import gradio as gr
import numpy as np
from PIL import Image
import requests

import hopsworks
import joblib

project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
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, sex, age, sibsp, parch, fare, embarked):
    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(fare)
    input_list.append(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.
    return res[0]


# def iris(sepal_length, sepal_width, petal_length, petal_width):
#     input_list = []
#     input_list.append(sepal_length)
#     input_list.append(sepal_width)
#     input_list.append(petal_length)
#     input_list.append(petal_width)
#     # '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.
#     flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
#     img = Image.open(requests.get(flower_url, stream=True).raw)            
#     return img
        
demo = gr.Interface(
    fn=titanic,
    title="Titanic Predictive Analytics",
    description="Experiment to predict if a passenger survived the Titanic disaster",
    allow_flagging="never",
    inputs=[
        gr.inputs.Number(default=1.0, label="PClass"),
        gr.inputs.Number(default=1.0, label="Sex"),
        gr.inputs.Number(default=1.0, label="Age"),
        gr.inputs.Number(default=1.0, label="SibSp"),
        gr.inputs.Number(default=1.0, label="Parch"),
        gr.inputs.Number(default=1.0, label="Fare"),
        gr.inputs.Number(default=1.0, label="Embarked")
        ],
    outputs=gr.Textbox())

demo.launch()

# monitoring part of the code
# import gradio as gr
# from PIL import Image
# import hopsworks

# project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
# fs = project.get_feature_store()
# #h
# dataset_api = project.get_dataset_api()

# dataset_api.download("Resources/images/latest_iris.png")
# dataset_api.download("Resources/images/actual_iris.png")
# dataset_api.download("Resources/images/df_recent.png")
# dataset_api.download("Resources/images/confusion_matrix.png")

# with gr.Blocks() as demo:
#     with gr.Row():
#       with gr.Column():
#           gr.Label("Today's Predicted Image")
#           input_img = gr.Image("latest_iris.png", elem_id="predicted-img")
#       with gr.Column():          
#           gr.Label("Today's Actual Image")
#           input_img = gr.Image("actual_iris.png", elem_id="actual-img")        
#     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()