Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import hf_hub_download
+import torch
+from PIL import Image
+
+# Load your model from your HF repo
+model = torch.load(
+    hf_hub_download(repo_id="newtechdevng/detect", filename="your_model_file.pt"),
+    map_location="cpu"
+)
+model.eval()
+
+# Define labels
+labels = ["car", "bike", "mountain", "road"]  # update to match your model
+
+def predict(image):
+    # Preprocess
+    from torchvision import transforms
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+    ])
+    tensor = transform(image).unsqueeze(0)
+    
+    with torch.no_grad():
+        output = model(tensor)
+        predicted = torch.argmax(output, dim=1).item()
+    
+    return labels[predicted]
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(),
+    title="Car/Bike/Mountain/Road Detector"
+).launch()