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| import streamlit as st | |
| import tensorflow as tf | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| from fpdf import FPDF | |
| from io import BytesIO | |
| # Load the model | |
| model = tf.keras.models.load_model(r'DiabeticModel.keras') | |
| # Define class labels | |
| class_labels = ['Healthy', 'Mild DR', 'Moderate DR', 'Severe DR', 'Proliferative DR'] | |
| # Function to preprocess the uploaded image | |
| def preprocess_image(image: Image.Image): | |
| img_array = np.array(image) | |
| img_array = cv2.resize(img_array, (224, 224)) | |
| img_array = img_array / 255.0 # Normalize to [0, 1] | |
| img_array = np.expand_dims(img_array, axis=0) # Add batch dimension | |
| return img_array | |
| # Function to create PDF report | |
| def create_pdf(patient_name, patient_age, predicted_class, prediction_percentages): | |
| pdf = FPDF() | |
| pdf.add_page() | |
| pdf.set_font("Arial", size=12) | |
| pdf.cell(200, 10, txt="Diabetic Retinopathy Detection Report", ln=True, align='C') | |
| pdf.cell(200, 10, txt=f"Patient Name: {patient_name}", ln=True, align='L') | |
| pdf.cell(200, 10, txt=f"Patient Age: {patient_age}", ln=True, align='L') | |
| pdf.cell(200, 10, txt=f"Predicted Level: {predicted_class}", ln=True, align='L') | |
| # Add the DR level prediction details | |
| for i, label in enumerate(class_labels): | |
| pdf.cell(200, 10, txt=f"{label}: {prediction_percentages[i]:.2f}%", ln=True, align='L') | |
| # Generate PDF content as a byte string (dest='S' for return as string) | |
| pdf_content = pdf.output(dest='S').encode('latin1') # Encoding to 'latin1' ensures compatibility with PDF format | |
| # Convert the byte string to BytesIO object | |
| pdf_output = BytesIO(pdf_content) | |
| return pdf_output | |
| # Streamlit interface | |
| st.title("Diabetic Retinopathy Detection App") | |
| st.write("Welcome to our Diabetic Retinopathy Detection App! This app utilizes deep learning models to detect diabetic retinopathy in retinal images. Diabetic retinopathy is a common complication of diabetes and early detection is crucial for effective treatment.") | |
| # Create tabs for image upload and camera input | |
| tab1, tab2 = st.tabs(["📁 Upload Image", "📷 Use Camera"]) | |
| with tab1: | |
| # Patient details input | |
| st.write("### Enter Patient Details") | |
| patient_name = st.text_input("Patient Name") | |
| patient_age = st.number_input("Patient Age", min_value=0) | |
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| # Open and display the uploaded image | |
| image = Image.open(uploaded_file) | |
| # Preprocess the image | |
| img_array = preprocess_image(image) | |
| # Prepare inputs for model | |
| img_array_pair = [img_array, img_array] # Model expects two inputs | |
| # Make prediction | |
| predictions = model.predict(img_array_pair)[0] | |
| # Convert predictions to percentages | |
| prediction_percentages = predictions * 100 | |
| # Find the class with the highest probability | |
| highest_index = np.argmax(prediction_percentages) | |
| predicted_class = class_labels[highest_index] | |
| # Display the image and predictions side by side | |
| col1, col2 = st.columns([1, 2]) # Set the width ratio between columns | |
| # Display image in the first column with limited width | |
| with col1: | |
| st.image(image, caption='Uploaded Image', width=150) | |
| # Display the predictions in the second column | |
| with col2: | |
| st.write(f"### Predicted Level: **{predicted_class}**") | |
| st.write("### Prediction Results") | |
| for i, label in enumerate(class_labels): | |
| st.progress(int(prediction_percentages[i])) | |
| st.write(f"{label}: {prediction_percentages[i]:.2f}%") | |
| # Create and download PDF report | |
| pdf_output = create_pdf(patient_name, patient_age, predicted_class, prediction_percentages) | |
| # Button to download the PDF | |
| st.download_button( | |
| label="Download Report", | |
| data=pdf_output, | |
| file_name=f"Diabetic_Retinopathy_Report_{patient_name.replace(' ', '_')}.pdf", | |
| mime='application/octet-stream' | |
| ) | |
| with tab2: | |
| st.write("### Capture an image using your camera") | |
| # Capture image from camera | |
| camera_image = st.camera_input("Take a picture") | |
| if camera_image is not None: | |
| # Open and display the captured image | |
| image = Image.open(camera_image) | |
| # Preprocess the image | |
| img_array = preprocess_image(image) | |
| # Prepare inputs for model | |
| img_array_pair = [img_array, img_array] # Model expects two inputs | |
| # Make prediction | |
| predictions = model.predict(img_array_pair)[0] | |
| # Convert predictions to percentages | |
| prediction_percentages = predictions * 100 | |
| # Find the class with the highest probability | |
| highest_index = np.argmax(prediction_percentages) | |
| predicted_class = class_labels[highest_index] | |
| # Display the image and predictions | |
| col1, col2 = st.columns([1, 2]) # Set the width ratio between columns | |
| # Display image in the first column | |
| with col1: | |
| st.image(image, caption='Captured Image', width=150) | |
| # Display the predictions in the second column | |
| with col2: | |
| st.write(f"### Predicted Level: **{predicted_class}**") | |
| st.write("### Prediction Results") | |
| for i, label in enumerate(class_labels): | |
| st.progress(int(prediction_percentages[i])) | |
| st.write(f"{label}: {prediction_percentages[i]:.2f}%") | |
| # Create and download PDF report | |
| pdf_output = create_pdf(patient_name, patient_age, predicted_class, prediction_percentages) | |
| # Button to download the PDF | |
| st.download_button( | |
| label="Download Report", | |
| data=pdf_output, | |
| file_name=f"Diabetic_Retinopathy_Report_{patient_name.replace(' ', '_')}.pdf", | |
| mime='application/octet-stream' | |
| ) | |