Update app.py

app.py

@@ -1,12 +1,14 @@
+import os
 import gradio as gr
 import google.generativeai as genai
 from dotenv import load_dotenv
+import time
 
 # Load environment variables from .env file
 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)
@@ -26,66 +28,57 @@ 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."""
-    
-    # Add user input to history as a tuple
-    chat_history.append(("user", user_input))
 
-    # Limit history length to the last 10 messages
-    chat_history = chat_history[-10:]
+    # 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="You are Shadow the Hedgehog and you must act like Shadow the Hedgehog's personality.",
+        system_instruction=updated_system_content,
     )
 
+    # Add user input to history
+    chat_history.append(user_input)
+
+    # Limit history length to the last 10 messages
+    chat_history = chat_history[-10:]
+
     retry_attempts = 3
     for attempt in range(retry_attempts):
         try:
             # Start a new chat session
             chat_session = model.start_chat()
 
-            # Format the history for the model
-            formatted_history = "\n".join([f"{role}: {msg}" for role, msg in chat_history])
-            response = chat_session.send_message(formatted_history)
-
-            # Append the assistant's response to history as a tuple
-            chat_history.append(("assistant", response.text))
-            return chat_history
+            # Send the entire chat history as the first message
+            response = chat_session.send_message("\n".join(chat_history))
+            return response.text, chat_history
 
         except Exception as e:
             if attempt < retry_attempts - 1:
+                time.sleep(2)  # Delay before retrying
                 continue
             else:
-                chat_history.append(("assistant", f"Error after {retry_attempts} attempts: {str(e)}"))
-                return chat_history
-
-# Build the Gradio interface using Chatbot and Button
-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...", lines=2)
-    submit_button = gr.Button("Send")  # Create a button to submit messages
+                return f"Error after {retry_attempts} attempts: {str(e)}", chat_history
+
+# Build the Gradio interface
+with gr.Blocks(theme="Hev832/Applio") as iface:
+    chat_input = gr.Textbox(lines=2, label="Talk to AI", placeholder="Enter your message here...")
     chat_history_state = gr.State([])  # State input for chat history
+    response_output = gr.Textbox(label="Response")
 
     # Define the layout and components
-    submit_button.click(
+    generate_button = gr.Button("Generate Response")
+    generate_button.click(
         fn=generate_response,
-        inputs=[user_input, chat_history_state],
-        outputs=chatbot
+        inputs=[chat_input, chat_history_state],
+        outputs=[response_output, chat_history_state]
     )
 
-    # Optional: Clear the input box after submission
-    def clear_input():
-        return ""
-
-    user_input.submit(
-        fn=generate_response,
-        inputs=[user_input, chat_history_state],
-        outputs=chatbot
-    ).then(clear_input, outputs=[user_input])
-
 iface.launch()
+