Spaces:
Sleeping
Sleeping
File size: 1,693 Bytes
fc9d5c1 602bdb4 fc9d5c1 602bdb4 3faa322 602bdb4 fc9d5c1 602bdb4 fc9d5c1 49c8dfd 602bdb4 fc9d5c1 59a1ae2 fc9d5c1 59a1ae2 fc9d5c1 49c8dfd 602bdb4 49c8dfd fc9d5c1 4f11c59 49c8dfd 3faa322 59a1ae2 602bdb4 fc9d5c1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
import gradio as gr
import pandas as pd
from neuralprophet import NeuralProphet
import io
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
url = "VN Index Historical Data.csv"
df = pd.read_csv(url)
df = df[["Date", "Price"]]
df = df.rename(columns={"Date": "ds", "Price": "y"})
df.fillna(method='ffill', inplace=True)
df.dropna(inplace=True)
class CustomNeuralProphet(NeuralProphet):
pass
m = CustomNeuralProphet(
n_forecasts=30,
n_lags=12,
changepoints_range=1,
num_hidden_layers=3,
yearly_seasonality=True,
n_changepoints=150,
trend_reg_threshold=False,
d_hidden=3,
global_normalization=True,
seasonality_reg=1,
unknown_data_normalization=True,
seasonality_mode="multiplicative",
drop_missing=True,
learning_rate=0.03,
)
m.fit(df, freq='D')
future = m.make_future_dataframe(df, periods=30, n_historic_predictions=True)
forecast = m.predict(future)
def predict_vn_index(option=None):
fig = m.plot(forecast)
path = "forecast_plot.png"
fig.savefig(path)
disclaimer = "Quý khách chỉ xem đây là tham khảo, công ty không chịu bất cứ trách nhiệm nào về tình trạng đầu tư của quý khách."
return path, disclaimer
if __name__ == "__main__":
dropdown = gr.inputs.Dropdown(["VNIndex"], label="Choose an option", default="VNIndex")
image_output = gr.outputs.Image(type="filepath", label="Forecast Plot")
disclaimer_output = gr.outputs.Textbox(label="Disclaimer")
interface = gr.Interface(fn=predict_vn_index, inputs=dropdown, outputs=[image_output, disclaimer_output], title="Dự báo VN Index 30 ngày tới")
interface.launch()
|