Create app.py

app.py

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+import gradio as gr
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+# Load a pre-trained image classification model
+model_name = "microsoft/resnet-50"
+image_processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+def classify_image(image):
+    # Ensure the image is in RGB mode
+    if image is None:
+        return "No image uploaded"
+    
+    # Convert image to RGB if needed
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+    
+    # Preprocess the image
+    inputs = image_processor(images=image, return_tensors="pt")
+    
+    # Perform prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+    
+    # Get predictions
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+    
+    # Get class labels and handle fewer than 5 classes
+    labels = model.config.id2label
+    num_classes = len(labels)
+    
+    # Determine number of predictions to show
+    top_k = min(num_classes, 3)
+    
+    # Get top predictions
+    top_prob, top_indices = probabilities.topk(top_k)
+    
+    # Format results
+    results = []
+    for prob, idx in zip(top_prob[0], top_indices[0]):
+        label = labels[idx.item()]
+        percentage = prob.item() * 100
+        results.append(f"{label}: {percentage:.2f}%")
+    
+    return "\n".join(results)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(label="Top Predictions"),
+    title="Image Classification with Hugging Face",
+    description="Upload an image to get classification predictions"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()