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]], 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.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.launch(debug=True)