Update app.py

app.py

@@ -1,51 +1,51 @@
-import gradio as gr
-from tensorflow import keras
-import numpy as np
-from PIL import Image
-
-# Load the pre-trained Keras model
-model = keras.models.load_model('c:/Users/neeli/diabet_intern/retino_model.keras')  # Replace with your actual .keras model file path
-
-# Define class names for your model predictions
-class_names = ['Healthy', 'Mild DR', 'Moderate DR', 'Proliferative DR', 'Severe DR']
-
-# Function to provide additional care information based on the predicted condition
-def eye_care_recommendations(predicted_class):
-    recommendations = {
-        'Healthy': 'Your eyes seem healthy. Continue with regular eye check-ups and maintain a balanced diet.',
-        'Mild DR': 'Mild signs of diabetic retinopathy. Ensure strict blood sugar control and regular eye exams.',
-        'Moderate DR': 'Moderate diabetic retinopathy detected. Consult with an ophthalmologist for treatment options.',
-        'Proliferative DR': 'Advanced stage detected. Immediate medical attention is required to prevent further vision loss.',
-        'Severe DR': 'Severe diabetic retinopathy detected. Medical intervention is necessary. Please visit a doctor immediately.'
-    }
-    return recommendations.get(predicted_class, "No recommendation available.")
-
-# Prediction function that processes the image and returns the result and care advice
-def predict(image):
-    # Resize image to the expected size for the model
-    image = image.resize((128, 128))  # Adjust this size based on your model's input size
-    image = np.expand_dims(np.array(image), axis=0)  # Add batch dimension
-
-    # Make a prediction
-    predictions = model.predict(image)
-    predicted_class_index = np.argmax(predictions, axis=1)[0]
-    predicted_class = class_names[predicted_class_index]
-
-    # Get eye care recommendations based on the prediction
-    care_info = eye_care_recommendations(predicted_class)
-
-    # Return the prediction and care information
-    return f"Predicted Condition: {predicted_class}", care_info
-
-# Gradio interface
-interface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),  # Accepts an image as input
-    outputs=[gr.Textbox(label="Prediction"), gr.Textbox(label="Eye Care Recommendations")],  # Output text fields
-    title="Diabetic Retinopathy Prediction",
-    description="Upload a retinal image, and the model will predict the stage of Diabetic Retinopathy. Eye care recommendations will be provided based on the prediction."
-)
-
-# Launch the interface
-if __name__ == "__main__":
-    interface.launch()
+import gradio as gr
+from tensorflow import keras
+import numpy as np
+from PIL import Image
+
+# Load the pre-trained Keras model
+model = keras.models.load_model('retino_model.keras')  
+
+# Define class names for your model predictions
+class_names = ['Healthy', 'Mild DR', 'Moderate DR', 'Proliferative DR', 'Severe DR']
+
+# Function to provide additional care information based on the predicted condition
+def eye_care_recommendations(predicted_class):
+    recommendations = {
+        'Healthy': 'Your eyes seem healthy. Continue with regular eye check-ups and maintain a balanced diet.',
+        'Mild DR': 'Mild signs of diabetic retinopathy. Ensure strict blood sugar control and regular eye exams.',
+        'Moderate DR': 'Moderate diabetic retinopathy detected. Consult with an ophthalmologist for treatment options.',
+        'Proliferative DR': 'Advanced stage detected. Immediate medical attention is required to prevent further vision loss.',
+        'Severe DR': 'Severe diabetic retinopathy detected. Medical intervention is necessary. Please visit a doctor immediately.'
+    }
+    return recommendations.get(predicted_class, "No recommendation available.")
+
+# Prediction function that processes the image and returns the result and care advice
+def predict(image):
+    # Resize image to the expected size for the model
+    image = image.resize((128, 128))  # Adjust this size based on your model's input size
+    image = np.expand_dims(np.array(image), axis=0)  # Add batch dimension
+
+    # Make a prediction
+    predictions = model.predict(image)
+    predicted_class_index = np.argmax(predictions, axis=1)[0]
+    predicted_class = class_names[predicted_class_index]
+
+    # Get eye care recommendations based on the prediction
+    care_info = eye_care_recommendations(predicted_class)
+
+    # Return the prediction and care information
+    return f"Predicted Condition: {predicted_class}", care_info
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),  # Accepts an image as input
+    outputs=[gr.Textbox(label="Prediction"), gr.Textbox(label="Eye Care Recommendations")],  # Output text fields
+    title="Diabetic Retinopathy Prediction",
+    description="Upload a retinal image, and the model will predict the stage of Diabetic Retinopathy. Eye care recommendations will be provided based on the prediction."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()