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()
# #q

# 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]

        
# 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: Female = 0, Male = 1"),
#         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: S = 0, C = 1, Q = 2"),
#         ],
#     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="otd1BvtKwvlF8OC1.Y8Kyt1QpZqDPMRNPIF3KvVGuFJpRdxIy39879ueQwymTgSDUU9vWKFMOnBqsyxfk")
fs = project.get_feature_store()
#ss
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:
    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()