Create app.py

app.py

@@ -0,0 +1,80 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import yfinance as yf
+from keras.models import Sequential
+from sklearn.preprocessing import MinMaxScaler
+from keras.layers.core import Dense,Dropout,Activation
+from keras.layers.recurrent import LSTM
+from datetime import date
+import gradio as gr
+
+def create_dataset(dataset,time_step=15):
+    x_ind,y_dep =[],[]
+    for i in range(len(dataset)-time_step-1):
+        a=dataset[i:(i+time_step),0]
+        x_ind.append(a)
+        y_dep.append(dataset[i+time_step,0])
+    return np.array(x_ind),np.array(y_dep)
+
+def stockprice(stockname,number_of_samples):
+    df_yahoo = yf.download(stockname,start='2020-09-15',end=date.today(),interval = "1h",progress=False,auto_adjust=True)
+    df=df_yahoo
+    df.index.rename('Date', inplace=True)
+    df=df.sort_values(by=['Date'],ignore_index=True)
+    min_max_scaler=MinMaxScaler(feature_range=(0,1))
+    dataset=min_max_scaler.fit_transform(df['Close'].values.reshape(-1,1))
+    train_size=int(len(df)*0.8)
+    test_size=len(df)-train_size
+    Train=dataset[0:train_size,:]
+    Test=dataset[train_size:len(dataset),:]
+    x_train,y_train=create_dataset(Train,time_step=15)
+    x_test,y_test=create_dataset(Test,time_step=15)
+    x_train=np.reshape(x_train,(x_train.shape[0],1,x_train.shape[1]))
+    x_test=np.reshape(x_test,(x_test.shape[0],1,x_test.shape[1]))
+    time_step=15
+    model=Sequential()
+    model.add(LSTM(20,input_shape=(1,time_step)))
+    model.add(Dense(1))
+    model.compile(loss="mean_squared_error",optimizer='adam')
+    model.fit(x_train,y_train,epochs=100,verbose=0)
+    y_pred=model.predict(x_test)
+    y_pred_RNN=min_max_scaler.inverse_transform(y_pred)
+    y_test=np.expand_dims(y_test,axis=1)
+    y_test=min_max_scaler.inverse_transform(y_test)
+    df1=df.drop(["Volume","Open","High","Low"],axis=1)
+    a= int(number_of_samples)*15
+    new_data = df1[-(a+1):-1]
+    last60prices=np.array(new_data)
+    last60prices=last60prices.reshape(-1, 1)
+    X=min_max_scaler.transform(last60prices)
+    TimeSteps=int(15)
+    NumFeatures=int(1)
+    number_of_samples=int(number_of_samples)
+    X=X.reshape(number_of_samples, NumFeatures, TimeSteps)
+    predicted_Price = model.predict(X)
+    predicted_Price = min_max_scaler.inverse_transform(predicted_Price)
+    pred_df=pd.DataFrame(list(map(lambda x: x[0], predicted_Price)),columns=["PREDICTIONS"])
+
+    pred_df.reset_index(inplace=True)
+    pred_df = pred_df.rename(columns = {'index':'HOURS'})
+    
+    plt.figure(figsize=(15, 6))
+    range_history = len(new_data)
+    range_future = list(range(range_history, range_history +len(predicted_Price)))
+    plt.plot(np.arange(range_history), np.array(new_data),label='History')
+    plt.plot(range_future, np.array(predicted_Price),label='Forecasted for RNN')
+    plt.legend(loc='upper right')
+    plt.xlabel('Time step (hour)')
+    plt.ylabel('Stock Price')
+    
+    
+    return pred_df,plt.gcf()
+
+interface = gr.Interface(fn = stockprice, 
+inputs = [gr.inputs.Textbox(lines=1, placeholder="Enter STOCK-TICKER", default="FB", label="STOCKNAME"),
+gr.inputs.Slider(minimum=0, maximum=150, step=1, default=5, label="Number of Sample to Predict")], 
+outputs = ["dataframe","plot"],
+description="LSTM STOCK PREDICTION")
+
+interface.launch()