Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_squared_error
+import tensorflow as tf
+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
+
+def get_ans(inp):
+  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))
+  training_size = int(len(df1) * 0.65)
+  test_size = len(df1) - training_size
+  train_data, test_data = df1[0:training_size, :], df1[training_size:len(df1), :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)
+  time_step = 100
+  X_train, y_train = create_dataset(train_data, time_step)
+  X_test, ytest = create_dataset(test_data, time_step)
+
+  X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
+  X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)
+  model = Sequential()
+  model.add(LSTM(50, return_sequences=True, input_shape=(100, 1)))
+  model.add(LSTM(50, return_sequences=True))
+  model.add(LSTM(50))
+  model.add(Dense(1))
+  model.compile(loss='mean_squared_error', optimizer='adam')
+  model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=2,batch_size=64,verbose=1)
+  train_predict=model.predict(X_train)
+  test_predict=model.predict(X_test)
+  train_predict=scaler.inverse_transform(train_predict)
+  test_predict=scaler.inverse_transform(test_predict)
+  look_back=100
+  trainPredictPlot = np.empty_like(df1)
+  trainPredictPlot[:, :] = np.nan
+  trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict
+  # shift test predictions for plotting
+  testPredictPlot = np.empty_like(df1)
+  testPredictPlot[:, :] = np.nan
+  testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict
+  # plot baseline and predictions
+  plt.plot(scaler.inverse_transform(df1))
+  plt.plot(trainPredictPlot)
+  plt.plot(testPredictPlot)
+
+  x_input=test_data[341:].reshape(1,-1)
+  resize_var = x_input.size
+  temp_input=list(x_input)
+  temp_input=temp_input[0].tolist()
+  lst_output=[]
+  n_steps=100
+  i=0
+  while(i<30):
+
+      if(len(temp_input)>100):
+          #print(temp_input)
+          x_input=np.array(temp_input[1:])
+          # print("{} day input {}".format(i,x_input))
+          x_input=x_input.reshape(1,-1)
+          x_input = x_input.reshape((1, x_input.size, 1))
+          #print(x_input)
+          yhat = model.predict(x_input, verbose=0)
+          # print("{} day output {}".format(i,yhat))
+          temp_input.extend(yhat[0].tolist())
+          temp_input=temp_input[1:]
+          #print(temp_input)
+          lst_output.extend(yhat.tolist())
+          i=i+1
+      else:
+          x_input = x_input.reshape((1, n_steps,1))
+          yhat = model.predict(x_input, verbose=0)
+          # print(yhat[0])
+          temp_input.extend(yhat[0].tolist())
+          # print(len(temp_input))
+          lst_output.extend(yhat.tolist())
+          i=i+1
+
+  day_new=np.arange(1,101)
+  day_pred=np.arange(101,131)
+
+  df3=df1. tolist()
+  df3.extend (lst_output)
+  len_lis = len(lst_output)
+  df3=pd.DataFrame(df3, columns=['Values'])
+  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:])), 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)
+
+
+    with gr.Blocks() as demo:
+  with gr.Row().style(equal_height=True):
+    with gr.Column():
+      gr.Markdown("<center><h1>BI Project<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>")
+  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("<h3>Regression Trends of Price<h3>")
+      mp = gr.Plot()
+      gr.Markdown("<h3>Price over time<h3>")
+      with gr.Tab("All Time"):
+        mp1 = gr.LinePlot(visible=False, label="All time", height=1000, width=1000)
+        with gr.Row():
+          Max_all = gr.Textbox(placeholder="The Maximum price the stock has ever reached", label='Maximum of all time')
+          Min_all = gr.Textbox(placeholder="The Minimum price the stock has ever reached", label="Minimum of all time")
+      with gr.Tab("Past year"):
+        mp2 = gr.LinePlot(visible=False, label="Last year")
+        with gr.Row():
+          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')
+          Min_rec = gr.Textbox(placeholder="The Minimum price for the last few days", label="Recent Minimum")
+      gr.Markdown("<center><h2>Predictive Analysis</h2></center>")
+      Next_day = gr.Textbox(placeholder="Predicted price for tomorrow", label="Predicted price for Tomorrow")
+      Next_plot = gr.LinePlot(visible=False)
+      Next_plot_all = gr.LinePlot(visible=False)
+      Next_plot_year = gr.LinePlot(visible=False)
+
+
+      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])
+
+demo.launch(inline = False)