| import gradio as gr | |
| from PIL import Image | |
| import requests | |
| import hopsworks | |
| import joblib | |
| import pandas as pd | |
| project = hopsworks.login() | |
| 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]], | |
| columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width']) | |
| print("Predicting") | |
| print(df) | |
| res = model.predict(df) | |
| 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.Number(label="sepal length (cm)"), | |
| gr.Number(label="sepal width (cm)"), | |
| gr.Number(label="petal length (cm)"), | |
| gr.Number(label="petal width (cm)"), | |
| ], | |
| outputs=gr.Image(type="pil")) | |
| demo.launch(debug=True) | |