Create app.py

app.py

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+import numpy as np
+import tensorflow as tf
+import cv2
+import gradio as gr
+from tensorflow import keras
+
+model = keras.models.load_model('model_InceptionV3.h5')
+
+class_mapping = {1: 'Собака', 2: 'Кінь', 3: 'Слон', 4:'Метелик',
+                 5: 'Курка', 6: 'Кіт', 7:'Корова', 8: 'Вівця',
+                 9: 'Павук', 10: 'Білка'
+                }
+
+  # Створення функції для передбачення тварини
+def predict_image(image):
+    image = cv2.resize(image, (224, 224))
+    image = np.asarray(image)
+    image = image.astype('float32') / 255.0
+    predictions = model.predict(np.expand_dims(image, axis=0))[0]
+    prediction = {}
+    for index, probability in enumerate(predictions) :
+      prediction[class_mapping[index+1]] = float(round(probability, 3))
+      print(prediction)
+    return prediction
+
+demo = gr.Blocks()
+# Створення інтерфейсу Gradio
+with demo:
+    gr.Markdown("What animal is in the picture")
+    with gr.Tab("Predict image"):
+        image_input = gr.Image(label="Upload image")
+        output = gr.Label(label="Animal predicted by neural network")
+        image_button = gr.Button("Predict")
+        image_button.click(predict_image, inputs=image_input, outputs=output)
+
+demo.launch()