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

# ✅ Load the fastest model for code review
model_name = "EleutherAI/pythia-70m"  # Smallest & fastest alternative
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cpu")

# ✅ Function to analyze Python code
def review_code(code_snippet):
    inputs = tokenizer(code_snippet, return_tensors="pt").to("cuda")  # Move input to GPU
    outputs = model.generate(**inputs, max_length=80, do_sample=False, num_beams=3)  # Fast inference
    reviewed_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return reviewed_code

# ✅ Function to handle user input & return reviewed code
def check_code(input_code):
    reviewed_code = review_code(input_code)
    return input_code, reviewed_code, reviewed_code  # Return for UI display & download

# ✅ Gradio UI with Side-by-Side Comparison & Download Option
interface = gr.Interface(
    fn=check_code,
    inputs=gr.Textbox(label="Enter Python Code"),
    outputs=[
        gr.Textbox(label="Original Code", interactive=False),  # Left side
        gr.Textbox(label="Reviewed Code", interactive=False),  # Right side
        gr.File(label="Download Reviewed Code")  # Download button
    ],
    title="🚀 AI Code Reviewer",
    description="Enter Python code and get a reviewed version. Download the reviewed code as a file.",
    allow_flagging="never"
)

# ✅ Launch the app on Hugging Face Spaces with timeout settings
interface.launch(share=True)