Update app.py

app.py

@@ -1,79 +1,69 @@
 import gradio as gr
-from transformers import pipeline, Conversation
+from transformers import pipeline
 
-# Initialize the model pipeline (Hugging Face conversational model)
+# Initialize the conversational model pipeline
 chatbot_pipeline = pipeline("text-generation", model="Aditya0619/Medbot")
 
-# Define the bot's response function
+# Function to manage history and generate responses
 def respond(message, history, system_message, max_tokens, temperature, top_p):
-    # If no conversation history, initialize an empty one
+    # Initialize history if it's None
     if history is None:
         history = []
 
-    # Build the conversation object with past messages
-    conversation = Conversation()
+    # Build input by concatenating past messages (user-bot pairs)
+    chat_input = ""
     for user_input, bot_response in history:
-        conversation.add_user_input(user_input)
-        conversation.append_response(bot_response)
+        chat_input += f"User: {user_input}\nBot: {bot_response}\n"
+    chat_input += f"User: {message}\nBot:"
 
-    # Add the latest user message
-    conversation.add_user_input(message)
-
-    # Generate a response using the chatbot pipeline
-    result = chatbot_pipeline(
-        conversation,
+    # Generate a response using the pipeline
+    response = chatbot_pipeline(
+        chat_input,
         max_length=max_tokens,
         temperature=temperature,
         top_p=top_p,
-        pad_token_id=50256  # Avoid padding errors for some models like GPT-2 variants
-    )
+        pad_token_id=50256  # Avoids padding errors with models like GPT-2
+    )[0]["generated_text"].split("Bot:")[-1].strip()
 
-    # Get the latest response from the model
-    bot_response = result.generated_responses[-1]
+    # Update history with the new interaction
+    history.append((message, response))
 
-    # Update the history with the new exchange
-    history.append((message, bot_response))
+    # Return the updated chat history
     return history, history
 
-# Define the UI components and layout
+# Define the Gradio app layout
 with gr.Blocks() as demo:
-    # Title and description
-    gr.Markdown("# 🤖 AI Chatbot with Memory\nThis chatbot remembers your previous messages.")
+    gr.Markdown("# 🤖 AI Chatbot with Memory\nChat with the bot and it will remember your conversation!")
 
-    # Input fields for system message and settings
+    # Input fields for settings
     system_message = gr.Textbox(
         label="System Message (Optional)",
         placeholder="e.g., You are a helpful assistant."
     )
-    max_tokens = gr.Slider(
-        label="Max Tokens", minimum=50, maximum=500, value=250, step=10
-    )
-    temperature = gr.Slider(
-        label="Temperature", minimum=0.0, maximum=1.0, value=0.7, step=0.1
-    )
-    top_p = gr.Slider(
-        label="Top P", minimum=0.0, maximum=1.0, value=0.9, step=0.1
-    )
+    max_tokens = gr.Slider(label="Max Tokens", minimum=50, maximum=500, value=250, step=10)
+    temperature = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.7, step=0.1)
+    top_p = gr.Slider(label="Top P", minimum=0.0, maximum=1.0, value=0.9, step=0.1)
 
-    # Chatbot interface
+    # Chatbot interface and user input
     chatbot = gr.Chatbot(label="Chat with AI")
-    user_input = gr.Textbox(label="Your Message", placeholder="Type a message...")
+    user_input = gr.Textbox(label="Your Message", placeholder="Type a message...", lines=2)
 
     # Hidden state to store conversation history
     state = gr.State([])
 
-    # Submit button to trigger the response
+    # Submit button to send messages
     submit = gr.Button("Send")
 
-    # Link the input and chatbot response function
+    # Link the user input and chatbot response function
     submit.click(
         respond,
         inputs=[user_input, state, system_message, max_tokens, temperature, top_p],
         outputs=[chatbot, state]
     )
 
-    # Display an initial greeting message
+    # Initial greeting message
     demo.load(lambda: [("Hi! How can I assist you today?", "")], outputs=chatbot)
 
 # Launch the Gradio app
 demo.launch()
+