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Classify_product.py

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+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('Saved Model\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('Saved Model\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('Saved Model\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('Saved Model\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}")

Clothes_best_model.pth

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+oid sha256:eddc1323a0b208ebb6dda79a8fe273e95ab0430ce6ab095be71849cf0b9bb010
+size 44791416

Main_Classifier_best_model.pth

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+oid sha256:e34b538419ac07e0f4f8182b5ae479eb6cc2ef77332fe444f6a05866d1eb9e56
+size 44791416

Phone_best_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b16817f04fbec8c2367c0dcc98b40572f6d56ad8246d35ba116316fea40fd8b8
+size 44789368

Soda_drinks_best_model.pth

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+oid sha256:7b8d0686fdf1449a52e9d9dedfd3f1bdbb677aa1c39c91bcfd02ff138784c5f5
+size 44791416

requirements.txt

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+streamlit==1.34.0
+Pillow==10.3.0
+torch==2.4.1
+torchvision==0.19.1
+numpy==1.26.4
+
+
+
+
+

tshirt_pants_classifier.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b06bed9897948c4abc9c5e15d075308c06886ddb79e9a886eb705e132da625c7
+size 85320024