#imports
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=1)
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")


def passenger(pclass, sex, age, embarked):
    input_list = []
    input_list.append(int(pclass+1))
    input_list.append(int(sex))
    input_list.append(int(age))
    input_list.append(int(embarked))
    # '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.
    titanic_url = "https://raw.githubusercontent.com/AbyelT/ID2223-Scalable-ML-and-DL/main/Lab1/Titanic/assets/" + str(res[0]) + ".png"
    img = Image.open(requests.get(titanic_url, stream=True).raw)            
    return img
        
demo = gr.Interface(
    fn=passenger,
    title="Titanic Survival Predictive Analytics",
    #description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
    allow_flagging="never",
    inputs=[
        gr.inputs.Dropdown(choices=["Class 1","Class 2","Class 3"], type="index", label="Pclass"),
        gr.inputs.Dropdown(choices=["Male", "Female"], type="index", label="Sex"),
        gr.inputs.Dropdown(choices=["0","1", "2", "3", "4","5"], type="index", label="Age"),
        gr.inputs.Dropdown(choices=["S", "C", "Q"], type="index", label="Embarked"),
        ],
    outputs=gr.Image(type="pil"))
    
demo.launch()