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| import gradio as gr | |
| from PIL import Image | |
| import requests | |
| import hopsworks | |
| import joblib | |
| import pandas as pd | |
| import os | |
| import time | |
| project = hopsworks.login(api_key_value=os.environ['UNI_HOPSWORKS_API_KEY']) | |
| fs = project.get_feature_store() | |
| mr = project.get_model_registry() | |
| model = mr.get_model("iris_model", version=1) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/iris_model.pkl") | |
| print("Model downloaded") | |
| def iris(sepal_length, sepal_width, petal_length, petal_width): | |
| print("Calling 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 | |
| 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.components.Number(2.0,label="sepal length (cm)"), | |
| gr.components.Number(1.0,label="sepal width (cm)"), | |
| gr.components.Number(2.0,label="petal length (cm)"), | |
| gr.components.Number(1.0,label="petal width (cm)"), | |
| ], | |
| outputs=gr.Image(type="pil") | |
| ) | |
| demo.launch(debug=True) |