app.py

import gradio as gr
import numpy as np
import cv2
import tensorflow as tf

# Load the model
vgg16_model = tf.keras.models.load_model('fish_vgg16_model.h5')
mobilenet_model = tf.keras.models.load_model('fish_mobilenet_model.h5')
custom_cnn_model = tf.keras.models.load_model('fish_cnn_model.h5')

# Define the preprocessing function
def preprocess_image(image):
    image = cv2.resize(image, (224, 224))
    image = image / 255.0
    return image

# Define the prediction function
def predict(image):
    # Preprocess the image
    processed_image = preprocess_image(image)
    
    # Make predictions using the models
    vgg_pred = vgg16_model.predict(np.expand_dims(processed_image, axis=0))[0]
    mobilenet_pred = mobilenet_model.predict(np.expand_dims(processed_image, axis=0))[0]
    custom_cnn_pred = custom_cnn_model.predict(np.expand_dims(processed_image, axis=0))[0]
    
    # Get the predicted labels
    vgg_label = np.argmax(vgg_pred)
    mobilenet_label = np.argmax(mobilenet_pred)
    custom_cnn_label = np.argmax(custom_cnn_pred)
    
    label_map = {0: 'Black Sea Sprat', 1: 'Gilt-Head Bream', 2: 'Hourse Mackerel', 
             3: 'Red Mullet', 4: 'Red Sea Bream', 5: 'Sea Bass', 
             6: 'Shrimp', 7: 'Striped Red Mullet', 8: 'Trout'}
    
    label1 = label_map[custom_cnn_label]
    label2 = label_map[vgg_label]
    label3 = label_map[mobilenet_label]
    
    return label1, label2, label3


# Create the Gradio interface
inputs = gr.components.Image()
outputs=[
        gr.components.Textbox(label="Custom CNN Model Label"),
        gr.components.Textbox(label="VGG16 Model Label"),
        gr.components.Textbox(label="MobileNet Label")
    ]

gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title="Fish Classification", theme="dark").launch()