Update app.py

app.py

@@ -1,32 +1,9 @@
- import datetime
 import gradio as gr
-import pandas as pd
-import yfinance as yf
-import seaborn as sns
-sns.set()
-import matplotlib.pyplot as plt
-import plotly.graph_objects as go
+from config import index_options, time_intervals, START_DATE, END_DATE, FORECAST_PERIOD
+from data_fetcher import get_stocks_from_index
+from stock_analysis import get_stock_graph_and_info
 
-from datetime import date, timedelta
-from matplotlib import pyplot as plt
-from plotly.subplots import make_subplots
-from pytickersymbols import PyTickerSymbols
-from statsmodels.tsa.arima.model import ARIMA
-from pandas.plotting import autocorrelation_plot
-from dateutil.relativedelta import relativedelta
-
-index_options = ['FTSE 100(UK)', 'NASDAQ(USA)', 'CAC 40(FRANCE)']
-ticker_dict = {'FTSE 100(UK)': 'FTSE 100', 'NASDAQ(USA)': 'NASDAQ 100', 'CAC 40(FRANCE)': 'CAC 40'}
-time_intervals = ['1d', '1m', '5m', '15m', '60m']
-
-global START_DATE, END_DATE
-END_DATE = date.today()
-START_DATE = END_DATE - relativedelta(years=1)
-FORECAST_PERIOD = 7
 demo = gr.Blocks()
-stock_names = []
-
-
 
 with demo:
     d1 = gr.Dropdown(index_options, label='Please select Index...', info='Will be adding more indices later on', interactive=True)
@@ -35,94 +12,17 @@ with demo:
     d4 = gr.Radio(['Line Graph', 'Candlestick Graph'], label='Select Graph Type', value='Line Graph', interactive=True)
     d5 = gr.Dropdown(['ARIMA', 'Prophet', 'LSTM'], label='Select Forecasting Method', value='ARIMA', interactive=True)
 
-    def forecast_series(series, model="ARIMA", forecast_period=7):
-        predictions = list()
-        if series.shape[1] > 1:
-            series = series['Close'].values.tolist()
-        
-        if model == "ARIMA":
-            for i in range(forecast_period):
-                model = ARIMA(series, order=(5, 1, 0))
-                model_fit = model.fit()
-                output = model_fit.forecast()
-                yhat = output[0]
-                predictions.append(yhat)
-                series.append(yhat)
-        elif model == "Prophet":
-            # Implement Prophet forecasting method
-            pass
-        elif model == "LSTM":
-            # Implement LSTM forecasting method
-            pass
-
-        return predictions
-
-    def is_business_day(a_date):
-        return a_date.weekday() < 5
+    out_graph = gr.Plot()
+    out_fundamentals = gr.DataFrame()
 
-
-    def get_stocks_from_index(idx):
-        stock_data = PyTickerSymbols()
-        index = ticker_dict[idx]
-        stocks = list(stock_data.get_stocks_by_index(index))
-        stock_names = [f"{stock['name']}:{stock['symbol']}" for stock in stocks]
-        return gr.Dropdown(choices=stock_names, label='Please Select Stock from your selected index', interactive=True)
-
-
-    d1.input(get_stocks_from_index, d1, d2)
-    
-    def get_stock_graph(idx, stock, interval, graph_type, forecast_method):
-        stock_name, ticker_name = stock.split(":")
-        
-        if ticker_dict[idx] == 'FTSE 100':
-            ticker_name += '.L' if ticker_name[-1] != '.' else 'L'
-        elif ticker_dict[idx] == 'CAC 40':
-            ticker_name += '.PA'
-    
-        series = yf.download(tickers=ticker_name, start=START_DATE, end=END_DATE, interval=interval)
-        series = series.reset_index()
-    
-        predictions = forecast_series(series, model=forecast_method)
-    
-        last_date = pd.to_datetime(series['Date'].values[-1])
-        forecast_week = []
-        i = 1
-        while len(forecast_week) < FORECAST_PERIOD:
-            next_date = last_date + timedelta(days=i)
-            if is_business_day(next_date):
-                forecast_week.append(next_date)
-            i += 1
-    
-        # Ensure predictions and forecast_week have the same length
-        predictions = predictions[:len(forecast_week)]
-        forecast_week = forecast_week[:len(predictions)]
-    
-        forecast = pd.DataFrame({"Date": forecast_week, "Forecast": predictions})
-    
-        if graph_type == 'Line Graph':
-            fig = go.Figure()
-            fig.add_trace(go.Scatter(x=series['Date'], y=series['Close'], mode='lines', name='Historical'))
-            fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast'))
-        else:  # Candlestick Graph
-            fig = go.Figure(data=[go.Candlestick(x=series['Date'],
-                                                 open=series['Open'],
-                                                 high=series['High'],
-                                                 low=series['Low'],
-                                                 close=series['Close'],
-                                                 name='Historical')])
-            fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast'))
-    
-        fig.update_layout(title=f"Stock Price of {stock_name}",
-                          xaxis_title="Date",
-                          yaxis_title="Price")
-    
-        return fig
-    out = gr.Plot()
     inputs = [d1, d2, d3, d4, d5]
-    d2.input(get_stock_graph, inputs, out)
-    d3.input(get_stock_graph, inputs, out)
-    d4.input(get_stock_graph, inputs, out)
-    d5.input(get_stock_graph, inputs, out)
+    outputs = [out_graph, out_fundamentals]
 
+    d1.input(get_stocks_from_index, d1, d2)
+    d2.input(get_stock_graph_and_info, inputs, outputs)
+    d3.input(get_stock_graph_and_info, inputs, outputs)
+    d4.input(get_stock_graph_and_info, inputs, outputs)
+    d5.input(get_stock_graph_and_info, inputs, outputs)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()