent 2c2cdf158626ebcd05afb076ec4cafc5f8c41298
Author: AMIT DUTTA <amitdutta4255@gmail.com>
Date: Sat, 17 Jan 2026 21:11:16 +0530
Delete app.py
Diffstat:
| D | app.py | | | 94 | ------------------------------------------------------------------------------- |
1 file changed, 0 insertions(+), 94 deletions(-)
diff --git a/app.py b/app.py
@@ -1,94 +0,0 @@
-from flask import Flask, request, jsonify, Response # Import Response
-from flask_cors import CORS
-from Backend.Chatbot import ChatBot
-import uuid
-import os
-import json # Import json for encoding error messages
-
-app = Flask(__name__)
-CORS(app) # <--- ADD THIS LINE: Enables CORS for all routes
-
-# --- Ensure Data directory exists (same as in Chatbot.py) ---
-if not os.path.exists("Data"):
- os.makedirs("Data")
-
-@app.route('/')
-def home():
- """Basic route to confirm the server is running."""
- return "Your AI Chatbot API is running! Access the /chat endpoint via POST."
-
-@app.route('/chat', methods=['POST'])
-def chat():
- """
- API endpoint for handling chat messages.
- Expects a JSON payload with 'message' and optionally 'user_id'.
- Now streams the AI response.
- """
- data = request.get_json()
-
- user_message = data.get('message')
- user_id = data.get('user_id') # Get user_id from the frontend
-
- if not user_message:
- return jsonify({"response": "Error: 'message' field is required.", "user_id": user_id}), 400
-
- if not user_id:
- user_id = str(uuid.uuid4())
- print(f"New guest session initiated: {user_id}")
-
- sender_for_chatbot = f"web_user_{user_id}@website.com"
-
- def generate_response_stream():
- """Generator function to stream response chunks."""
- full_response_content = "" # To capture the full response for saving to chat log
-
- try:
- # Call the ChatBot function which now yields chunks
- for chunk in ChatBot(user_message, sender_for_chatbot):
- # Check if this is an image response (non-streaming text, but still yielded)
- if chunk.startswith("[IMAGE_BASE64]:"):
- # If it's an image, we send it as a single JSON response
- # The frontend will detect this Content-Type
- # We need to encode the JSON string to bytes
- yield json.dumps({"response": chunk, "user_id": user_id}).encode('utf-8')
- return # Stop streaming and return
-
- # If it's not an image, it's a text chunk, so stream it
- full_response_content += chunk # Accumulate for saving
- yield chunk.encode('utf-8') # Encode chunks to bytes for streaming
-
- # After streaming is complete, save the full response to ChatLog.json
- # This part will only execute if the ChatBot yielded text chunks.
- # For image/search/youtube, ChatBot returns early.
- if full_response_content:
- # Load existing chat log
- try:
- messages = json.load(open("Data/ChatLog.json"))
- except (FileNotFoundError, json.JSONDecodeError):
- messages = []
-
- # Append the full AI response
- messages.append({"role": "assistant", "content": full_response_content})
-
- # Save updated chat log
- with open("Data/ChatLog.json", "w") as f:
- json.dump(messages, f, indent=4)
- print("Full LLM response saved to ChatLog.json")
-
- except Exception as e:
- print(f"Error processing chat request during streaming: {e}")
- # Send error message as a JSON object if an error occurs during streaming setup
- # This will be the only thing sent if the error happens early.
- yield json.dumps({"response": f"Error: {str(e)}", "user_id": user_id}).encode('utf-8')
-
- # Return a streaming response.
- # For text streaming, 'text/plain' is appropriate.
- # For image responses, the generator will yield a JSON string, and the frontend
- # will need to parse it. Let's keep text/plain for the stream
- # and let the frontend parse the first chunk if it's JSON.
- return Response(generate_response_stream(), mimetype='text/plain')
-
-if __name__ == '__main__':
- port = int(os.environ.get('PORT', 5000))
- app.run(debug=True, host='0.0.0.0', port=port)
-
