app.py

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

# 🔹 Load your trained model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
model.fc = nn.Sequential(
    nn.Linear(2048, 512),
    nn.ReLU(),
    nn.Dropout(0.4),
    nn.Linear(512, 15)
)

model.load_state_dict(torch.load("best_model.pth", map_location=device))  # 🟡 Replace with your file path
model.to(device)
model.eval()

# 🔹 Preprocessing (must match training)
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]) # Enclose the transforms in a list


# 🔹 Class labels (update to match your labels)
class_names = [
    'Bear', 'Bird', 'Cat', 'Cow', 'Deer', 'Dog', 'Dolphin',
    'Elephant', 'Giraffe', 'Horse', 'Kangaroo', 'Lion',
    'Panda', 'Tiger', 'Zebra'
]


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

# 🔹 Gradio UI
interface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=5),
    title="Animal Image Classifier",
    description="Upload an image of an animal and get the top predictions!"
)

# 🔹 Launch the app (use share=True in Colab to get a public link)
interface.launch()