app.py

import gradio as gr
from keras.models import load_model  # TensorFlow is required for Keras to work
from PIL import Image, ImageOps  # Install pillow instead of PIL
import numpy as np

  # Load the model
model = load_model("keras_model.h5", compile=False)

# Load the labels
class_names = open("labels.txt", "r").readlines()

def image_classifier(image):

  # Disable scientific notation for clarity
  np.set_printoptions(suppress=True)

  # 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
  #image = Image.open("/content/drive/My Drive/chishiki/979.png").convert("RGB")
  image = Image.fromarray(image.astype("uint8"),"RGB")

  # resizing the image to be at least 224x224 and then cropping from the center
  size = (224, 224)
  image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)

  # turn the image into a numpy array
  image_array = np.asarray(image)

  # Normalize the image
  normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1

  # Load the image into the array
  data[0] = normalized_image_array

  # Predicts the model
  prediction = model.predict(data)
  index = np.argmax(prediction)
  class_name = class_names[index]
  confidence_score = prediction[0][index]

  # Print prediction and confidence score
  result = class_name[2:]
  if index ==  2:
    text1 ='互角です'
  elif index ==  0:
    text1 ='苦手です'
  elif index ==  1:
    text1 ='有利です'
  ans = result + text1
  return ans

demo = gr.Interface(
    fn=image_classifier,
    inputs=gr.Image(),  # 画像を入力として受け取ります。
    outputs="text"  # 結果をテキストで表示します。
)

demo.launch(debug=True, share=True)