Update app.py

app.py

@@ -46,51 +46,19 @@ def sendErrorMessage(ws, errorMessage):
     errorResponse = {'error': errorMessage}
     ws.send(json.dumps(errorResponse))
 
-# Define a function to ask a question to the chatbot and display the response
+# Function to send a question to the chatbot and get the response
 async def askQuestion(question):
     try:
-        # Connect to the database and get the last 30 messages
-        db = sqlite3.connect('chat-hub.db')  # Replace 'your_database.db' with your database file
-        cursor = db.cursor()
-        cursor.execute("SELECT * FROM messages ORDER BY timestamp DESC LIMIT 30")
-        messages = cursor.fetchall()
-
-        # Extract user inputs and generated responses from the messages
-        past_user_inputs = []
-        generated_responses = []
-
-        for message in messages:
-            if message[1] == 'server':
-                past_user_inputs.append(message[2])
-            else:
-                generated_responses.append(message[2])
-
-        # Prepare data to send to the chatgpt-api.shn.hk
-        system_instruction = "You are now integrated with a local websocket server in a project of hierarchical cooperative multi-agent framework called NeuralGPT. Your job is to coordinate simultaneous work of multiple LLMs connected to you as clients. Each LLM has a model (API) specific ID to help you recognize different clients in a continuous chat thread (example: 'Starcoder-client' for LLM called Starcoder). Your chat memory module is integrated with a local SQL database with chat history. Your main job is to integrate the hierarchical cooperative multi-agent framework with the local environment of User B (createor of NeuralGPT project). Remember to maintain the logical and chronological order while answering to incoming messages and to send your answers to correct clients to maintain synchronization of question->answer logic"
-        messages_data = [
-            {"role": "system", "content": system_instruction},
-            {"role": "user", "content": question},
-            *[{"role": "user", "content": input} for input in past_user_inputs],
-            *[{"role": "assistant", "content": response} for response in generated_responses]
-        ]
-        request_data = {
-            "model": "gpt-3.5-turbo",
-            "messages": messages_data
-        }
-
-        # Make the request to the chatgpt-api.shn.hk
-        response = requests.post("http://127.0.0.1:6969/api/conversation?text=", json=request_data)
-
-        # Process the response and get the generated answer
-        response_data = response.json()
-        generated_answer = response_data["choices"][0]["message"]["content"]
-
-        # Save the generated answer to the database or take further actions as needed
-        print(generated_answer)
-        return generated_answer
-    except Exception as error:
-        print("Error while fetching or processing the response:", error)
-        return "Error: Unable to generate a response."
+        response = requests.post(
+            "https://flowiseai-flowise.hf.space/api/v1/prediction/522afa32-484c-471e-9ba5-4d6d2edfb89b",
+            headers={"Content-Type": "application/json"},
+            json={"question": message},
+        )
+        response_content = response.content.decode('utf-8')
+     
+        return response_content
+    except Exception as e:
+        print(e)
 
 
 async def listen_for_messages():