init push

app.py

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+import gradio as gr
+import requests
+from PIL import Image
+from io import BytesIO
+from transformers import pipeline
+
+# 1. Load a pretrained ResNet-50 from the Hugging Face Hub
+model_id = "halictus/resnet50_honeybee"
+classifier = pipeline("image-classification", model=model_id)
+
+# 2. Define an inference function
+def classify_image_from_url(image_url: str):
+    """
+    Downloads an image from a public URL and runs it through
+    the ResNet-50 image-classification pipeline, returning the top predictions.
+    """
+    try:
+        # Fetch the image
+        response = requests.get(image_url)
+        response.raise_for_status()
+        image = Image.open(BytesIO(response.content)).convert("RGB")
+        
+        # Run inference
+        results = classifier(image)
+        
+        # You can return raw results or format them as desired
+        return results
+    
+    except Exception as e:
+        return {"error": str(e)}
+
+# 3. Create a Gradio interface
+#    - We accept a single Textbox input (the public image URL)
+#    - We return the classification results in JSON format
+demo = gr.Interface(
+    fn=classify_image_from_url,
+    inputs=gr.Textbox(lines=1, label="Image URL"),
+    outputs="json",
+    title="ResNet-50 Image Classifier",
+    description="Enter a public image URL to get top predictions."
+)
+
+# 4. Launch the app
+if __name__ == "__main__":
+    demo.launch()
+