pages/SVM-Model.py

import streamlit as st
import joblib
import cv2
import numpy as np
from PIL import Image, ImageOps
import warnings
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt

# Suppress warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=FutureWarning)

# Streamlit app header
st.title("Cervical Cancer Cell Classification - SVM Model")

st.sidebar.info("Feel free to select other models from the pages above 🙂")

st.sidebar.write("""
### Instructions 🔧
Make sure you have predicted Cytoplasm and Nuclei Mask from U-Net-Model before proceeding. Please upload Cell Image, Cytoplasm Mask, and Nuclei Mask for accurate and precise classification of Cervical Cancel Cell Type
""")

# Function to load the pre-trained model
def load_model():
    model = joblib.load('pages/svm_model.pkl')  # Replace with the correct path to your model
    # Label encoder (should be the one used during training)
    # If saved, load it or recreate it from class folders
    class_folders = [
        "Dyskeratotic",
        "Koilocytotic",
        "Metaplastic",
        "Parabasal",
        "Superficial-Intermediate"
    ]
    label_encoder = LabelEncoder()
    label_encoder.fit(class_folders)
    return model, label_encoder

# Load model with spinner
with st.spinner('Loading model...'):
    svm_model, label_encoder = load_model()

# Function to process uploaded images
def process_uploaded_image(uploaded_file):
    image = Image.open(uploaded_file)
    image = ImageOps.grayscale(image)  # Convert to grayscale
    image = np.array(image)
    return image

# Upload images through Streamlit
st.subheader("Upload Images")
cell_image_file = st.file_uploader("Upload Cell Image", type=['png', 'jpg', 'jpeg'])
cytoplasm_mask_file = st.file_uploader("Upload Cytoplasm Mask", type=['png', 'jpg', 'jpeg'])
nuclei_mask_file = st.file_uploader("Upload Nuclei Mask", type=['png', 'jpg', 'jpeg'])

# Check if all images are uploaded
if cell_image_file and cytoplasm_mask_file and nuclei_mask_file:
    # Display the uploaded images
    st.subheader("Uploaded Images")
    col1, col2, col3 = st.columns(3)
    with col1:
        st.image(cell_image_file, caption="Cell Image", use_column_width=True)
    with col2:
        st.image(cytoplasm_mask_file, caption="Cytoplasm Mask", use_column_width=True)
    with col3:
        st.image(nuclei_mask_file, caption="Nuclei Mask", use_column_width=True)

    # Convert the uploaded files to numpy arrays
    cell_image = process_uploaded_image(cell_image_file)
    cytoplasm_mask = process_uploaded_image(cytoplasm_mask_file)
    nuclei_mask = process_uploaded_image(nuclei_mask_file)

    # Function to make predictions
    def predict_cell_type(svm_model, label_encoder, cell_image, cytoplasm_mask, nuclei_mask):
        cytoplasm_region = cell_image * (cytoplasm_mask / 255)
        nuclei_region = cell_image * (nuclei_mask / 255)

        concatenated_image = np.dstack((cytoplasm_region, nuclei_region))
        flattened_image = concatenated_image.flatten().reshape(1, -1)

        predicted_class = svm_model.predict(flattened_image)
        predicted_label = label_encoder.inverse_transform(predicted_class)[0]

        return predicted_label  # Return the predicted label

    # Make prediction
    predicted_label = predict_cell_type(svm_model, label_encoder, cell_image, cytoplasm_mask, nuclei_mask)

    # Display prediction results
    st.subheader("Prediction Results")
    st.write(f"**Predicted Cell Type:** {predicted_label}")

    # Function to plot the uploaded image with the prediction result
    def plot_uploaded_image(cell_image, cytoplasm_mask, nuclei_mask, predicted_label):
        color_coded_image = np.zeros((cell_image.shape[0], cell_image.shape[1], 3), dtype=np.uint8)
        color_coded_image[:, :, 1] = cytoplasm_mask  # Green for cytoplasm
        color_coded_image[:, :, 0] = nuclei_mask     # Red for nuclei

        plt.imshow(color_coded_image)
        plt.title(f"Predicted: {predicted_label}")
        plt.axis('off')
        st.pyplot(plt)

    # Plot the results
    plot_uploaded_image(cell_image, cytoplasm_mask, nuclei_mask, predicted_label)

else:
    st.write("Please upload all required images.")