app.py

import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
import gradio as gr

# Device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Classes
class_names = ["Cat", "Dog"]

# Load model (architecture same as training)
def load_model(model_path="pet_model.pth"):
    base_model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
    in_features = base_model.fc.in_features
    base_model.fc = nn.Sequential(
        nn.Linear(in_features, 512),
        nn.ReLU(),
        nn.Dropout(0.4),
        nn.Linear(512, len(class_names))
    )
    base_model.load_state_dict(torch.load(model_path, map_location=device))
    base_model.to(device)
    base_model.eval()
    return base_model

model = load_model()

# EXACT same transform as training
transform = transforms.Compose([
    transforms.Lambda(lambda x: x.convert('RGB')),
    transforms.Resize((224,224)),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.ColorJitter(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5],
                         std=[0.5, 0.5, 0.5])
])

# Prediction function
def predict(img):
    img_tensor = transform(img).unsqueeze(0).to(device)
    with torch.no_grad():
        outputs = model(img_tensor)
        probs = torch.softmax(outputs, dim=1)
        return {class_names[i]: float(probs[0][i]) for i in range(len(class_names))}

# Gradio interface
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=gr.Label(num_top_classes=2, label="Prediction"),
    title="🐱🐶 Cat vs Dog Classifier",
    description="Upload a picture of a cat or a dog. Model was trained with RandomRotation and ColorJitter on all images.",
)

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