app.py

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

# Load trained model
checkpoint = torch.load("animal_model.pth", map_location="cpu")
class_names = checkpoint["class_names"]

# Define model architecture
model = models.resnet50(weights=None)  # same as trained
model.fc = torch.nn.Linear(model.fc.in_features, len(class_names))
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()

# Image preprocessing
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# Prediction function
def predict(image):
    img = Image.fromarray(image).convert("RGB")
    img = transform(img).unsqueeze(0)  # add batch dimension

    with torch.no_grad():
        outputs = model(img)
        _, pred = torch.max(outputs, 1)

    return class_names[pred.item()]

# Gradio Interface
app = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="numpy"),
    outputs="text",
    title="Animal Image Classifier",
    description="Upload an image of an animal and the model will classify it."
)

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