kanthimathi-vr/deeplearningLLJ_iris_recognition_system

1import gradio as gr2import tensorflow as tf3import numpy as np4from PIL import Image5 6# Load the trained model7model = tf.keras.models.load_model("iris_recognition_model.h5")8 9# Create class labels10num_classes = model.output_shape[-1]11class_names = [f"Class {i}" for i in range(num_classes)]12 13def predict_iris(image):14    # Resize image15    img = image.resize((224, 224))16 17    # Convert to array18    img = np.array(img) / 255.019 20    # Convert grayscale to RGB if needed21    if len(img.shape) == 2:22        img = np.stack((img,) * 3, axis=-1)23 24    # Expand dimensions25    img = np.expand_dims(img, axis=0)26 27    # Prediction28    prediction = model.predict(img)29    predicted_class = np.argmax(prediction)30    confidence = np.max(prediction)31 32    return f"Predicted: {class_names[predicted_class]}\nConfidence: {confidence:.2f}"33 34# Gradio interface35demo = gr.Interface(36    fn=predict_iris,37    inputs=gr.Image(type="pil"),38    outputs="text",39    title="Iris Recognition System",40    description="Upload an iris image to identify the class"41)42 43demo.launch()