app.py

import gradio as gr
import numpy as np
import pandas as pd
from tensorflow.keras import models 

import tensorflow as tf

# open categories.txt in read mode
categories = open("categories.txt", "r")
labels = categories.readline().split(";")

model = models.load_model('models/modelnet/best_model.h5')


def predict_image(image):
    image = np.array(image) / 255
    image = np.expand_dims(image, axis=0)

    pred = model.predict(image)

    acc = dict((labels[i], "%.2f" % pred[0][i]) for i in range(len(labels)))
    print(acc)
    return acc


image = gr.inputs.Image(shape=(224, 224), label="Upload Your Image Here")
label = gr.outputs.Label(num_top_classes=len(labels))

samples = ['samples/basking.jpg', 'samples/blacktip.jpg', 'samples/blue.jpg', 'samples/bull.jpg', 'samples/hammerhead.jpg',
        'samples/lemon.jpg', 'samples/mako.jpg', 'samples/nurse.jpg', 'samples/sand tiger.jpg', 'samples/thresher.jpg', 
        'samples/tigre.jpg', 'samples/whale.jpg', 'samples/white.jpg', 'samples/whitetip.jpg']
        
interface = gr.Interface(
    fn=predict_image, 
    inputs=image, 
    outputs=label, 
    capture_session=True, 
    allow_flagging=False, 
    examples=samples
)
interface.launch()