raw app

app.py

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+# -*- 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
+
+model = tf.keras.models.load_model('/content/drive/MyDrive/project_image_2023_NO/saved_models/saved_model/densenet')
+labels = ['Healthy', '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!",     
+             ).launch(share = True)