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import gradio as gr |
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from fastai.data.all import * |
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from fastai.vision.all import * |
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import pandas as pd |
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learn_fc = load_learner('fc_model.pkl') |
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prediction_horizon = 32 |
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look_back = 64 |
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feature_selected = ['Open', 'Volume'] |
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label_selected = 'Open' |
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class MyDataset: |
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"""Great for big datasets and long sequencies""" |
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def __init__(self, df, features, target, look_back=1, prediction_horizon=1): |
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self.df, self.look_back, self.prediction_horizon = df, look_back, prediction_horizon |
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self.features, self.target = features, target |
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train_mean = 0 |
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train_std = self.df.std() |
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self.df = (self.df - train_mean) / train_std |
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def __len__(self): |
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n = len(self.df)-self.look_back+1-self.prediction_horizon |
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return n |
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def new_empty(self): return self[0] |
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def __getitem__(self, i): |
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p = self.look_back |
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q = self.prediction_horizon |
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X = self.df.iloc[i:(i+p)][self.features].values |
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y = self.df.iloc[(i+p):(i+p + q)][self.target].values |
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return tensor(X), tensor(y) |
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def predict(df): |
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df.set_index('Date', inplace=True) |
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test_ds = MyDataset(df, feature_selected, label_selected, look_back, prediction_horizon) |
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pred,_,_ = learn_fc.predict(test_ds[len(test_ds)-1]) |
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fig = plt.figure() |
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plt.plot(pred) |
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return fig |
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title = "Predict time series" |
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description = "Predict next 32 values for stock time series" |
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examples = ['BA-test.csv', 'BA-test-2.csv', 'BA-test-3.csv'] |
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iface = gr.Interface(fn=predict, inputs=gr.inputs.Timeseries(x=None, y=['Date','Open','High','Low','Close','Adj Close','Volume']), |
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outputs=gr.outputs.Plot(type='matplotlib'), examples=examples, title=title, description=description) |
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iface.launch() |
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