app.py

import torch
import gradio as gr
import torch.nn as nn
import torch.nn.functional as F
import os
from pathlib import Path
from torch.nn import init
import torchvision.transforms as transforms
from PIL import Image

# ... [Keep all your existing model definitions and initialization code] ...

# Precompute example image paths
example_dir = "examples"
example_images = [os.path.join(example_dir, f) for f in os.listdir(example_dir) 
                 if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]

# Custom CSS for styling
css = """
.centered-examples {
    margin: 0 auto !important;
    justify-content: center !important;
    gap: 8px !important;
}
.centered-examples .thumb {
    height: 100px !important;
    width: 100px !important;
    object-fit: cover !important;
}
"""

# PREPROCESSING PIPELINE (ADD THIS BACK)
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Precompute example image paths
example_dir = "examples"
example_images = [os.path.join(example_dir, f) for f in os.listdir(example_dir) 
                 if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]

def predict(img_path):
    """Process single image and return prediction"""
    if not img_path:
        return "Please select or upload an image first"
    
    try:
        image = Image.open(img_path).convert('RGB')
        tensor = preprocess(image).unsqueeze(0)
        
        with torch.inference_mode():
            outputs = model(tensor)
            _, pred = torch.max(outputs, 1)
        
        return classes[pred.item()]
    
    except Exception as e:
        return f"Error: {str(e)}"

with gr.Blocks(title="Animal Classifier", css=css) as demo:
    gr.Markdown("## 🐾 Animal Classifier")
    gr.Markdown("Select an image below or upload your own, then click Classify")
    
    # Store current image path
    current_image = gr.State()
    
    with gr.Row():
        with gr.Column():
            image_preview = gr.Image(label="Selected Image", type="filepath")
            upload_btn = gr.UploadButton("Upload Custom Image", file_types=["image"])
            classify_btn = gr.Button("Classify 🚀", variant="primary")
        result = gr.Textbox(label="Prediction", lines=3)
    
    # Example gallery at bottom
    with gr.Row(variant="panel"):
        examples_gallery = gr.Gallery(
            value=example_images,
            label="Example Images (Click to Select)",
            columns=7,
            height=120,
            allow_preview=False,
            elem_classes=["centered-examples"]
        )

    # Handle image selection from examples
    def select_example(evt: gr.SelectData):
        return example_images[evt.index]
    
    examples_gallery.select(
        fn=select_example,
        outputs=[image_preview, current_image],
        show_progress=False
    )

    # Handle custom uploads
    upload_btn.upload(
        fn=lambda file: (file.name, file.name),
        inputs=upload_btn,
        outputs=[image_preview, current_image]
    )

    # Handle classification
    classify_btn.click(
        fn=predict,
        inputs=current_image,
        outputs=result
    )

if __name__ == "__main__":
    demo.launch(show_error=True)