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.gitattributes

@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+cat.png filter=lfs diff=lfs merge=lfs -text
+frog.png filter=lfs diff=lfs merge=lfs -text
+hippo.png filter=lfs diff=lfs merge=lfs -text
+jaguar.png filter=lfs diff=lfs merge=lfs -text
+sloth.png filter=lfs diff=lfs merge=lfs -text
+toucan.png filter=lfs diff=lfs merge=lfs -text
+turtle.png filter=lfs diff=lfs merge=lfs -text

animal_image_app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+# Load image classification model
+# Using a pre-trained model that can classify various animals and objects
+classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+def classify_image(image):
+    """
+    Classify an uploaded animal image and return top predictions with progress bars
+    """
+    if image is None:
+        return "<div style='font-family: Arial, sans-serif; padding: 20px; text-align: center; color: #666;'>Please upload an image.</div>"
+    
+    # Classify the image
+    results = classifier(image)
+    
+    # Format results with HTML progress bars - show top 5 predictions
+    html_results = "<div style='font-family: Arial, sans-serif;'>"
+    html_results += "<h3 style='margin-top: 0;'>Top Predictions:</h3>"
+    
+    for i, result in enumerate(results[:5], 1):
+        label = result['label']
+        score = result['score'] * 100
+        score_int = int(score)
+        
+        # Create progress bar with color gradient (green for high, yellow for medium, red for low)
+        if score_int >= 70:
+            bar_color = "#4CAF50"  # Green
+        elif score_int >= 40:
+            bar_color = "#FF9800"  # Orange
+        else:
+            bar_color = "#F44336"  # Red
+        
+        html_results += f"""
+        <div style='margin-bottom: 15px;'>
+            <div style='display: flex; justify-content: space-between; margin-bottom: 5px;'>
+                <span style='font-weight: bold;'>{i}. {label}</span>
+                <span style='font-weight: bold; color: #333;'>{score:.2f}%</span>
+            </div>
+            <div style='background-color: #e0e0e0; border-radius: 10px; height: 25px; overflow: hidden;'>
+                <div style='background-color: {bar_color}; height: 100%; width: {score:.2f}%; transition: width 0.3s ease; border-radius: 10px;'></div>
+            </div>
+        </div>
+        """
+    
+    html_results += "</div>"
+    return html_results
+
+# Create the Gradio interface
+with gr.Blocks(title="Animal Image Classifier") as demo:
+    gr.Markdown("# Animal Image Classifier")
+    gr.Markdown("Upload an animal photo to classify it using AI!")
+    
+    with gr.Row():
+        with gr.Column():
+            # Image input
+            input_image = gr.Image(
+                type="pil",
+                label="Upload Animal Photo"
+            )
+            
+            # Classify button
+            classify_btn = gr.Button("Classify Image", variant="primary", size="lg")
+            clear_btn = gr.Button("Clear", variant="secondary")
+        
+        with gr.Column():
+            # Output for classification results with HTML progress bars
+            output_html = gr.HTML(
+                label="Classification Results"
+            )
+    
+    # Example images at the bottom
+    gr.Markdown("### Example Images")
+    gr.Markdown("Try these example images:")
+    
+    example_images = [
+        "cat.png",
+        "frog.png",
+        "hippo.png",
+        "jaguar.png",
+        "sloth.png",
+        "toucan.png",
+        "turtle.png"
+    ]
+    
+    # Create example gallery - images are in the same directory as this script
+    import os
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    example_paths = [[os.path.join(script_dir, img)] for img in example_images]
+    
+    gr.Examples(
+        examples=example_paths,
+        inputs=input_image,
+        label="Click on an example image to load it"
+    )
+    
+    # Define button actions
+    classify_btn.click(
+        fn=classify_image,
+        inputs=input_image,
+        outputs=output_html
+    )
+    
+    clear_btn.click(
+        fn=lambda: (None, "<div></div>"),
+        inputs=None,
+        outputs=[input_image, output_html]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+transformers
+torch
+sentencepiece