Upload app.py

app.py

@@ -0,0 +1,44 @@
+import os
+
+import gradio as gr
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.layers import *
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+
+def classify_grapevine_leaves(img):
+    categories = ("Healthy", "Powdery Mildew", "Rust")
+
+    # load keras model
+    model = tf.keras.models.load_model("./keras_model/")
+    # load image
+    # img = tf.keras.preprocessing.image.load_img(img, target_size=(360, 360))
+    # convert image to array
+    img = tf.keras.preprocessing.image.img_to_array(img)
+    # add batch dimension
+    img = tf.expand_dims(img, axis=0)
+    # predict
+    prediction = model.predict(img)
+    # get label
+    print(np.argmax(prediction, axis=1))
+    label = categories[prediction.argmax()]
+    # get confidence
+    conf = prediction[0][prediction.argmax()]
+    # return label and confidence
+    return dict(zip(categories, map(float, prediction[0])))
+
+
+exemples = [
+    "./exemples/healthy.jpg",
+    "./exemples/powdery.jpg",
+    "./exemples/rust.jpg",
+]
+
+image = gr.inputs.Image(shape=(360, 360))
+label = gr.outputs.Label()
+app = gr.Interface(
+    fn=classify_grapevine_leaves, inputs=image, outputs=label, examples=exemples
+)
+app.launch(inline=False)