app.py

import torch
import gradio as gr
from app.model import PetClassificationModel
from app.backbone import Backbone
from app.config import CFG
from torchvision import transforms

# Load model
backbone = Backbone(CFG.MODEL, len(CFG.idx_to_class), pretrained = CFG.PRETRAINED)
model = PetClassificationModel(base_model = backbone.model, config = CFG)
model.load_state_dict(torch.load('models/best_model.pt'))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Eval mode
model.eval()

model.to(device)


pred_transforms = transforms.Compose([
    transforms.Resize(CFG.IMG_SIZE),
    transforms.ToTensor(),
])

def predict(x):
    x = pred_transforms(x).unsqueeze(0) # transform and batched
    x = x.to(device)
    
    with torch.no_grad():
        prediction = torch.nn.functional.softmax(model(x)[0], dim=0)
        confidences = {CFG.idx_to_class[i]: float(prediction[i]) for i in range(len(CFG.idx_to_class))}
        
    return confidences

gr.Interface(fn=predict,
             title = "Breed Classifier 🐶🧡🐱",
             description = "Clasifica una imagen entre: 120 razas, gato o ninguno!",
             inputs=gr.Image(type="pil"),
             outputs=gr.Label(num_top_classes=5),
             examples=["statics/pug.jpg", "statics/poodle.jpg", "statics/cat.jpg", "statics/no.jpg"]).launch()