import gradio as gr import numpy as np from PIL import Image import requests import hopsworks import joblib project = hopsworks.login() fs = project.get_feature_store() #HwJaWmtvaCzFra3g.89QYueFGuScRnJkiepzG2tiWtKSrqNHCCJrnVie9fwhIMeJxRUpAGAT7mF36MDMv mr = project.get_model_registry() model = mr.get_model("iris_modal", version=1) model_dir = model.download() model = joblib.load(model_dir + "/iris_model.pkl") def iris(sepal_length, sepal_width, petal_length, petal_width): input_list = [] input_list.append(sepal_length) input_list.append(sepal_width) input_list.append(petal_length) input_list.append(petal_width) # 'res' is a list of predictions returned as the label. res = model.predict(np.asarray(input_list).reshape(1, -1)) # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want # the first element. 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=1.0, label="sepal length (cm)"), gr.inputs.Number(default=1.0, label="sepal width (cm)"), gr.inputs.Number(default=1.0, label="petal length (cm)"), gr.inputs.Number(default=1.0, label="petal width (cm)"), ], outputs=gr.Image(type="pil")) demo.launch(share = True)