app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load your model and tokenizer
model_name = "facebook/blenderbot-400M-distill"  # Replace with your model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the chatbot function
def chatbot_response(input_text):
    inputs = tokenizer.encode(input_text, return_tensors="pt")
    outputs = model.generate(inputs, max_length=50, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Create Gradio interface
interface = gr.Interface(
    fn=chatbot_response,
    inputs=gr.Textbox(label="Ask me anything!"),
    outputs=gr.Textbox(label="Response"),
    title="My Chatbot",
    description="A simple chatbot deployed using Hugging Face Spaces and Gradio!"
)

# Launch the app
if __name__ == "__main__":
    interface.launch()