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import gradio as gr |
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import numpy as np |
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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(api_key_value="0rdWXlLgEd3mkGOg.iRZ7TtAkWGPlJHNQcAEph6Qbokoaq7QTBRI9ckwWUki8tIYGyBvrKhJvtLoUOGQ4") |
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fs = project.get_feature_store() |
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def get_model(project, model_name): |
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"""Retrieve desired model or download it from the Hopsworks Model Registry. |
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In second case, it will be physically downloaded to this directory""" |
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TARGET_FILE = "model.pkl" |
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list_of_files = [os.path.join(dirpath,filename) for dirpath, _, filenames \ |
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in os.walk('.') for filename in filenames if filename == TARGET_FILE] |
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if list_of_files: |
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model_path = list_of_files[0] |
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model = joblib.load(model_path) |
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else: |
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if not os.path.exists(TARGET_FILE): |
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mr = project.get_model_registry() |
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model = mr.get_model("xgboost_model", version=1) |
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model_dir = model.download() |
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model = joblib.load("/model.pkl") |
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return model |
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model = get_model(project,"xgboost_model") |
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def forecast(): |
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x = [ 0. , 24 , -0.68645433, -0.06804887, -0.31264014, |
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-0.13749569, -0.32063957, -0.2942814 , -0.18460245, -0.41253886, |
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0.06395449, 0.71276574, -0.36466156, -1.03879548, -0.65985627, |
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0 , 0 , 0.12254366, 0.39172671, 0.34205118, |
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0.21383452, -1.0216134 , 0.40277851, -0.34577169, -0.36832646, |
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-0.7210296 , 0 ] |
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res = model.predict(np.asarray(x).reshape(-1, 1)) |
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return model_dir |
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demo = gr.Interface( |
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fn=forecast, |
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title="Air Quality Prediction", |
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description="Get aqi value", |
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allow_flagging="never", |
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inputs=[], |
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outputs=gr.Textbox(label="Result: ")) |
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demo.launch() |
