app.py

import streamlit as st
import torch
from torchvision import models, transforms
from PIL import Image

# Load a pre-trained ResNet model
model = models.resnet50(pretrained=True)
model.eval()

# Define the transformations for the input 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]),
])

def classify_image(image):
    # Preprocess the input image
    image = transform(image)
    image = image.unsqueeze(0)  # Add batch dimension

    # Make a prediction
    with torch.no_grad():
        output = model(image)

    # Get the predicted class
    _, predicted_class = torch.max(output, 1)

    return predicted_class.item()

def main():
    st.title("Image Classification with PyTorch and Streamlit")

    uploaded_file = st.file_uploader("Choose a file", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        # Display the uploaded image
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded Image.", use_column_width=True)

        # Make a prediction
        class_idx = classify_image(image)

        # Display the result
        class_label = str(class_idx)
        st.write("Class Prediction: ", class_label)

if __name__ == "__main__":
    main()