app.py

import gradio as gr
from PIL import Image
import hopsworks
import joblib

project = hopsworks.login()
fs = project.get_feature_store()
mr = project.get_model_registry()
model = mr.get_model("titanic_survival_modal", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")

def tb_titanic(pclass,sex,age,sibsp,parch,embarked,fare_per_customer,cabin):
    input_list = []
    input_list.append(pclass)
    input_list.append(sex)
    input_list.append(age)
    input_list.append(sibsp)
    input_list.append(parch)
    input_list.append(embarked)
    input_list.append(fare_per_customer)
    input_list.append(cabin)
    # 'res' is a list of predictions returned as the label.
    #global res
    res = model.predict(np.asarray(input_list).reshape(1, 8))
    if res[0]=="S":
       return ("survived")
    else:
       return("deceased")
  


dataset_api = project.get_dataset_api()
dataset_api.download("Resources/images/df_recent.png")
dataset_api.download("Resources/images/confusion_matrix.png")

with gr.Blocks() as demo:
    fn=tb_titanic,
    with gr.Row():
      with gr.Column():
          gr.Label("Today's Predicted survive")
          input=gr.Textbox()
      with gr.Column():          
          gr.Label("Today's Actual survive")
          input=gr.Textbox()
    with gr.Row():
      with gr.Column():
          gr.Label("Recent Prediction History")
          input_img = gr.Image("df_recent.png", elem_id="recent-predictions")
      with gr.Column():          
          gr.Label("Confusion Maxtrix with Historical Prediction Performance")
          input_img = gr.Image("confusion_matrix.png", elem_id="confusion-matrix")        

demo.launch()