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| # import gradio as gr | |
| # import numpy as np | |
| # from PIL import Image | |
| # import requests | |
| # import hopsworks | |
| # import joblib | |
| # project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU") | |
| # fs = project.get_feature_store() | |
| # 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() | |
| import gradio as gr | |
| from PIL import Image | |
| import hopsworks | |
| project = hopsworks.login(api_key_value="rA4UUi0EGe9o2Lpo.xoqva15Ia7l8Fz7PBFrFTV4WjSG8B1aQofJlVp3oV3Xp0iHyFTzw5ybC4OapypyU") | |
| fs = project.get_feature_store() | |
| dataset_api = project.get_dataset_api() | |
| dataset_api.download("Resources/images/latest_iris.png") | |
| dataset_api.download("Resources/images/actual_iris.png") | |
| dataset_api.download("Resources/images/df_recent.png") | |
| dataset_api.download("Resources/images/confusion_matrix.png") | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Label("Today's Predicted Image") | |
| input_img = gr.Image("latest_iris.png", elem_id="predicted-img") | |
| with gr.Column(): | |
| gr.Label("Today's Actual Image") | |
| input_img = gr.Image("actual_iris.png", elem_id="actual-img") | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Label("Recent Prediction History") | |
| input_img = gr.Image("df_recent.png", elem_id="recent-predictions") | |
| with gr.Column(): | |
| gr.Label("Confusion Maxtrix with Historical Prediction Performance") | |
| input_img = gr.Image("confusion_matrix.png", elem_id="confusion-matrix") | |
| demo.launch() |