Delete app.py

app.py

@@ -1,47 +0,0 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-import requests
-
-import hopsworks
-import joblib
-
-project = hopsworks.login()
-fs = project.get_feature_store()
-
-
-mr = project.get_model_registry()
-model = mr.get_model("titanic_modal", version=2)
-model_dir = model.download()
-model = joblib.load(model_dir + "/titanic_model.pkl")
-
-
-def titanic(pclass, sex, age_bin, fare_bin):
-    input_list = []
-    input_list.append(pclass)
-    input_list.append(sex)
-    input_list.append(age_bin)
-    input_list.append(fare_bin)
-    # 'res' is a list of predictions returned as the label.
-    res = model.predict(np.asarray(input_list).reshape(1, -1)) 
-    res_0 = str(res[0])
-    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want 
-    # the first element.
-    prediction_url = "https://raw.githubusercontent.com/torileatherman/serverless_ml_titanic/main/src/assets/"+res_0+".png"
-    img = Image.open(requests.get(prediction_url, stream=True).raw)            
-    return img
-        
-demo = gr.Interface(
-    fn=titanic,
-    title="Titanic Survival Predictive Analytics",
-    description="Experiment with class, sex, age, and fare type to predict if the passenger survived",
-    allow_flagging="never",
-    inputs=[
-        gr.inputs.Number(default=1, label="Class (1 is highest, 3 is lowest"),
-        gr.inputs.Number(default=1, label="Gender (0 is male, 1 is female)"),
-        gr.inputs.Number(default=20, label="Age (years)"),
-        gr.inputs.Number(default=1, label="Fare Type (1 is lowest, 4 is highest)"),
-        ],
-    outputs=gr.Image(type="pil"))
-
-demo.launch(share=True)