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

# Replace with your actual Hugging Face username and model repo
model_id = "Scaryscar/Hackhaton"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"  # automatically maps model to GPU if available
)

# Inference function
def generate_answer(prompt, max_new_tokens=256, temperature=0.7, top_p=0.95):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_p=top_p,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
        )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Gradio UI
gr.Interface(
    fn=generate_answer,
    inputs=[
        gr.Textbox(label="Enter your math problem or prompt here"),
        gr.Slider(50, 1024, value=256, step=1, label="Max New Tokens"),
        gr.Slider(0.1, 1.0, value=0.7, step=0.05, label="Temperature"),
        gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
    ],
    outputs=gr.Textbox(label="WizardMath Response"),
    title="🧙 WizardMath: Fine-Tuned LLM",
    description="Ask WizardMath a math question or give it a reasoning prompt. This model has been fine-tuned for math reasoning using LLM capabilities.",
    theme="default"
).launch()