app.py

import gradio as gr
import torch
from torchvision.transforms import ToTensor
from torchvision.models import resnet50
from PIL import Image
import torch.nn as nn

# Load your PyTorch model
model = resnet50(pretrained=False)
model.fc = nn.Linear(model.fc.in_features, 2)
model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')))

classes = ['bom', 'ruim']

# Define the function for image classification
def classify_image(image):
    image_tensor = ToTensor()(image).unsqueeze(0)

    # Perform inference using your PyTorch model
    with torch.no_grad():
        model.eval()
        outputs = model(image_tensor)

        _, predicted = torch.max(outputs.data, 1)
        return classes[predicted.item()]


# Define the Gradio interface
inputs = gr.Image()
outputs = gr.Label(num_top_classes=1)

interface = gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs)

interface.launch(debug=True)