app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Use a lightweight and public model
model_name = "distilgpt2"  # You can also use "tiiuae/falcon-rw-1b" or "EleutherAI/gpt-neo-1.3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define text generation function
def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        inputs["input_ids"],
        max_length=100,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        top_k=50,
        top_p=0.95,
        temperature=0.7,
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Gradio interface with styling
def build_interface():
    with gr.Blocks(theme=gr.themes.Base(), css="""
        body { background-color: #FFFACD; }
        h1 { color: brown; font-weight: bold; text-align: center; }
        footer { text-align: center; padding-top: 10px; font-style: italic; color: #555; }
    """) as demo:
        gr.Markdown("# AI Text Generation Chatbot")
        with gr.Row():
            with gr.Column():
                input_text = gr.Textbox(label="Enter your prompt", placeholder="e.g., Once upon a time...")
                submit_btn = gr.Button("Generate Text")
            with gr.Column():
                output_text = gr.Textbox(label="Generated Text")

        submit_btn.click(fn=generate_response, inputs=input_text, outputs=output_text)
        gr.Markdown("<footer>Designed by Mehak Mazhar</footer>")
    return demo

# Launch app
if __name__ == "__main__":
    demo = build_interface()
    demo.launch()