app.py

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

# Force CPU usage
device = torch.device('cpu')

# Load your trained ResNet-50 model
model = models.resnet50(pretrained=False)  # Load the ResNet-50 architecture
model.load_state_dict(torch.load("model.pth", map_location=device))  # Load the trained weights (.pth)
model.to(device)  # Move model to CPU (even if you have a GPU)

model.eval()  # Set model to evaluation mode

# Define the transformation required for the input image
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Define the labels for ImageNet (or your specific dataset labels)
LABELS = ["class_1", "class_2", "class_3", "class_4", "class_5",  # Replace with your classes
          "class_6", "class_7", "class_8", "class_9", "class_10"]

# Define the prediction function
def predict(image):
    image = Image.open(image).convert("RGB")  # Open the image and convert to RGB
    image = transform(image).unsqueeze(0)  # Apply transformations and add batch dimension

    # Move the image tensor to CPU as well
    image = image.to(device)

    with torch.no_grad():
        outputs = model(image)  # Get model predictions

    _, predicted = torch.max(outputs, 1)  # Get the class with highest probability
    return LABELS[predicted.item()]  # Return the predicted class label

# Set up the Gradio interface
interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(type="pil"), outputs="text")

# Launch the interface
interface.launch()