app.py

import gradio as gr
from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration

# Load the model and tokenizer
MODEL_NAME = "facebook/blenderbot-400M-distill"
try:
    tokenizer = BlenderbotTokenizer.from_pretrained(MODEL_NAME)
    model = BlenderbotForConditionalGeneration.from_pretrained(MODEL_NAME)
    print("Model and tokenizer loaded successfully!")
except Exception as e:
    print(f"Error loading model or tokenizer: {e}")

# Function to generate chatbot response
def chat_bot(user_input, history):
    """Handles chat conversation in real-time."""
    try:
        # Tokenize the user input
        inputs = tokenizer([user_input], return_tensors="pt", max_length=1000, truncation=True)

        # Generate the response using the model
        reply_ids = model.generate(**inputs)
        response = tokenizer.decode(reply_ids[0], skip_special_tokens=True)
        print(f"Generated response: {response}")  # Debugging: Print generated response

        # Update the history
        history.append((user_input, response))
        print(f"Updated history: {history}")  # Debugging: Print updated history

        return history
    except Exception as e:
        print(f"Error in chat_bot function: {e}")
        return history  # Return the history even if there's an error

# Gradio Chat Interface
with gr.Blocks() as app:
    gr.Markdown("<h1 style='text-align: center;'>💬 Open-Source Chatbot</h1>")
    
    chatbot = gr.Chatbot()
    msg = gr.Textbox(placeholder="Type your message here...", label="Your Message")
    clear = gr.Button("Clear")

    # Auto-update conversation
    msg.submit(chat_bot, inputs=[msg, chatbot], outputs=[chatbot])
    clear.click(lambda: [], None, chatbot, queue=False)

# Launch app in Hugging Face Spaces
app.launch(share=True)