# 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("iris_modal", version=1)
# model_dir = model.download()
# model = joblib.load(model_dir + "/iris_model.pkl")


# 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=iris,
#     title="Iris Flower Predictive Analytics",
#     description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
#     allow_flagging="never",
#     inputs=[
#         gr.inputs.Number(default=1.0, label="sepal length (cm)"),
#         gr.inputs.Number(default=1.0, label="sepal width (cm)"),
#         gr.inputs.Number(default=1.0, label="petal length (cm)"),
#         gr.inputs.Number(default=1.0, label="petal width (cm)"),
#         ],
#     outputs=gr.Image(type="pil"))

# demo.launch()

import gradio as gr
from PIL import Image
import hopsworks

project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU")
fs = project.get_feature_store()

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()