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import gradio as gr |
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import numpy as np |
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import pandas as pd |
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from pandas.core.frame import DataFrame |
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from PIL import Image |
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import requests |
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import hopsworks |
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import joblib |
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project = hopsworks.login() |
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fs = project.get_feature_store() |
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mr = project.get_model_registry() |
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model = mr.get_model("aurora_model", version=1) |
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model_dir = model.download() |
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model = joblib.load(model_dir+"/aurora_model.pkl") |
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def tb_aurora(Kp_index, visibility, icon): |
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input_list = [] |
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input_list.append(Kp_index) |
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input_list.append(visibility) |
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input_icon = icon |
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icon_feature_list = ['clear_day', 'clear_night', 'cloudy', 'fog', 'partly_cloudy_day', 'partly_cloudy_night', 'rain', |
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'snow', 'wind'] |
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icon_feature_list.append(input_icon) |
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icon_df = DataFrame(icon_feature_list) |
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icon_df_one = pd.get_dummies(icon_df) |
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icon = icon_df_one.values.tolist()[9] |
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input_list.extend(icon) |
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print(input_list) |
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res = model.predict(np.asarray(input_list).reshape(1, 11)) |
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aurora_url = "https://raw.githubusercontent.com/NeoForNew/ID2223_scalable_machine_learning_and_deep_learning/main/Project/pic/" + str(res[0]) + ".png" |
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img = Image.open(requests.get(aurora_url, stream=True).raw) |
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return img |
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demo = gr.Interface( |
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fn=tb_aurora, |
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title="Aurora Predictive Analytics", |
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description="Predict aurora 0 for not occur and 1 for occur. ", |
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inputs=[ |
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gr.inputs.Number(default=0.0, label="Kp_index"), |
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gr.inputs.Number(default=0.0, label="visibility"), |
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gr.inputs.Dropdown(['clear_day', 'clear_night', 'cloudy', 'fog', 'partly_cloudy_day', 'partly_cloudy_night', 'rain', |
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'snow', 'wind'], label="icon"), |
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], |
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outputs=gr.Image(type="pil")) |
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demo.launch() |
