app.py

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

# %%capture
# #Use capture to not show the output of installing the libraries!

pip install tensorflow
pip install gradio

import gradio as gr
import numpy as np
import tensorflow as tf
from transformers import pipeline

# load the model from the Hugging Face Model Hub
model = pipeline('image-classification', model='image_classification/densenet')


#model = tf.keras.models.load_model('/content/drive/MyDrive/project_image_2023_NO/saved_models/saved_model/densenet')
#labels = ['Healthy', 'Patient']
labels = {0: 'healthy', 1: 'patient'}

def classify_image(inp):
  inp = inp.reshape((-1, 224, 224, 3))
  inp = tf.keras.applications.densenet.preprocess_input(inp)
  prediction = model.predict(inp)
  confidences = {labels[i]: float(prediction[0][i]) for i in range(2)}
  return confidences


gr.Interface(fn=classify_image, 
             inputs=gr.Image(shape=(224, 224)),
             outputs=gr.Label(num_top_classes = 2),
             title="Demo",
             description="Here's a sample image classification. Enjoy!",
             examples=[['path/to/example/image.jpg']]
             ).launch(share = True)