app.py

import gradio as gr
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch

print("Loading model...")

# Load your model from Hugging Face
MODEL_NAME = "Branis333/astro-gpt2-chatbot"
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_NAME)
model = GPT2LMHeadModel.from_pretrained(MODEL_NAME)

# Set model to evaluation mode
model.eval()

print("Model loaded successfully!")

def format_question(question):
    """
    Automatically add question mark if not present.
    
    Args:
        question (str): The input question
    
    Returns:
        str: Question with proper punctuation
    """
    question = question.strip()
    
    # Check if question already ends with ?, !, or .
    if not question.endswith(('?', '!', '.')):
        question = question + '?'
    
    return question

def answer_astronomy_question(question, max_length=150, temperature=0.7, top_p=0.9):
    """Generate an answer to an astronomy question."""
    
    # Automatically format question with ? if needed
    formatted_question = format_question(question)
    
    # Format the prompt
    prompt = f"Q: {formatted_question}\nA:"
    
    # Tokenize input
    inputs = tokenizer(prompt, return_tensors="pt")
    
    # Generate response
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_length,
            temperature=temperature,
            top_p=top_p,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=tokenizer.eos_token_id,
            repetition_penalty=1.2,
        )
    
    # Decode the output
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Extract just the answer part
    if "A:" in generated_text:
        answer = generated_text.split("A:", 1)[1].strip()
    else:
        answer = generated_text.strip()
    
    return answer

# Examples with ALL 4 parameters
examples = [
    ["What is a black hole?", 150, 0.7, 0.9],
    ["What is a constellation?", 150, 0.7, 0.9],
    ["What causes auroras on Earth?", 150, 0.7, 0.9],
    ["Explain the difference between a planet and a star.", 200, 0.7, 0.9],
    ["What is the Big Bang theory?", 200, 0.8, 0.9],
]

# Create Gradio interface
interface = gr.Interface(
    fn=answer_astronomy_question,
    inputs=[
        gr.Textbox(
            label="Ask an Astronomy Question",
            placeholder="e.g., What is a black hole (question mark is optional)",
            lines=2
        ),
        gr.Slider(
            minimum=50,
            maximum=300,
            value=150,
            step=10,
            label="Max Answer Length"
        ),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.7,
            step=0.1,
            label="Temperature (creativity)"
        ),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.9,
            step=0.1,
            label="Top-p (diversity)"
        ),
    ],
    outputs=gr.Textbox(
        label="Answer",
        lines=8
    ),
    examples=examples,
    title="🌌 Astronomy GPT-2 Chatbot",
    description="""
    Ask questions about astronomy and space science! This chatbot is powered by a fine-tuned GPT-2 model 
    trained on 2,736 astronomy Q&A pairs.
    
    **Tip:** You don't need to add a question mark - it will be added automatically! ✨
    
    **Note:** This is an educational tool. Always verify important astronomical facts with authoritative sources.
    """,
    article="""
    ### About This Model
    - **Base Model:** GPT-2
    - **Training Data:** 2,736 cleaned astronomy Q&A pairs
    - **Perplexity:** 1.61
    - **Specialization:** Astronomy terminology, concepts, and phenomena
    
    ### Tips for Best Results:
    - Ask specific, clear questions (question mark optional!)
    - Lower temperature = more focused answers
    - Higher temperature = more creative answers
    
    ### Model Repository
    [View on Hugging Face](https://huggingface.co/Branis333/astro-gpt2-chatbot)
    """,
    theme=gr.themes.Soft(),
)

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