app.py

import streamlit as st
import torch
import torchvision.transforms as transforms
from torchvision.models import resnet50
from PIL import Image
import requests
from io import BytesIO

# Load the pre-trained ResNet-50 model
model = resnet50(pretrained=True)
model.eval()

# Define the image transforms
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 label map for ImageNet classes
LABELS_URL = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json"
response = requests.get(LABELS_URL)
labels = response.json()

# Streamlit UI
st.title("Image Classification with Pre-trained ResNet-50")
st.write("Upload an image and the model will predict the class of the object in the image.")

# File uploader
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Open the image file
    image = Image.open(uploaded_file)
    
    # Display the image
    st.image(image, caption='Uploaded Image', use_column_width=True)
    st.write("")
    st.write("Classifying...")
    
    # Preprocess the image
    image = transform(image).unsqueeze(0)
    
    # Predict the class
    with torch.no_grad():
        outputs = model(image)
    
    # Get the predicted class
    _, predicted = torch.max(outputs, 1)
    predicted_class = labels[predicted.item()]
    
    # Display the result
    st.write(f"Predicted Class: {predicted_class}")