update_pipiline

app.py

@@ -1,23 +1,22 @@
 # -*- coding: utf-8 -*-
-"""gradioApp.ipynb
 
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/19rOnZUE7tNaMyAjlhnO4vLKb8mojrf2V
-"""
-
-# Commented out IPython magic to ensure Python compatibility.
 # %%capture
 # #Use capture to not show the output of installing the libraries!
 # !pip install gradio
 
+
 import gradio as gr
 import numpy as np
 import tensorflow as tf
+from transformers import pipeline
 
-model = tf.keras.models.load_model('/content/drive/MyDrive/project_image_2023_NO/saved_models/saved_model/densenet')
-labels = ['Healthy', 'Patient']
+# 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))
@@ -26,9 +25,11 @@ def classify_image(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!",     
+             description="Here's a sample image classification. Enjoy!",
+             examples=[['path/to/example/image.jpg']]
              ).launch(share = True)