app.py

import torch
import torchvision
import torch.nn as nn
import torchvision.transforms as transforms

model = torchvision.models.resnet50(pretrained=True)
model.fc = nn.Linear(model.fc.in_features, 2)
model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')))
model.eval()
device = torch.device("cpu")

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])
])

classes = ['Fruta própria para o consumo', 'Fruta imprópria para o consumo']

import gradio as gr
from PIL import Image

# Define the function to make predictions
def predict(image):
    image = transform(image).unsqueeze(0).to(device)
    model.eval()
    with torch.no_grad():
        output = model(image)
        _, predicted = torch.max(output.data, 1)
        return classes[predicted.item()]

# Define the input and output components
image_input = gr.inputs.Image(type="pil", label="Upload Image")
label_output = gr.outputs.Label()

# Create the interface
interface = gr.Interface(fn=predict, inputs=image_input, outputs=label_output)

# Launch the interface
interface.launch()