app.py

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

# 🖥️ Device (CPU for Gradio unless you have GPU setup)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 🔨 Rebuild your model
resnet = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
in_features = resnet.fc.in_features
resnet.fc = nn.Sequential(
    nn.Linear(in_features, 1024),
    nn.ReLU(),
    nn.Dropout(0.5),
    nn.Linear(1024, 3)  # 3 classes: dog, wild, cat
)
resnet = resnet.to(device)

# 📥 Load saved weights
resnet.load_state_dict(torch.load("best_model.pth", map_location=device))
resnet.eval()

# 🖼️ Validation transforms
val_transforms = transforms.Compose([
    transforms.Lambda(lambda img: img.convert("RGB")),  # 🧠 Force 3-channel
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5]*3, std=[0.5]*3)
])

# 🏷️ Class names
class_names = ["dog", "cat", "wild"]

# 🔮 Prediction function
def classify_image(img):
    img = val_transforms(img).unsqueeze(0).to(device)  # Add batch dim & send to device
    with torch.no_grad():
        outputs = resnet(img)
        probs = torch.softmax(outputs, dim=1)
        confidences = probs.squeeze().cpu().tolist()
        predicted_class = class_names[torch.argmax(probs).item()]
        return {class_names[i]: confidences[i] for i in range(len(class_names))}

# 🎨 Gradio Interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=3),
    title="Dog/Wild/Cat Classifier 🐶🐯🐱",
    description="Upload an image to classify it as Dog, Wild Animal, or Cat."
)

iface.launch()