app.py

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

# Load the trained model
model_path = "model_scripted.pt"
net = torch.jit.load(model_path)
net.eval()

# Define the target classes (update this to match your model's classes)
target_classes = ["Rope", "Hammer", "Other"]

# Define a prediction function
def classify_image(image):
    transform = transforms.Compose([
        transforms.Resize((128, 128)),
        transforms.ToTensor(),
        transforms.Normalize((0.5,), (0.5,))
    ])
    image = transform(image).unsqueeze(0)  # Add batch dimension
    with torch.no_grad():
        output = net(image)
        _, predicted = torch.max(output, 1)
        return target_classes[predicted.item()]

# Create the Gradio interface
interface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Mechanical Tools Classifier"
)

# Launch the app
interface.launch()