app.py

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()