Upload app1.py

app1.py

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+import streamlit as st
+import streamlit.web.cli as stcli
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+IMAGE_SIZE = 256
+
+# Load the saved model
+model = tf.keras.models.load_model('my_model.h5')
+
+# Define class labels (adjust this according to your specific classes)
+class_labels = ['Mild', 'Moderate', 'No_DR', 'Proliferate_DR', 'Severe']
+
+
+def predict(image):
+    # Preprocess the image to the required size and scale
+    image = tf.image.resize(image, (IMAGE_SIZE, IMAGE_SIZE))
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
+    # Make prediction
+    predictions = model.predict(image)
+    confidence = np.max(predictions)
+    predicted_class = class_labels[np.argmax(predictions)]
+
+    return predicted_class, float(confidence)
+
+
+# Create the Streamlit interface
+st.title("Early Diabetic Retinopathy Detection")
+st.write("Upload an image and get the predicted class along with confidence score.")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    predicted_class, confidence = predict(image)
+
+    st.write(f"Predicted Class: {predicted_class}")
+    st.write(f"Confidence: {confidence:.2f}")