app.py

# app.py

from transformers import pipeline
import gradio as gr

# Load the text2text generation pipeline
pipe = pipeline("text2text-generation", model="google/flan-t5-base")

# Initialize conversation history
conversation_history = ""

def converse(user_message):
    global conversation_history
    # Append the user's message to the conversation history
    conversation_history += f"User: {user_message}\n"

    # Generate response
    response = pipe(conversation_history, max_length=200)[0]['generated_text']

    # Append the model's response
    conversation_history += f"Bot: {response}\n"

    return response

# Gradio UI
iface = gr.Interface(
    fn=converse,
    inputs=gr.Textbox(lines=2, placeholder="Enter your message...", label="Your Message"),
    outputs=gr.Textbox(label="Bot Response"),
    title="Chat with FLAN-T5",
    description="A simple conversational app using FLAN-T5 and Gradio"
)

# Launch for Hugging Face Spaces
if __name__ == "__main__":
    iface.launch()