app.py

import streamlit as st
from PIL import Image
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.models as models
import numpy as np

# Set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load the main classifier (Main_Classifier_best_model.pth)
main_model = models.resnet18(pretrained=False)
num_ftrs = main_model.fc.in_features
main_model.fc = nn.Linear(num_ftrs, 3)  # 3 classes: Soda drinks, Clothing, Mobile Phones
main_model.load_state_dict(torch.load('Main_Classifier_best_model.pth', map_location=device))
main_model = main_model.to(device)
main_model.eval()

# Define class names for the main classifier based on folder structure
main_class_names = ['Clothing', 'Mobile Phones', 'Soda drinks']

# Sub-classifier models
def load_soda_drinks_model():
    model = models.resnet18(pretrained=False)
    num_ftrs = model.fc.in_features
    model.fc = nn.Linear(num_ftrs, 3)  # 3 classes: Miranda, Pepsi, Seven Up
    model.load_state_dict(torch.load('Soda_drinks_best_model.pth', map_location=device))
    model = model.to(device)
    model.eval()
    return model

def load_clothing_model():
    model = models.resnet18(pretrained=False)
    num_ftrs = model.fc.in_features
    model.fc = nn.Linear(num_ftrs, 2)  # 2 classes: Pants, T-Shirt
    model.load_state_dict(torch.load('Clothes_best_model.pth', map_location=device))
    model = model.to(device)
    model.eval()
    return model

def load_mobile_phones_model():
    model = models.resnet18(pretrained=False)
    num_ftrs = model.fc.in_features
    model.fc = nn.Linear(num_ftrs, 2)  # 2 classes: Apple, Samsung
    model.load_state_dict(torch.load('Phone_best_model.pth', map_location=device))
    model = model.to(device)
    model.eval()
    return model

def convert_to_rgb(image):
    """
    Converts 'P' mode images with transparency to 'RGBA', and then to 'RGB'.
    This is to avoid transparency issues during model training.
    """
    if image.mode in ('P', 'RGBA'):
        return image.convert('RGB')
    return image

# Define preprocessing transformations (same used during training)
preprocess = transforms.Compose([
    transforms.Lambda(convert_to_rgb),
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  # ImageNet normalization
])

# Streamlit App Interface
st.title("Main Classifier and Sub-Classifier System")
st.write("Upload an image to classify whether it belongs to Clothing, Mobile Phones, or Soda Drinks. Based on the prediction, it will further classify within the subcategory.")

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

if uploaded_file is not None:
    # Open the image using PIL
    image = Image.open(uploaded_file)

    # Display the uploaded image
    st.image(image, caption='Uploaded Image', use_column_width=True)
    st.write("")
    st.write("Classifying...")

    # Preprocess the image
    input_image = preprocess(image).unsqueeze(0).to(device)

    # Perform inference with the main classifier
    with torch.no_grad():
        output = main_model(input_image)
        probabilities = torch.nn.functional.softmax(output[0], dim=0)
        confidence, predicted_class = torch.max(probabilities, 0)

    # Display the main classifier result
    main_prediction = main_class_names[predicted_class]
    st.write(f"**Main Predicted Class:** {main_prediction}")
    st.write(f"**Confidence:** {confidence.item():.4f}")

    # Load and apply the sub-classifier based on the main classification
    if main_prediction == 'Soda drinks':
        st.write("Loading Soda Drinks Model...")
        soda_model = load_soda_drinks_model()
        sub_class_names = ['Miranda', 'Pepsi', 'Seven Up']
    elif main_prediction == 'Clothing':
        st.write("Loading Clothing Model...")
        clothing_model = load_clothing_model()
        sub_class_names = ['Pants', 'T-Shirt']
    elif main_prediction == 'Mobile Phones':
        st.write("Loading Mobile Phones Model...")
        phones_model = load_mobile_phones_model()
        sub_class_names = ['Apple', 'Samsung']

    # Perform inference with the sub-classifier
    with torch.no_grad():
        if main_prediction == 'Soda drinks':
            sub_output = soda_model(input_image)
        elif main_prediction == 'Clothing':
            sub_output = clothing_model(input_image)
        elif main_prediction == 'Mobile Phones':
            sub_output = phones_model(input_image)
        
        sub_probabilities = torch.nn.functional.softmax(sub_output[0], dim=0)
        sub_confidence, sub_predicted_class = torch.max(sub_probabilities, 0)

    # Display the sub-classifier result
    st.write(f"**Sub Predicted Class:** {sub_class_names[sub_predicted_class]}")
    st.write(f"**Confidence:** {sub_confidence.item():.4f}")