Update app.py

app.py

@@ -1,3 +1,4 @@
+import gradio as gr
 import pandas as pd
 import numpy as np
 import math
@@ -9,9 +10,8 @@ from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense
 from tensorflow.keras.layers import LSTM
 
-import gradio as gr
-
 import yfinance as yf
+from statsmodels.tsa.seasonal import seasonal_decompose
 
 def get_ans(inp):
   plt.close()
@@ -105,8 +105,68 @@ def get_ans(inp):
   df3['index']=range(1, len(df3) + 1)
   lst_output = pd.DataFrame(lst_output, columns=["Values"])
   lst_output['index']=range(1, len(lst_output) + 1)
-  return plt, gr.update(visible=True,value=df, x="Date",y="Open", height=500, width=800),gr.update(visible=True,value=df[-300:], x="Date",y="Open", height=500, width=800),gr.update(visible=True,value=df[-30:], x="Date",y="Open", height=500, width=800), max(np.asarray(df['Open'])), min(np.asarray(df['Open'])), max(np.asarray(df['Open'])[-300:]), min(np.asarray(df['Open'][-300:])), max(np.asarray(df['Open'])[-30:]), min(np.asarray(df['Open'][-30:])), (max(np.asarray(df['Open'])))*(lst_output["Values"][0]), gr.update(visible=True,value=lst_output, x="index",y="Values", height=500, width=800),  gr.update(visible=True,value=df3, x="index",y="Values", height=500, width=800),  gr.update(visible=True,value=df3[-300:], x="index",y="Values", height=500, width=800)
+  the_max = max(np.asarray(df['Open']))
+  df3['Values'] = [i * the_max for i in df3['Values']]
+  return plt, gr.update(visible=True,value=df, x="Date",y="Open", height=500, width=800),gr.update(visible=True,value=df[-300:], x="Date",y="Open", height=500, width=800),gr.update(visible=True,value=df[-30:], x="Date",y="Open", height=500, width=800), max(np.asarray(df['Open'])), min(np.asarray(df['Open'])), max(np.asarray(df['Open'])[-300:]), min(np.asarray(df['Open'][-300:])), max(np.asarray(df['Open'])[-30:]), min(np.asarray(df['Open'][-30:])), (max(np.asarray(df['Open']))) * (lst_output["Values"][0]), gr.update(visible=True,value=lst_output, x="index",y="Values", height=500, width=800),  gr.update(visible=True,value=df3, x="index",y="Values", height=500, width=800),  gr.update(visible=True,value=df3[-300:], x="index",y="Values", height=500, width=800)
 
+def get_seo(inp):
+  plt.close()
+  tickers = yf.Tickers(inp)
+  x = tickers.tickers[inp].history(period="15y")
+  df = x
+  df.reset_index(inplace=True)
+  df1 = df.reset_index()['Close']
+  df['Date'] = pd.to_datetime(df['Date'])
+  scaler = MinMaxScaler(feature_range=(0, 1))
+  df1 = scaler.fit_transform(np.array(df1).reshape(-1, 1))
+  def create_dataset(dataset, time_step=1):
+      dataX, dataY = [], []
+      for i in range(len(dataset) - time_step - 1):
+          a = dataset[i:(i + time_step), 0]
+          dataX.append(a)
+          dataY.append(dataset[i + time_step, 0])
+      return np.array(dataX), np.array(dataY)
+  X_train, y_train = create_dataset(df1, time_step)
+  decompose_result_mult = seasonal_decompose(X_train, model="additive", period=time_step)
+  trend = decompose_result_mult.trend
+  seasonal = decompose_result_mult.seasonal
+  residual = decompose_result_mult.resid
+
+  z = [i[0] for i in trend]
+  z = pd.DataFrame(z, columns=['Values'])
+  z['index'] = range(1, len(z) + 1)
+
+  y = [i[0] for i in seasonal]
+  y = pd.DataFrame(y, columns=['Values'])
+  y['index'] = range(1, len(z) + 1)
+
+  a = [i[0] for i in residual]
+  a = pd.DataFrame(a, columns=['Values'])
+  a['index'] = range(1, len(a) + 1)
+
+  return gr.update(visible=True, value=z, x='index', y='Values', height=500, width=800), gr.update(visible=True, value=y[:100], x='index', y='Values', height=500, width=800), gr.update(visible=True, value=a, x='index', y='Values', height=500, width=800)
+
+
+def get_info(inp):
+  tickers = yf.Ticker(inp)
+  info = tickers.info
+  balance = tickers.balance_sheet
+
+  long_info= info['longBusinessSummary']
+  curr_rat = info['currentRatio']
+  quick_rat = info['quickRatio']
+  short_rat = info['shortRatio']
+  debt_eq = info['debtToEquity']
+  volume = info['volume']
+  market_cap = info['marketCap']
+  curr_price = info['currentPrice']
+  rev_per = info['revenuePerShare']
+
+
+
+
+
+  return long_info, curr_rat, quick_rat, short_rat, debt_eq, volume, market_cap, curr_price, rev_per
 
 with gr.Blocks() as demo:
   with gr.Row().style(equal_height=True):
@@ -114,13 +174,36 @@ with gr.Blocks() as demo:
       gr.Markdown("<center><h1>Stock Analysis Tool<h1></center>")
       gr.Markdown("<center><h3>Give the Ticker of the company you want to analyse. We will provide complete insights on the given company.</h3></center>")
       gr.Markdown("<center>To get the ticker of the company, click <a href = 'https://finance.yahoo.com/lookup/'>here.</a></center>")
-
   with gr.Row():
     with gr.Column():
       Name_of_the_company = gr.Textbox(placeholder="eg, GOOG / MSFT / AAPL", label="TICKER of the company")
       btn = gr.Button("ANALYSE")
       gr.Markdown("<center><h2>Analysis<h2></center>")
+      gr.Markdown("<center><h3>Inportant Information</h3></center>")
+      info1 = gr.Textbox()
+      gr.Markdown("<h4>Insightful Ratios</h4>")
+      with gr.Row():
+        ratio1 = gr.Textbox(label='Current Ratio')
+        ratio2 = gr.Textbox(label='Quick Ratio')
+        ratio3 = gr.Textbox(label='Short Ratio')
+        ratio4 = gr.Textbox(label='Debt to Equity Ratio')
+
+      gr.Markdown("<center><h3>General Information</h3></center>")
+      with gr.Row():
+        curr_price = gr.Textbox(label='Current Price of Stock')
+        rev_per = gr.Textbox(label='Revenue per Share')
+      vol = gr.Textbox(label='Volume')
+      mar_cap = gr.Textbox(label='Market Cap')
+
       gr.Markdown("<h3>Regression Trends of Price<h3>")
+      with gr.Tab("Overall Trend"):
+        trend_g = gr.LinePlot(visible=False, label='Trend of stock over its lifetime', height=1000, width=1000)
+      with gr.Tab("Seasonal Trends"):
+        Seaso = gr.LinePlot(visible=False,label="This is for one season", height=1000, width=1000)
+      with gr.Tab("Residual Variation"):
+        resid = gr.LinePlot(visible=False, label="Residual Variation over time", height=1000, width=1000)
+
+
       mp = gr.Plot()
       gr.Markdown("<h3>Price over time<h3>")
       with gr.Tab("All Time"):
@@ -134,6 +217,7 @@ with gr.Blocks() as demo:
           Max_year = gr.Textbox(placeholder="The Maximum price for the last year", label='Maximum')
           Min_year = gr.Textbox(placeholder="The Minimum price for the last year", label="Minimum")
       with gr.Tab("Past few Days"):
+
         mp3 = gr.LinePlot(visible=False, label="Past few Days")
         with gr.Row():
           Max_rec = gr.Textbox(placeholder="The Maximum price for the last few days", label='Recent Maximum')
@@ -146,5 +230,7 @@ with gr.Blocks() as demo:
 
 
       btn.click(get_ans, inputs=Name_of_the_company, outputs= [mp,mp1,mp2,mp3, Max_all, Min_all,Max_year, Min_year, Max_rec, Min_rec, Next_day, Next_plot, Next_plot_all, Next_plot_year])
+      btn.click(get_info, inputs=Name_of_the_company, outputs=[info1, ratio1, ratio2, ratio3, ratio4, vol, mar_cap, curr_price, rev_per])
+      btn.click(get_seo, inputs=Name_of_the_company, outputs=[trend_g, Seaso, resid])
 
-demo.launch(inline = False)
+demo.launch(inline=False)