Upload app.py

app.py

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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()