app.py

import gradio as gr
import tensorflow as tf
from PIL import Image, ImageOps  # Install pillow instead of PIL
import numpy as np

#%%

class_names={0:"iyi huylu",1:"melanoma"}


def predict_input_image(img):
  #teachable machine ile eğitip kaydettiğim model
  model = tf.keras.models.load_model('keras_model.h5')
  img_4d=img.reshape(-1,224,224,3)

  prediction=model.predict(img_4d)[0]

    
  return {class_names[i]: float(prediction[i]) for i in range(2)}


image = gr.Image()
label = gr.Label(num_top_classes=4)

gr.Interface(fn=predict_input_image, inputs=image, outputs=label).launch(debug='True')