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_passanger(age, sex, sibsp, parch, fare, embarked, pclass):
    input_list = []
    sex_value = 1 if sex=='female' else 0
    pclass_value = int(pclass)
    if embarked == 'S':
        embarked_value = 0
    elif embarked == 'C':
        embarked_value = 1
    else:
        embarked_value = 2
    input_list.append(pclass_value)
    input_list.append(sex_value)
    input_list.append(age)
    input_list.append(sibsp)
    input_list.append(parch)
    input_list.append(fare)
    input_list.append(embarked_value)


    # '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.
    img_urls=["https://uxwing.com/wp-content/themes/uxwing/download/health-sickness-organs/skull-icon.png", "https://uxwing.com/wp-content/themes/uxwing/download/emoji-emoticon/happy-icon.png"]
    img_url  = img_urls[res[0]]
    img = Image.open(requests.get(img_url, stream=True).raw)
    return img
        
demo = gr.Interface(
    fn=titanic_passanger,
    title="Titanic Survivor Predictive Analytics",
    description="Experiment with the features to predict survivor status.",
    allow_flagging="never",
    inputs=[
        gr.inputs.Number(default=22.0, label="Age"),
        gr.inputs.Radio(['female', 'male'], label="Sex"),

        gr.inputs.Number(default=1.0, label="Number of siblings and spouses aboard"),
        gr.inputs.Number(default=1.0, label="Number of parents and children aboard"),

        gr.inputs.Number(default=1.0, label="Fare"),

        gr.inputs.Radio(['S', 'C', 'Q'], label="Port embarked"),
        gr.inputs.Radio(['1', '2', '3'], label="Ticket class"),
    ],
    outputs=gr.Image(type="pil"))

demo.launch()