Intial app code

app.py

@@ -1,7 +1,266 @@
 import gradio as gr
+from fastai.vision.all import *
+from pathlib import Path
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def load_model():
+    """Load the exported FastAI model"""
+    try:
+        model_path = Path('bears_model_clean.pkl')
+        learn = load_learner(model_path)
+        return learn
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None
+
+
+learn = load_model()
+
+
+def classify_bear(image):
+    """
+    Detect bear species from uploaded image
+
+    Args:
+        image: PIL Image or numpy array
+
+    Returns:
+        dict: Prediction probabilities for each bear type
+    """
+    if learn is None:
+        return {"Error": "Model not loaded properly"}
+    if image is None:
+        return {"No Image": "Please upload an image"}
+
+    try:
+        # Make prediction
+        pred, pred_idx, probs = learn.predict(image)
+
+        # Get class names
+        class_names = learn.dls.vocab
+
+        # Create confidence dictionary
+        confidences = {}
+        for i, class_name in enumerate(class_names):
+            confidences[class_name] = float(probs[i])
+
+        return confidences
+
+    except Exception as e:
+        return {"Error": f"Prediction failed: {str(e)}"}
+
+
+def get_bear_info(prediction_dict):
+    """
+    Get information about the predicted bear type
+
+    Args:
+        prediction_dict: Dictionary with prediction confidences
+
+    Returns:
+        str: Information about the most likely bear type
+    """
+    if "Error" in prediction_dict:
+        return prediction_dict["Error"]
+    if "No Image" in prediction_dict:
+        return "Upload an image to learn about the bear species!"
+
+    # Get the bear type with highest confidence
+    top_prediction = max(prediction_dict.items(), key=lambda x: x[1])
+    bear_type = top_prediction[0]
+    confidence = top_prediction[1]
+
+    # Bear information dictionary
+    bear_info = {
+        "black": "🐻 **Black Bear**: The most common bear in North America. They're excellent climbers and swimmers, with a varied omnivorous diet.",
+        "grizzly": "🐻 **Grizzly Bear**: A powerful subspecies of brown bear found in North America. Known for their distinctive shoulder hump and long claws.",
+        "polar": "🐻‍❄️ **Polar Bear**: The largest bear species, perfectly adapted to Arctic life. They're excellent swimmers and primarily hunt seals.",
+        "panda": "🐼 **Giant Panda**: A beloved bear species native to China, famous for their black and white coloring and bamboo diet.",
+        "teddy": "🧸 **Teddy Bear**: A stuffed toy bear! Named after President Theodore Roosevelt, these cuddly companions have been beloved by children for over a century."
+    }
+
+    # Find matching bear info (case insensitive)
+    info = ""
+    for key, value in bear_info.items():
+        if key.lower() in bear_type.lower():
+            info = value
+            break
+
+    if not info:
+        info = f"🐻 **{bear_type}**: A type of bear!"
+
+    return f"{info}\n\n**Confidence**: {confidence:.1%}"
+
+
+def predict_and_explain(image):
+    """
+    Main function that combines prediction and explanation
+
+    Args:
+        image: Input image
+
+    Returns:
+        tuple: (prediction_dict, explanation_text)
+    """
+    predictions = classify_bear(image)
+    explanation = get_bear_info(predictions)
+    return predictions, explanation
+
+
+def handle_image_change(image):
+    """
+    Handle image change events with proper None checking
+
+    Args:
+        image: Input image (can be None when cleared)
+
+    Returns:
+        tuple: (prediction_dict, explanation_text)
+    """
+    if image is None:
+        return {}, "Upload an image to learn about the bear species!"
+
+    return predict_and_explain(image)
+
+
+def get_sample_images():
+    """
+    Get list of sample images if they exist
+
+    Returns:
+        list: List of image paths for examples
+    """
+    sample_paths = [
+        "samples/black.jpg",
+        "samples/grizzly.jpg",
+        "samples/polar.jpg",
+        "samples/panda.jpg",
+        "samples/teddy.jpg"
+    ]
+    existing_samples = []
+    for path in sample_paths:
+        if Path(path).exists():
+            existing_samples.append([path])
+            print(f"✅ Found sample image: {path}")
+        else:
+            print(f"⚠️  Sample image not found: {path}")
+
+    return existing_samples
+
+
+def create_interface():
+    """Create and configure the Gradio interface"""
+
+    css = """
+    .gradio-container {
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    }
+    .bear-title {
+        text-align: center;
+        color: #8B4513;
+        font-size: 2.5em;
+        margin-bottom: 20px;
+    }
+    """
+
+    with gr.Blocks(css=css, title="🐻 Bear Species Detector") as demo:
+        gr.HTML("""
+        <div class="bear-title">
+            🐻 Bear Species Detector 🐼
+        </div>
+        <p style="text-align: center; font-size: 1.2em; color: #666;">
+            Upload an image of a bear and I'll tell you what species it is!<br>
+            <em>Supports: Black Bear, Grizzly Bear, Polar Bear, Giant Panda, and even Teddy Bears! 🧸</em>
+        </p>
+        """)
+
+        with gr.Row():
+            with gr.Column():
+                # Image input
+                image_input = gr.Image(
+                    label="Upload Bear Image 📸",
+                    type="pil",
+                    height=400
+                )
+
+                # Submit button
+                submit_btn = gr.Button(
+                    "Detect Bear Type! 🔍",
+                    variant="primary",
+                    size="lg"
+                )
+
+                # Get sample images
+                sample_images = get_sample_images()
+
+                # Only show examples if we have sample images
+                if sample_images:
+                    gr.Examples(
+                        examples=sample_images,
+                        inputs=image_input,
+                        label="Try these examples:"
+                    )
+                else:
+                    gr.HTML("""
+                    <p style="text-align: center; color: #888; font-style: italic;">
+                        💡 Add sample images to the 'samples/' folder to see examples here!
+                    </p>
+                    """)
+
+            with gr.Column():
+                # Prediction output
+                prediction_output = gr.Label(
+                    label="Prediction Confidence 📊",
+                    num_top_classes=5
+                )
+
+                # Bear information output
+                info_output = gr.Markdown(
+                    label="Bear Information 📖",
+                    value="Upload an image to learn about the bear species!"
+                )
+
+        # Connect the interface
+        submit_btn.click(
+            fn=predict_and_explain,
+            inputs=image_input,
+            outputs=[prediction_output, info_output]
+        )
+
+        # Also trigger on image upload
+        image_input.change(
+            fn=handle_image_change,
+            inputs=image_input,
+            outputs=[prediction_output, info_output]
+        )
+
+        gr.HTML("""
+        <div style="text-align: center; margin-top: 30px; color: #888;">
+            <p>Built with ❤️ using FastAI and Gradio</p>
+        </div>
+        """)
+
+    return demo
+
+
+# Main execution
+if __name__ == "__main__":
+    # Check if model is loaded
+    if learn is None:
+        print("❌ Error: Could not load the model. Please ensure 'bears_model_xx.pkl' is in the correct path.")
+        print("💡 Tip: Update the model_path in the load_model() function to point to your saved model.")
+    else:
+        print("✅ Model loaded successfully!")
+        print(f"📋 Classes: {learn.dls.vocab}")
+
+    # Create and launch the interface
+    demo = create_interface()
+
+    # Launch the app
+    demo.launch(
+        share=True,  # Set to True to create a public link
+        server_name="0.0.0.0",  # Allow access from any IP
+        server_port=7860,  # Default Gradio port
+        show_error=True
+    )