app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the tokenizer and model (change 'model_name' to your specific model)
model_name = "gpt2"  # Replace with your model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Function to generate a response
def generate_response(prompt):
    if not prompt:
        return "Please enter a prompt."
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=512)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    
    return response

# Streamlit UI
st.title("AI Text Generator")

prompt = st.text_area("Enter your prompt:", placeholder="Type your question or prompt here...")

if st.button("Generate Response"):
    with st.spinner("Generating response..."):
        response = generate_response(prompt)
    st.text_area("Model Response:", value=response, height=200, disabled=True)