app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load GPT-2 model and tokenizer
MODEL_NAME = "distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)

# Conversation history
conversation_history = ""

def chat(user_input):
    global conversation_history
    # Add user input to conversation history
    conversation_history += f"User: {user_input}\n"

    # Encode the conversation and generate a response
    inputs = tokenizer(conversation_history, return_tensors="pt", truncation=True, max_length=1000)
    outputs = model.generate(**inputs, max_length=200, num_return_sequences=1)
    
    # Decode and add bot response to history
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    conversation_history += f"Bot: {response}\n"

    return response

# Gradio interface
interface = gr.Interface(
    fn=chat,
    inputs=gr.Textbox(placeholder="Type your message here..."),
    outputs="text",
    title="GPT-2 Chatbot",
    description="A simple chatbot powered by GPT-2"
)

if __name__ == "__main__":
    interface.launch()