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| import streamlit as st | |
| from PIL import Image | |
| import tensorflow as tf | |
| import cv2 | |
| import numpy as np | |
| # Load the model | |
| try: | |
| model = tf.keras.models.load_model('FINAL_MODEL.keras') | |
| except FileNotFoundError: | |
| st.error("Disease classification model not found. Please ensure 'FINAL_MODEL.keras' is in the same directory as this app.") | |
| model = None | |
| # Title of the app | |
| st.title("DISEASE DETECTION n CLASSIFICATION") | |
| # Upload an image file | |
| uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
| if uploaded_image is not None and model is not None: | |
| img_opened = Image.open(uploaded_image).convert('RGB') | |
| #image_opened = Image.open(uploaded_image) | |
| image_pred = np.array(img_opened) | |
| image_pred = cv2.resize(image_pred, (150, 150)) | |
| # Convert the image to a numpy array | |
| image_pred = np.array(image_pred) | |
| # Rescale the image (if the model was trained with rescaling) | |
| image_pred = image_pred / 255.0 | |
| # Add an extra dimension to match the input shape (1, 150, 150, 3) | |
| image_pred = np.expand_dims(image_pred, axis=0) | |
| # Print the shape of the preprocessed image | |
| print("Shape of the preprocessed image:", image_pred.shape) | |
| # Predict using the model | |
| prediction = model.predict(image_pred) | |
| # Example prediction output | |
| prediction = np.array(prediction) | |
| #print(f"Prediction_Classes for different types\ncovid: 0\nnormal_chestray: 1\npneumonia: 2") | |
| # Get the predicted class | |
| predicted_ = np.argmax(prediction) | |
| # Display the image | |
| print(f"Predicted array for : {predicted_}") | |
| # Decode the prediction | |
| if predicted_ == 0: | |
| predicted_class= "Covid" | |
| elif predicted_ == 1: | |
| predicted_class= "Normal_chestray" | |
| else: | |
| predicted_class= "Pneumonia" | |
| # Print the predicted class | |
| print(f'The predicted class is: {predicted_class}') | |
| #print(prediction) | |
| st.image(image_pred, caption='Input image by user', use_column_width=True) | |
| st.write("CLasses description for understanding") | |
| st.write("Prediction Classes for different types:") | |
| st.write("COVID: 0") | |
| st.write("Normal Chest X-ray: 1") | |
| st.write("Pneumonia: 2") | |
| st.write("\n") | |
| # Display some text | |
| st.write("DETECTED DISEASE DISPLAY") | |
| st.write(f"Predicted Class : {predicted_}") | |
| st.write(predicted_class) | |
| else: | |
| st.write("Please upload an image file or ensure the disease model is loaded.") | |