app.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms, models
from PIL import Image, UnidentifiedImageError
import streamlit as st
import numpy as np
import requests
from io import BytesIO
from kan_linear import KANLinear
import logging
import os

# Setup logging
logging.basicConfig(level=logging.INFO)

# Define the model
class KANVGG16(nn.Module):
    def __init__(self, num_classes=1):  # For binary classification (cats and dogs)
        super(KANVGG16, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(64),  # Added Batch Normalization

            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(128),  # Added Batch Normalization

            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(256),  # Added Batch Normalization

            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(512),  # Added Batch Normalization

            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(512),  # Added Batch Normalization
        )
        self.classifier = nn.Sequential(
            KANLinear(512 * 7 * 7, 2048),  # Adjusted for input size 224x224
            nn.ReLU(inplace=True),
            nn.Dropout(0.5),  # Increased Dropout
            KANLinear(2048, 2048),
            nn.ReLU(inplace=True),
            nn.Dropout(0.5),  # Increased Dropout
            KANLinear(2048, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

def load_model(weights_path, device):
    model = KANVGG16().to(device)
    state_dict = torch.load(weights_path, map_location=device)
    
    # Remove 'module.' prefix from keys
    new_state_dict = {}
    for k, v in state_dict.items():
        if k.startswith('module.'):
            new_state_dict[k[len('module.'):]] = v
        else:
            new_state_dict[k] = v

    model.load_state_dict(new_state_dict)
    model.eval()
    return model

class CustomImageLoadingError(Exception):
    """Custom exception for image loading errors"""
    pass

def load_image_from_url(url):
    try:
        logging.info(f"Loading image from URL: {url}")

        # Check the file extension
        valid_extensions = ['jpg', 'jpeg', 'png', 'webp']
        file_extension = os.path.splitext(url)[1][1:].lower()
        if file_extension not in valid_extensions:
            raise CustomImageLoadingError(f"URL does not point to an image with a valid extension: {file_extension}")

        response = requests.get(url)
        response.raise_for_status()  # Check if the request was successful

        content_type = response.headers['Content-Type']
        logging.info(f"Content-Type: {content_type}")

        # Check if the content type is an image
        if 'image' not in content_type:
            raise CustomImageLoadingError(f"URL does not point to an image: {content_type}")

        img = Image.open(BytesIO(response.content)).convert('RGB')
        logging.info("Image successfully loaded and converted to RGB")
        return img
    except requests.HTTPError as e:
        logging.error(f"HTTPError while loading image: {e}")
        raise CustomImageLoadingError(f"Error loading image from URL: {e}")
    except UnidentifiedImageError as e:
        logging.error(f"UnidentifiedImageError while loading image: {e}")
        raise CustomImageLoadingError(f"Cannot identify image file: {e}")
    except requests.RequestException as e:
        logging.error(f"RequestException while loading image: {e}")
        raise CustomImageLoadingError(f"Error loading image from URL: {e}")
    except Exception as e:
        logging.error(f"Unexpected error while loading image: {e}")
        raise CustomImageLoadingError(f"Error loading image from URL: {e}")

def preprocess_image(image):
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor()
    ])
    return transform(image).unsqueeze(0)

# Streamlit app
st.title("Cat and Dog Classification with VGG16-KAN")

st.sidebar.title("Upload Images")
uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png", "webp"])
image_url = st.sidebar.text_input("Or enter image URL...")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = load_model('weights/best_model_VGG16_KAN_97.pth', device)

img = None

if uploaded_file is not None:
    logging.info("Image uploaded via file uploader")
    img = Image.open(uploaded_file).convert('RGB')
elif image_url:
    try:
        img = load_image_from_url(image_url)
    except CustomImageLoadingError as e:
        st.sidebar.error(str(e))
    except Exception as e:
        st.sidebar.error(f"Unexpected error: {e}")

st.sidebar.write("-----")

# Define your information for the footer
name = "Wayan Dadang"

st.sidebar.write("Follow me on:")
# Create a footer section with links and copyright information
st.sidebar.markdown(f"""
    [LinkedIn](https://www.linkedin.com/in/wayan-dadang-801757116/)
    [GitHub](https://github.com/Wayan123)
    [Resume](https://wayan123.github.io/)
    © {name} - {2024}
    """, unsafe_allow_html=True)

if img is not None:
    st.image(np.array(img), caption='Uploaded Image.', use_column_width=True)
    if st.button('Predict'):
        img_tensor = preprocess_image(img).to(device)

        with torch.no_grad():
            output = model(img_tensor)
            prob = torch.sigmoid(output).item()

        st.write(f"Prediction: {prob:.4f}")

        if prob < 0.5:
            st.write("This image is classified as a Cat.")
        else:
            st.write("This image is classified as a Dog.")