neater output

app.py

@@ -25,22 +25,11 @@ def predict_plant(path):
     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
+    # find the confidence probability for each plant
     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)
+    confidences = {class_names[i]: float(score[i]) for i in range(len(class_names))}
+    return confidences
 
 # describe the model
 title = "Leaftracker Interactive Model"
@@ -54,7 +43,7 @@ description = """Leaftracker is an image classification model that differentiate
 app = gr.Interface(
     fn=predict_plant,
     inputs=gr.Image(type="filepath"),
-    outputs="text",
+    outputs=gr.Label(num_top_classes=3),
     flagging_options=["incorrect", "other"],
     title=title,
     description=description,