Update app.py

app.py

@@ -3,13 +3,14 @@ import tensorflow as tf
 import numpy as np
 import cv2
 from PIL import Image
-from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
+from fpdf import FPDF
+from io import BytesIO
 
 # Load the model
-model = tf.keras.models.load_model('DiabeticModel.keras')
+model = tf.keras.models.load_model(r'DiabeticModel.keras')
 
 # Define class labels
-class_labels = ['No DR', 'Mild DR', 'Moderate DR', 'Severe DR', 'Proliferative DR']
+class_labels = ['Healthy', 'Mild DR', 'Moderate DR', 'Severe DR', 'Proliferative DR']
 
 # Function to preprocess the uploaded image
 def preprocess_image(image: Image.Image):
@@ -19,6 +20,27 @@ def preprocess_image(image: Image.Image):
     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.")
@@ -27,17 +49,24 @@ st.write("Welcome to our Diabetic Retinopathy Detection App! This app utilizes d
 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)
-        st.image(image, caption='Uploaded Image', use_column_width=200)
         
         # 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, img_array])[0]
+        predictions = model.predict(img_array_pair)[0]
         
         # Convert predictions to percentages
         prediction_percentages = predictions * 100
@@ -45,43 +74,30 @@ with tab1:
         # Find the class with the highest probability
         highest_index = np.argmax(prediction_percentages)
         predicted_class = class_labels[highest_index]
-        st.write("Classifying...")
         
-        # Display the predictions
-        st.write(f"### Predicted Level: **{predicted_class}**")
+        # Display the image and predictions side by side
+        col1, col2 = st.columns([1, 2])  # Set the width ratio between columns
         
-        st.write("### Prediction Results")
-        for i, label in enumerate(class_labels):
-            progress_bar = st.progress(int(prediction_percentages[i]))
-            st.write(f"{label}: {prediction_percentages[i]:.2f}%")
-
-with tab2:
-    st.write("Use your webcam to capture an image for prediction.")
-    
-    # Define a custom video transformer for Streamlit WebRTC
-    class VideoTransformer(VideoTransformerBase):
-        def __init__(self):
-            self.result = None
+        # Display image in the first column with limited width
+        with col1:
+            st.image(image, caption='Uploaded Image', width=150)
         
-        def transform(self, frame):
-            img = frame.to_ndarray(format="bgr24")
-            img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            image = Image.fromarray(img_rgb)
-            
-            # Preprocess the image
-            img_array = preprocess_image(image)
-            
-            # Make prediction
-            predictions = model.predict([img_array, img_array])[0]
-            prediction_percentages = predictions * 100
-            highest_index = np.argmax(prediction_percentages)
-            self.result = class_labels[highest_index]
+        # Display the predictions in the second column
+        with col2:
+            st.write(f"### Predicted Level: **{predicted_class}**")
             
-            return cv2.putText(
-                img, f"Prediction: {self.result}", (10, 30),
-                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA
-            )
-
-    webrtc_ctx = webrtc_streamer(key="example", video_transformer_factory=VideoTransformer)
-    if webrtc_ctx.video_transformer:
-        st.write(f"### Predicted Level: **{webrtc_ctx.video_transformer.result}**")
+            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'
+        )