app.py

import gradio as gr

label_dict = {}

f = open('baseline_keras_model_labels.txt', 'r')

lines = f.readlines()
for line in lines:
    index, label_name = line.split(' ')
    label_dict[int(index)] = label_name.strip()
f.close()

from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
import os

# Load the model
model = load_model('baseline_keras_model.h5')

# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1.
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
# Replace this with the path to your image

folder_path = 'x_test'

y_pred = []

def clf(name):

    image = Image.open(fpath)
    size = (224, 224)
    image = ImageOps.fit(image, size, Image.ANTIALIAS)

    #turn the image into a numpy array
    image_array = np.asarray(image)
    # Normalize the image
    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
    # Load the image into the array
    data[0] = normalized_image_array

    # run the inference
    prediction = model.predict(data)
    class_index = np.argmax(prediction)
    
    return label_dict[class_index]

iface = gr.Interface(fn=clf, inputs="image", outputs="text")
iface.launch()