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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
"""
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