Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+import os
+import time
+from transformers import ResNetForImageClassification, AutoImageProcessor
+
+# Load model and processor
+processor = AutoImageProcessor.from_pretrained("glazzova/body_type")
+model = ResNetForImageClassification.from_pretrained("glazzova/body_type")
+
+# Load example images from the "template" folder
+example_images = [
+    os.path.join("template", x) for x in os.listdir("template") if x.lower().endswith((".png", ".jpg", ".jpeg"))
+]
+
+# Define the classification function
+def body_classification(image):
+    start_time = time.time()  # Record start time
+    inputs = processor(image, return_tensors="pt")  # Process the image
+    
+    # Get predictions
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    
+    predicted_label = logits.argmax(-1).item()
+    label = model.config.id2label[predicted_label]
+    elapsed_time = time.time() - start_time  # Calculate elapsed time
+    
+    return label, f"{elapsed_time:.2f} seconds"
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Body Type Classifier")
+    gr.Markdown(
+        """
+        Upload an image or use the example images to predict the body type. 
+        The app uses a pre-trained ResNet model fine-tuned for body type classification.
+        
+        **by Ishwor Subedi**  
+        GitHub: [@ishworrsubedii](https://github.com/ishworrsubedii)
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Image")
+        with gr.Column():
+            label_output = gr.Textbox(label="Predicted Body Type")
+            time_output = gr.Textbox(label="Processing Time (s)")
+    
+    classify_button = gr.Button("Classify")
+    classify_button.click(body_classification, inputs=image_input, outputs=[label_output, time_output])
+
+    gr.Markdown("### Example Images")
+    # Add example images as inputs
+    gr.Examples(examples=example_images, inputs=image_input, label="Template Images")
+
+# Run the app
+if __name__ == "__main__":
+    demo.launch(debug=True)