import gradio as gr
from PIL import Image
import requests
import hopsworks
import joblib
import pandas as pd


# def greet(name):
#     return "Oj Hello " + name + "!!"


# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()

def iris(sepal_length, sepal_width, petal_length, petal_width):
    print("Calling iris() function")
    #     df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]],
    df = pd.DataFrame([[sepal_length, sepal_width, petal_length, petal_width]],
                      columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
    print("Predicting...")
    print(df)
    # 'res' is a list of predictions returned as the label.
    res = model.predict(df)
    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
    # the first element.
    #     print("Res: {0}").format(res)
    print(res)
    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


print("Logging in to Hopsworks...")
project = hopsworks.login()

print("Getting feature store...")
fs = project.get_feature_store()

print("Getting model registry...")
mr = project.get_model_registry()

print("Getting model: ...")
model = mr.get_model("iris_model", version=1)

print("Downloading model...")
model_dir = model.download()

print("Initializing model locally...")
model = joblib.load(model_dir + "/iris_model.pkl")

print("Gradio version:", gr.__version__)

print("Configuring gradio interface...")
# 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=2.0, label="sepal length (cm)"),
#        gr.inputs.Number(default=1.0, label="sepal width (cm)"),
#        gr.inputs.Number(default=2.0, label="petal length (cm)"),
#        gr.inputs.Number(default=1.0, label="petal width (cm)"),
#    ],
#    outputs=gr.Image(type="pil"))

demo = gr.Interface(
    fn=iris,
    title="Iris Flower Predictive Analytics",
    description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
    inputs=[
        gr.Number(label="sepal length (cm)", value=2.0),
        gr.Number(label="sepal width (cm)", value=1.0),
        gr.Number(label="petal length (cm)", value=2.0),
        gr.Number(label="petal width (cm)", value=1.0)
    ],
    outputs=gr.Image(type="pil"),
)

print("Launching gradio...")
demo.launch(debug=True)

"""
Logging in to Hopsworks...
Connected. Call `.close()` to terminate connection gracefully.

Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/201877
Getting feature store...
Connected. Call `.close()` to terminate connection gracefully.
Getting model registry...
Connected. Call `.close()` to terminate connection gracefully.
Getting model: ...
Downloading model...
Downloading file ... Initializing model locally...
Gradio version: 4.1.2
Configuring gradio interface...
Traceback (most recent call last):
  File "/home/user/app/app.py", line 62, in <module>
    gr.inputs.Number(default=2.0, label="sepal length (cm)"),
AttributeError: module 'gradio' has no attribute 'inputs
"""