# -*- coding: utf-8 -*-


import gradio as gr
import numpy as np
import tensorflow_hub as hub
from tensorflow.keras.models import load_model
import cv2

# Define a dictionary to map the custom layer to its implementation
custom_objects = {'KerasLayer': hub.KerasLayer}

# Load your model (ensure the path is correct)
model = load_model('bird_model4.h5', custom_objects=custom_objects)

# Define your class labels or categories for predictions
train_info = []  # Replace with your actual class labels

# Read image names from the text file
with open('labelwithspace.txt', 'r') as file:
    train_info = [line.strip() for line in file.read().splitlines()]


def predict_image(image):
    img = cv2.resize(image, (224, 224))
    img = img / 255.0
    predictions = model.predict(img[np.newaxis, ...])[0]
    top_classes = np.argsort(predictions)[-3:][::-1]
    top_class = top_classes[0]  # Get the index of the top prediction
    label = train_info[top_class]  # Use the index to retrieve the label
    return label


# Define Gradio interface
input_image = gr.inputs.Image(shape=(224, 224))
output_label = gr.outputs.Label()

gr.Interface(fn=predict_image, inputs=input_image, outputs=output_label, capture_session=True).launch()