Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow import keras
+
+# load the pre-trained model from the appropriate file path
+def predict_plant(image):
+    model = tf.keras.models.load_model('lukelike1001/saved_model')
+
+    # redefine values from the model
+    img_height = img_width = 180
+    class_names = ['bear_oak', 'boxelder', 'eastern_poison_ivy',
+                   'eastern_poison_oak', 'fragrant_sumac',
+                   'jack_in_the_pulpit', 'poison_sumac',
+                   'virginia_creeper', 'western_poison_ivy',
+                   'western_poison_oak']
+    path = image
+
+    # load the image into a variable
+    img = tf.keras.utils.load_img(
+        path, target_size=(img_height, img_width)
+    )
+
+    # convert the image into a tensor and create a batch for testing
+    img_array = tf.keras.utils.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0)
+
+    # find the top three likeliest plants based on probabilities
+    predictions = model.predict(img_array)
+    score = tf.nn.softmax(predictions[0])
+    top_three = np.array(score).argsort()[-3:][::-1]
+    numpy_array = score.numpy()
+
+    # convert the folder names into English words then return the three likeliest probabilities
+    output = []
+    for i in top_three:
+        words = class_names[i].split("_")
+        name = " ".join([word.capitalize() for word in words])
+        output.append(
+            "This image likely belongs to {} with {:.2f}% confidence."
+            .format(name, 100 * numpy_array[i])
+        )
+    return "\n".join(output)
+
+# give the model a name
+title = "Leaftracker Demonstration"
+
+app = gr.Interface(
+    fn=predict_plant,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    flagging_options=["incorrect", "other"],
+)
+app.launch()