import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel

model_name = "HuggingFaceH4/zephyr-7b-beta"
adapter_path = "zephyr_lora_adapter"

tokenizer = AutoTokenizer.from_pretrained(adapter_path)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)

base_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

model = PeftModel.from_pretrained(base_model, adapter_path)
model.eval()

def solve_math(question, max_tokens=512):
    prompt = f"<|user|>\n{question}\n<|assistant|>\n"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_tokens,
            do_sample=False,
            pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id
        )
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return decoded.split("<|assistant|>")[-1].strip()

demo = gr.Interface(fn=solve_math,
                    inputs=gr.Textbox(lines=5, label="Enter math problem"),
                    outputs=gr.Textbox(label="Solution"),
                    title="Math Solver (Zephyr Fine-Tuned)",
                    description="This app uses a fine-tuned LLM to solve school-level math problems step by step.")

demo.launch()