Delete app.py

app.py

@@ -1,45 +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("wine_model", version=2)
-model_dir = model.download()
-model = joblib.load(model_dir + "/wine_model.pkl")
-print("Model downloaded")
-
-def wine(alcohol, chlorides, density, type, volatil_acidity):
-    print("Calling function")
-    df = pd.DataFrame([[alcohol, chlorides, density, type, volatil_acidity]], 
-                      columns=['alcohol','chlorides','density','type','volatil_acidity'])
-    print("Predicting")
-    print(df)
-    res = model.predict(df) 
-    print(res)
-    wine_url = "https://raw.githubusercontent.com/Anniyuku/wine_quality/main/" + res[0] + ".png"
-    img = Image.open(requests.get(wine_url, stream=True).raw)            
-    return img
-        
-demo = gr.Interface(
-    fn=wine,
-    title="Wine Predictive Analytics",
-    description="Experiment with alcohol, chlorides, density, type, volatil_acidity to predict which flower it is.",
-    allow_flagging="never",
-    inputs=[
-        gr.inputs.Number(default=9.00, label="alcohol"),
-        gr.inputs.Number(default=0.60, label="chlorides"),
-        gr.inputs.Number(default=1.00, label="density"),
-        gr.inputs.Number(default=1.00, label="type"),
-        gr.inputs.Number(default=1.00, label="volatil_acidity"),
-        ],
-    outputs=gr.Image(type="pil"))
-
-demo.launch(debug=True)
-