Update app.py

app.py

@@ -1,60 +1,79 @@
 import gradio as gr
-from transformers import pipeline
-# Load model directly
-pipe = pipeline("text-generation", model="Aditya0619/Medbot")
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
+from transformers import pipeline, Conversation
+
+# Initialize the model pipeline (Hugging Face conversational model)
+chatbot_pipeline = pipeline("text-generation", model="Aditya0619/Medbot")
+
+# Define the bot's response function
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    # If no conversation history, initialize an empty one
+    if history is None:
+        history = []
+
+    # Build the conversation object with past messages
+    conversation = Conversation()
+    for user_input, bot_response in history:
+        conversation.add_user_input(user_input)
+        conversation.append_response(bot_response)
+
+    # Add the latest user message
+    conversation.add_user_input(message)
+
+    # Generate a response using the chatbot pipeline
+    result = chatbot_pipeline(
+        conversation,
+        max_length=max_tokens,
         temperature=temperature,
         top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+        pad_token_id=50256  # Avoid padding errors for some models like GPT-2 variants
+    )
+
+    # Get the latest response from the model
+    bot_response = result.generated_responses[-1]
+
+    # Update the history with the new exchange
+    history.append((message, bot_response))
+    return history, history
+
+# Define the UI components and layout
+with gr.Blocks() as demo:
+    # Title and description
+    gr.Markdown("# 🤖 AI Chatbot with Memory\nThis chatbot remembers your previous messages.")
+
+    # Input fields for system message and 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
+    )
+
+    # Chatbot interface
+    chatbot = gr.Chatbot(label="Chat with AI")
+    user_input = gr.Textbox(label="Your Message", placeholder="Type a message...")
+
+    # Hidden state to store conversation history
+    state = gr.State([])
+
+    # Submit button to trigger the response
+    submit = gr.Button("Send")
+
+    # Link the 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
+    demo.load(lambda: [("Hi! How can I assist you today?", "")], outputs=chatbot)
+
+# Launch the Gradio app
+demo.launch()