Update app.py

app.py

@@ -1,86 +1,87 @@
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-import cv2
-from PIL import Image
-from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
-
-# Load the model
-model = tf.keras.models.load_model('DiabeticModel.keras')
-
-# Define class labels
-class_labels = ['No DR', '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
-
-# 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:
-    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=True)
-        
-        # Preprocess the image
-        img_array = preprocess_image(image)
-        
-        # Make prediction
-        predictions = model.predict([img_array, img_array])[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 predictions
-        st.write(f"### Predicted Level: **{predicted_class}**")
-        
-        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
-        
-        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]
-            
-            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}**")
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+import cv2
+from PIL import Image
+from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
+
+# Load the model
+model = tf.keras.models.load_model('DiabeticModel.keras')
+
+# Define class labels
+class_labels = ['No DR', '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
+
+# 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:
+    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)
+        
+        # Make prediction
+        predictions = model.predict([img_array, img_array])[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]
+        st.write("Classifying...")
+        
+        # Display the predictions
+        st.write(f"### Predicted Level: **{predicted_class}**")
+        
+        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
+        
+        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]
+            
+            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}**")