script.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

# Load your model
model_path = "best_model_final"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
model.eval()

# Prediction function
def predict_cpu_memory(code):
    inputs = tokenizer(code, return_tensors="pt", padding=True, truncation=True)

    with torch.no_grad():
        outputs = model(**inputs)
        preds = F.sigmoid(outputs.logits).numpy()

    cpu_time, memory_usage = preds[0]
    return f"CPU Time: {cpu_time:.4f}\nMemory Usage: {memory_usage:.4f}"

# Gradio Interface
iface = gr.Interface(
    fn=predict_cpu_memory,
    inputs=gr.Textbox(lines=10, placeholder="Paste your code here..."),
    outputs="text",
    title="Code Resource Usage Predictor"
)

if __name__ == "__main__":
    iface.launch()