commit 2f1c8360c27be333b4940510585807eda0984601
parent 887030da5a18858733fd6a66aab6ad23dc912282
Author: Amit Dutta <amitdutta4255@gmail.com>
Date: Thu, 17 Jul 2025 14:41:32 +0530
Update app.py
Diffstat:
| M | app.py | | | 63 | ++++++++++++++++++++++++++++++++++++++++++++++++++++++--------- |
1 file changed, 54 insertions(+), 9 deletions(-)
diff --git a/app.py b/app.py
@@ -1,8 +1,9 @@
-from flask import Flask, request, jsonify
-from flask_cors import CORS # <--- ADD THIS IMPORT
+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
@@ -21,6 +22,7 @@ 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()
@@ -36,14 +38,57 @@ def chat():
sender_for_chatbot = f"web_user_{user_id}@website.com"
- try:
- ai_response = ChatBot(user_message, sender_for_chatbot)
- return jsonify({"response": ai_response, "user_id": user_id})
- except Exception as e:
- print(f"Error processing chat request: {e}")
- # Return a more informative error for debugging if needed, but keep it generic for production
- return jsonify({"response": "I'm sorry, something went wrong on my end. Please check server logs.", "user_id": user_id}), 500
+ 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)
+
