app.py

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

# Load your trained model
with torch.no_grad():
    model = torch.load('classifier.pt')

# Define the preprocessing function for the input image
def preprocess(image):
    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]),
    ])
    image = Image.fromarray(image.astype('uint8'), 'RGB')
    image = transform(image)
    return image.unsqueeze(0)

# Define the predict function
def predict(image):
    # Preprocess the image
    input_tensor = preprocess(image)
    
    # Make a prediction
    with torch.no_grad():
        output = model(input_tensor)
    
    # Perform post-processing if needed (e.g., softmax for probabilities)
    # Replace this with your actual post-processing logic
    probabilities = torch.softmax(output, dim=1).squeeze().tolist()
    
    # Map the class indices to class labels
    class_labels = ["Class1", "Class2", "Class3", "Class4"]
    
    # Create a dictionary with class labels and probabilities
    predictions = {label: prob for label, prob in zip(class_labels, probabilities)}
    
    return predictions

# Create the Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(),
    outputs=gr.Label(num_top_classes=4),
    live=True
)

# Launch the Gradio app
iface.launch()