Add Gradio app with WebP support, beautiful UI, and deployment files

- Fixed WebP image processing by implementing manual preprocessing
- Added beautiful Gradio interface with bird class descriptions and example images
- Created minimal requirements.txt for Hugging Face Spaces deployment
- Updated README with proper Space configuration and documentation
- Added res.pkl model file via Git LFS

README.md

@@ -1,14 +1,45 @@
 ---
-title: Penguin Or Puffin
-emoji: 🐠
-colorFrom: pink
-colorTo: green
+title: Penguin Or Puffin Classifier
+emoji: 🐧
+colorFrom: blue
+colorTo: purple
 sdk: gradio
 sdk_version: 5.45.0
 app_file: app.py
 pinned: false
 license: apache-2.0
-short_description: image multiclassifier for real and fake penguins
+short_description: AI classifier for King Penguins, Gentoo Penguins, and Puffins
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Arctic & Antarctic Bird Classifier
+
+An bird classification model that can distinguish between:
+- **King Penguin** (Epic)
+- **Gentoo Penguin** (Classic) 
+- **Puffin** (Fake Short Penguin)
+
+## Features
+
+- Built with FastAI and ResNet architecture
+- Supports WebP, JPG, and PNG image formats
+- Beautiful Gradio interface with example images
+- Handles images from any angle with robust preprocessing
+
+## How It Works
+
+The model analyzes visual features like:
+- Body shape and size (Penguins are larger and more streamlined)
+- Beak characteristics (Puffins have colorful triangular beaks)
+- Plumage patterns (Each species has unique markings)
+- Overall proportions (Different body-to-head ratios)
+
+Try to trick the model into guessing a puffin is a penguin - that would be very impressive!
+
+## Technical Details
+
+- **Model**: ResNet-based CNN trained with FastAI
+- **Input**: 224x224 RGB images
+- **Preprocessing**: Manual image processing to handle WebP and other formats
+- **Interface**: Gradio web app with custom styling
+
+Upload an image and let the AI identify which magnificent bird you've found!

app.py

@@ -1,8 +1,221 @@
-app.py
 import gradio as gr
+from fastai.vision.all import *
+import torch
+import numpy as np
+from PIL import Image
 
-def greet(name):
-    return "Hello " + name + "!!"
+learn18 = load_learner('res.pkl')
+labels = learn18.dls.vocab
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Get the model and its transforms for manual processing
+model = learn18.model
+device = next(model.parameters()).device
+
+# Standard ImageNet normalization used by most vision models
+mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
+std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
+
+def predict(img):
+    try:
+        print(f"Input type: {type(img)}")
+        
+        # Convert to PIL Image if needed
+        if isinstance(img, str):
+            img = Image.open(img)
+        elif not hasattr(img, 'convert'):
+            img = Image.fromarray(np.array(img))
+        
+        # Ensure RGB mode
+        if img.mode != 'RGB':
+            img = img.convert('RGB')
+        
+        print(f"Processing PIL image: {img.mode}, size: {img.size}")
+        
+        # Manual preprocessing to bypass problematic transforms
+        # Resize to 224x224 (typical model input size)
+        img_resized = img.resize((224, 224), Image.BILINEAR)
+        
+        # Convert to tensor
+        img_array = np.array(img_resized).astype(np.float32) / 255.0
+        img_tensor = torch.from_numpy(img_array).permute(2, 0, 1).unsqueeze(0).to(device)
+        
+        # Normalize
+        img_normalized = (img_tensor - mean) / std
+        
+        # Run inference
+        model.eval()
+        with torch.no_grad():
+            outputs = model(img_normalized)
+            probs = torch.softmax(outputs, dim=1)
+            probs = probs.cpu().numpy()[0]
+        
+        print(f"Prediction successful: {len(probs)} classes")
+        return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    
+    except Exception as e:
+        print(f"Error in predict function: {e}")
+        import traceback
+        traceback.print_exc()
+        return {"Error": 1.0}
+
+# Custom CSS for better styling
+custom_css = """
+.gradio-container {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+}
+.header {
+    text-align: center;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    color: white;
+    padding: 2rem;
+    border-radius: 10px;
+    margin-bottom: 2rem;
+}
+.bird-info {
+    background: #f8f9fa;
+    padding: 1.5rem;
+    border-radius: 10px;
+    margin: 1rem 0;
+    border-left: 4px solid #667eea;
+}
+"""
+
+# Detailed descriptions for each bird class
+bird_descriptions = """
+## About the Bird Classes
+
+This model can distinguish between three bird species:
+
+### **King Penguin:** epic
+- **Habitat**: Sub-Antarctic islands
+- **Size**: Second largest penguin species (70-100 cm tall)
+- **Features**: Orange-yellow neck patches, long curved beak, streamlined body
+- **Fun fact**: Can dive up to 300 meters deep!
+
+### **Gentoo Penguin:** classic
+- **Habitat**: Antarctic Peninsula and sub-Antarctic islands
+- **Size**: Third largest penguin species (51-90 cm tall)
+- **Features**: White stripe across the top of the head, bright orange-red beak
+- **Fun fact**: Fastest swimming penguin, reaching speeds up to 36 km/h!
+
+### **Puffin:** fake short penguin
+- **Habitat**: North Atlantic and North Pacific coasts
+- **Size**: Much smaller than penguins (25-30 cm tall)
+- **Features**: Colorful triangular beak (especially during breeding season), black and white plumage
+- **Fun fact**: Can hold up to 60 small fish in their beak at once!
+
+*Try to trick the model into guessing a puffin is a penguin, that would be very impressive!*
+"""
+
+example_images = [
+    "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT61sjxMkdnjjyzqSLuQLojxcvbjVvLT8zHcA&s",  # King Penguin
+    "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQUH6u4B2TmBYZhXBBvVVJN85sn8dCed3XD3g&s",  # Gentoo Penguin
+    "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRJCVzXkCE5haPfcwlJTj1QpY2bpw2-TNXACA&s"   # Puffin
+]
+
+with gr.Blocks(css=custom_css, title="🐧 Arctic & Antarctic Bird Classifier 🐦") as demo:
+    gr.HTML("""
+    <div class="header">
+        <h1>Made with Resnet18 model</h1>
+        <p style="font-size: 1.2em; margin-top: 1rem;">Simple AI-powered bird identification for Penguins and Puffins</p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### Upload Bird Image")
+            input_image = gr.Image(
+                type="pil", 
+                label="Drop an image here or click to upload",
+                height=400
+            )
+            
+            predict_btn = gr.Button(
+                "Analyze (run inference)", 
+                variant="primary", 
+                size="lg"
+            )
+            
+            gr.Markdown("#### tips:")
+            gr.Markdown("""
+            - JPG, PNG, and WebP formats supported
+            - Works with photos from any angle
+            """)
+        
+        with gr.Column(scale=1):
+            gr.Markdown("### Detection Results")
+            output_label = gr.Label(
+                num_top_classes=3,
+                label="Species Confidence Scores"
+            )
+            
+            gr.Markdown("### 📊 How it works")
+            gr.Markdown("""
+            This model uses deep learning to analyze visual features like:
+            - **Body shape and size** - Penguins are larger and more streamlined
+            - **Beak characteristics** - Puffins have colorful triangular beaks
+            - **Plumage patterns** - Each species has unique markings
+            - **Overall proportions** - Different body-to-head ratios
+            """)
+    
+    with gr.Row():
+        gr.Markdown("## About the Bird Classes")
+    
+    gr.Markdown("This model can distinguish between three similar looking bird species:")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### **King Penguin: Epic**")
+            gr.Image(
+                value=example_images[0], 
+                label="King Penguin Example",
+                height=200,
+                interactive=False
+            )
+            gr.Markdown("""
+            - **Habitat**: Sub-Antarctic islands
+            - **Size**: Second largest penguin species (70-100 cm tall)
+            - **Features**: Orange-yellow neck patches, long curved beak, streamlined body
+            - **Fun fact**: Can dive up to 300 meters deep!
+            """)
+            
+        with gr.Column():
+            gr.Markdown("### **Gentoo Penguin: Classic**")
+            gr.Image(
+                value=example_images[1], 
+                label="Gentoo Penguin Example",
+                height=200,
+                interactive=False
+            )
+            gr.Markdown("""
+            - **Habitat**: Antarctic Peninsula and sub-Antarctic islands
+            - **Size**: Third largest penguin species (51-90 cm tall)
+            - **Features**: White stripe across the top of the head, bright orange-red beak
+            - **Fun fact**: Fastest swimming penguin, reaching speeds up to 36 km/h!
+            """)
+            
+        with gr.Column():
+            gr.Markdown("### **Puffin: Fake Short Penguin**")
+            gr.Image(
+                value=example_images[2], 
+                label="Puffin Example",
+                height=200,
+                interactive=False
+            )
+            gr.Markdown("""
+            - **Habitat**: North Atlantic and North Pacific coasts
+            - **Size**: Much smaller than penguins (25-30 cm tall)
+            - **Features**: Colorful triangular beak (especially during breeding season), black and white plumage
+            - **Fun fact**: Can hold up to 60 small fish in their beak at once!
+            """)
+    
+    gr.Markdown("*Try to trick the model into guessing a puffin is a penguin, that would be very impressive!*")
+    
+    predict_btn.click(
+        fn=predict,
+        inputs=input_image,
+        outputs=output_label
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

@@ -0,0 +1,6 @@
+gradio==5.45.0
+fastai==2.8.4
+torch==2.8.0
+torchvision==0.23.0
+pillow==11.3.0
+numpy==2.3.3

res.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21d83ee9f575bcf0c1252e10a942269cebeb71ecbf28a4b3b80746e586fdb152
+size 46988745