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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()
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