src/streamlit_app.py

import numpy as np
import streamlit as st
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as T
from PIL import Image

st.set_page_config(page_title="Garbage Classification")


# CNN Model Definition
class SimpleCNN(nn.Module):
    def __init__(self, num_classes, input_channels=3):
        super().__init__()

        # Convolutional layers
        self.conv1 = nn.Conv2d(input_channels, 32, kernel_size=3, padding=0)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=0)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=0)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=0)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.flatten = nn.Flatten()

        # Dense layers
        self.fc1 = nn.Linear(256 * 12 * 12, 512)
        self.dropout1 = nn.Dropout(0.5)

        self.fc2 = nn.Linear(512, 512)
        self.dropout2 = nn.Dropout(0.5)

        self.fc3 = nn.Linear(512, num_classes)

    def forward(self, x):
        # Conv blocks
        x = F.relu(self.conv1(x))
        x = self.pool1(x)

        x = F.relu(self.conv2(x))
        x = self.pool2(x)

        x = F.relu(self.conv3(x))
        x = self.pool3(x)

        x = F.relu(self.conv4(x))
        x = self.pool4(x)

        # Dense layers
        x = self.flatten(x)
        x = F.relu(self.fc1(x))
        x = self.dropout1(x)

        x = F.relu(self.fc2(x))
        x = self.dropout2(x)

        x = self.fc3(x)
        return x


# Class names
CLASS_NAMES = [
    "battery",
    "biological",
    "cardboard",
    "clothes",
    "glass",
    "metal",
    "paper",
    "plastic",
    "shoes",
    "trash",
]


# Cache the model loading
@st.cache_resource
def load_model():
    """Load the trained model"""
    device = torch.device("cpu")
    model = SimpleCNN(num_classes=10)
    model = nn.DataParallel(model)

    try:
        model.load_state_dict(torch.load("best_model.pth", map_location=device))
        model.eval()
        return model, device
    except Exception as e:
        st.error(f"Error loading model: {e}")
        return None, device


def preprocess_image(image):
    """Preprocess uploaded image"""
    transform = T.Compose(
        [
            T.Resize(224),
            T.CenterCrop(224),
            T.ToTensor(),
            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ]
    )

    image_tensor = transform(image).unsqueeze(0)
    return image_tensor


def predict_image(image, model, device):
    """Make prediction on image"""
    # Preprocess image
    input_tensor = preprocess_image(image).to(device)

    # Make prediction
    with torch.no_grad():
        outputs = model(input_tensor)
        probabilities = F.softmax(outputs, dim=1)
        confidence, predicted_idx = torch.max(probabilities, 1)

    predicted_class = CLASS_NAMES[predicted_idx.item()]
    confidence_score = confidence.item()
    all_probabilities = probabilities.cpu().numpy().flatten()

    return predicted_class, confidence_score, all_probabilities


def get_confidence_color(confidence):
    """Get color class based on confidence score"""
    if confidence >= 0.7:
        return "confidence-high"
    elif confidence >= 0.4:
        return "confidence-medium"
    else:
        return "confidence-low"


def main():
    # Load model
    model, device = load_model()

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

    if uploaded_file is not None:
        # Display uploaded image
        image = Image.open(uploaded_file).convert("RGB")

        col1, col2 = st.columns([1, 1])
        with col1:
            st.image(image, caption="Uploaded Image", use_container_width=True)

        # Make prediction
        with st.spinner("🔍 Analyzing image..."):
            predicted_class, confidence, probabilities = predict_image(
                image, model, device
            )

        sorted_indices = np.argsort(probabilities)[::-1]

        container = col2.container(border=True)
        for i, idx in enumerate(sorted_indices):
            class_name = CLASS_NAMES[idx]
            prob = probabilities[idx]
            container.write(f"{class_name.title()}: {prob:.1%}")


if __name__ == "__main__":
    main()