Delete app.py

app.py

@@ -1,92 +0,0 @@
-import gradio as gr
-import numpy as np
-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("titanic_modal", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/titanic_model.pkl")
-
-
-def titanic(pclass,age,sibsp,parch,fare,sex,embarked):
-    input_list = []
-    input_list.append(pclass)
-    input_list.append(age)
-    input_list.append(sibsp)
-    input_list.append(parch)
-    input_list.append(fare)
-    if sex == "male":
-        input_list.append(0)
-        input_list.append(1)
-    elif sex == "female":
-        input_list.append(1)
-        input_list.append(0)
-    
-    if embarked == "C":
-        input_list.append(1)
-        input_list.append(0)
-        input_list.append(0)
-        input_list.append(0)
-    elif embarked == "Q":
-        input_list.append(0)
-        input_list.append(1)
-        input_list.append(0)
-        input_list.append(0)
-    elif embarked == "S":
-        input_list.append(0)
-        input_list.append(0)
-        input_list.append(1)
-        input_list.append(0)
-    elif embarked == "Unknown":
-        input_list.append(0)
-        input_list.append(0)
-        input_list.append(0)
-        input_list.append(1)
-    
-    # input_df = pd.DataFrame(data=input_list, columns = ['Pclass', 'Age', 'SibSp', 'Parch', 
-    #                                                     'Fare', 'Sex_female','Sex_male', 
-    #                                                     'Embarked_C', 'Embarked_Q', 'Embarked_S',
-    #                                                     'Embarked_Unknown'])
-    # 'res' is a list of predictions returned as the label.
-    res = model.predict(np.asarray(input_list).reshape(1, -1))
-    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want 
-    # the first element.
-    if res[0] == 1:
-        res_str = "survivor"
-    else:
-        res_str = "victim"
-    passenger_url = "https://raw.githubusercontent.com/daniel-rdt/serverless_ml_titanic_dr/main/assets/" + res_str + ".png"
-    img = Image.open(requests.get(passenger_url, stream=True).raw)            
-    return img
-    # if res[0] == 1:
-    #     return "The passenger is predicted to be a survivor."
-    # else:
-    #     return "The passenger is predicted to be a victim."
-        
-demo = gr.Interface(
-    fn=titanic,
-    title="Titanic Passenger Predictive Analytics",
-    description="Experiment with passenger data to predict whether the passenger is a survivor or not.",
-    allow_flagging="never",
-    inputs=[
-        gr.inputs.Number(default=2, label="Passenger class (choose from either 1, 2 or 3)"),
-        gr.inputs.Number(default=30, label="Age in full years (if child younger than 1 round up to 1)"),
-        gr.inputs.Number(default=1, label="Number of siblings or spouses"),
-        gr.inputs.Number(default=0, label="Number of parents or children"),
-        gr.inputs.Number(default=100, label="Fare (cost between 0 and 513)"),
-        gr.inputs.Textbox(default="male", label="Sex (choose from either male or female)"),
-        gr.inputs.Textbox(default="Unknown", label="Embarked (choose from either C, Q, S or Unknown)"),
-        ],
-    # outputs=gr.outputs.Textbox())
-    outputs=gr.Image(type="pil"))
-
-demo.launch()