update app

app.py

@@ -17,16 +17,11 @@ print("Model downloaded")
 
 def iris(sepal_length, sepal_width, petal_length, petal_width):
     print("Calling function")
-    #     df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]],
     df = pd.DataFrame([[sepal_length, sepal_width, petal_length, petal_width]],
                       columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
     print("Predicting")
     print(df)
-    # 'res' is a list of predictions returned as the label.
     res = model.predict(df)
-    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want 
-    # the first element.
-    #     print("Res: {0}").format(res)
     print(res)
     flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + \
                  res[0] + ".png"
@@ -40,11 +35,11 @@ demo = gr.Interface(
     description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
     allow_flagging="never",
     inputs=[
-        gr.Number(default=2.0, label="sepal length (cm)"),
-        gr.Number(default=1.0, label="sepal width (cm)"),
-        gr.Number(default=2.0, label="petal length (cm)"),
-        gr.Number(default=1.0, label="petal width (cm)"),
+        gr.Number(label="sepal length (cm)"),
+        gr.Number(label="sepal width (cm)"),
+        gr.Number(label="petal length (cm)"),
+        gr.Number(label="petal width (cm)"),
     ],
     outputs=gr.Image(type="pil"))
 
-demo.launch(debug=True)
+demo.launch(debug=True)