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from data_processing import process_data, standardizeX |
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from api_data import fetch_data |
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from tensorflow import keras |
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import pandas as pd |
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import gradio as gr |
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import numpy as np |
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import pickle |
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import io |
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temp_model = keras.models.load_model('results/temp_model.h5') |
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hum_model = keras.models.load_model('results/hum_model.h5') |
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scale = pickle.load(open("results/mean_std.pkl", "rb")) |
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mean = scale["mean"] |
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std = scale["std"] |
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temp_mean = mean[0] |
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temp_std = std[0] |
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hum_mean = mean[2] |
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hum_std = std[2] |
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def forecast(time): |
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time = int(time) |
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if time>9 or time<0: |
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return "please enter valid time", "0 to 9" |
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response = fetch_data(9-time) |
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if response is not None: |
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df = pd.read_csv(io.StringIO(response.content.decode('utf-8'))) |
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df = process_data(df) |
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X = np.array(df[-24:]).reshape(-1, 24, 16) |
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X = standardizeX(X, mean, std) |
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temp = temp_model.predict(X) |
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hum = hum_model.predict(X) |
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temp = (temp[0][0]*temp_std) + temp_mean |
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hum = (hum[0][0]*hum_std) + hum_mean |
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temp = str(round(((temp-32)*5)/9, 2)) + " c" |
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hum = str(round(hum, 2)) + " %" |
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return temp, hum |
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else: |
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return "API access denied" |
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gr.Interface(fn=forecast, |
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inputs = gr.Textbox(placeholder="Provide value between 0 to 9, 0 means present, 9 means 9 hrs in future "), |
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outputs = [gr.Textbox(label="Temperature"),gr.Textbox(label="Humidity")] |
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).launch() |
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