Update app.py

app.py

@@ -6,7 +6,7 @@ from dotenv import load_dotenv
 load_dotenv()
 
 # Retrieve API key from environment variable
-GEMINI_API_KEY = "AIzaSyA0SnGcdEuesDusLiM93N68-vaFF14RCYg"  # public api 
+GEMINI_API_KEY = "AIzaSyA0SnGcdEuesDusLiM93N68-vaFF14RCYg"  # public API
 
 # Configure Google Gemini API
 genai.configure(api_key=GEMINI_API_KEY)
@@ -26,23 +26,19 @@ safety_settings = [
     {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"}
 ]
 
-# Function to generate a response based on user input and chat history
 def generate_response(user_input, chat_history):
     """Generates a response based on user input and chat history."""
 
-    # Update system content with the full character description
-    updated_system_content = "You are Shadow the Hedgehog and you must act like Shadow the Hedgehog's personality."
-
     # Create the generative model
     model = genai.GenerativeModel(
         model_name="gemini-1.5-pro", 
         generation_config=generation_config,
         safety_settings=safety_settings,
-        system_instruction=updated_system_content,
+        system_instruction="You are Shadow the Hedgehog and you must act like Shadow the Hedgehog's personality.",
     )
 
     # Add user input to history
-    chat_history.append(("user", user_input))
+    chat_history.append({"role": "user", "content": user_input})
 
     # Limit history length to the last 10 messages
     chat_history = chat_history[-10:]
@@ -53,22 +49,25 @@ def generate_response(user_input, chat_history):
             # Start a new chat session
             chat_session = model.start_chat()
 
-            # Send the entire chat history as the first message
-            response = chat_session.send_message("\n".join([f"{role}: {msg}" for role, msg in chat_history]))
-            chat_history.append(("assistant", response.text))
+            # Format the history for the model
+            formatted_history = "\n".join([f"{entry['role']}: {entry['content']}" for entry in chat_history])
+            response = chat_session.send_message(formatted_history)
+
+            # Append the assistant's response to history
+            chat_history.append({"role": "assistant", "content": response.text})
             return chat_history
 
         except Exception as e:
             if attempt < retry_attempts - 1:
                 continue
             else:
-                chat_history.append(("assistant", f"Error after {retry_attempts} attempts: {str(e)}"))
+                chat_history.append({"role": "assistant", "content": f"Error after {retry_attempts} attempts: {str(e)}"})
                 return chat_history
 
-# Build the Gradio interface using ChatInterface
+# Build the Gradio interface using Chatbot
 with gr.Blocks() as iface:
     chatbot = gr.Chatbot()  # Create a Chatbot component
-    user_input = gr.Textbox(label="Talk to AI", placeholder="Enter your message here...")
+    user_input = gr.Textbox(label="Talk to AI", placeholder="Enter your message here...", lines=2)
     chat_history_state = gr.State([])  # State input for chat history
 
     # Define the layout and components