Delete app.py

app.py

@@ -1,49 +0,0 @@
-import gradio as gr
-from PIL import Image
-import requests
-import hopsworks
-import joblib
-import pandas as pd
-
-project = hopsworks.login()
-fs = project.get_feature_store()
-
-
-mr = project.get_model_registry()
-model = mr.get_model("iris_model", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/iris_model.pkl")
-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"
-    img = Image.open(requests.get(flower_url, stream=True).raw)            
-    return img
-        
-demo = gr.Interface(
-    fn=iris,
-    title="Iris Flower Predictive Analytics",
-    description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
-    allow_flagging="never",
-    inputs=[
-        gr.inputs.Number(default=2.0, label="sepal length (cm)"),
-        gr.inputs.Number(default=1.0, label="sepal width (cm)"),
-        gr.inputs.Number(default=2.0, label="petal length (cm)"),
-        gr.inputs.Number(default=1.0, label="petal width (cm)"),
-        ],
-    outputs=gr.Image(type="pil"))
-
-demo.launch(debug=True)
-