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)