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Update app.py
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app.py
CHANGED
@@ -20,43 +20,25 @@ feature_view = fs.get_feature_view(
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def air_quality(city):
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air_quality_df = pd.DataFrame()
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weather_df = pd.DataFrame()
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for i in range(8):
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weather_data = get_weather_df([get_weather_data((datetime.now() + timedelta(days=i)).strftime("%Y-%m-%d"))])
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weather_df = weather_df.append(weather_data)
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quality_data= get_air_quality_df([get_air_quality_data(city)])
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air_quality_df=air_quality_df.append(quality_data)
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print(air_quality_df)
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print(weather_df)
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weather_df = weather_df.drop(columns=["feelslikemin", "feelslikemax","precipprob", "snow", "snowdepth", "uvindex", "date","city","conditions"]).fillna(0)
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"""predictions = ''
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for k in range(7):
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predictions += "Predicted AQI on " + (datetime.now() + timedelta(days=k)).strftime('%Y-%m-%d') + ": " + str(int(preds[k]))+"\n"
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print(predictions)"""
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return preds
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demo = gr.Interface(fn=air_quality, title="Air quality predictor",
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description="Input a value to get next weeks AQI prediction for
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start_date = datetime.now() - timedelta(days=1)
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start_time = int(start_date.timestamp()) * 1000
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X = feature_view.get_batch_data(start_time=start_time)
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X = X.drop(columns=["date"]).fillna(0)
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mr = project.get_model_registry()
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model = mr.get_model("gradient_boost_paris_model", version=1)
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model_dir = model.download()
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model = joblib.load(model_dir + "/model.pkl")
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preds = model.predict(X)
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demo = gr.Interface(fn=air_quality, title="Air quality predictor",
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description="Input a value to get next weeks AQI prediction for Paris", inputs="text", outputs="text")
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