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import streamlit as st
import os
from PIL import Image
import torch
from torchvision import transforms
from models.cnn import CNNModel
from utils.transforms import get_transforms
os.environ["STREAMLIT_ROOT"] = "/tmp/.streamlit"
@st.cache_resource
def load_model(model_path='saved_models/cnn_model.pth'):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint = torch.load(model_path, map_location=device)
class_names = checkpoint['class_names']
model = CNNModel(num_classes=len(class_names))
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
return model, class_names, device
st.title("📸 Intel Image Classification")
st.write("Upload an image to classify it into one of the image categories: buildings, forest, glacier, mountain, sea, or street.")
model, class_names, device = load_model()
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file:
image = Image.open(uploaded_file).convert("RGB")
st.image(image, caption="Uploaded Image", use_container_width=True)
transform = get_transforms(train=False)
image_tensor = transform(image).unsqueeze(0).to(device)
with torch.no_grad():
output = model(image_tensor)
predicted_idx = torch.argmax(output, 1).item()
predicted_class = class_names[predicted_idx]
st.success(f"Predicted class: {predicted_class}")
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