Update app.py

app.py

@@ -7,6 +7,7 @@ import streamlit as st
 import requests
 from streamlit_lottie import st_lottie
 from keras.models import load_model
+from sklearn.preprocessing import MinMaxScaler
 
 st.set_page_config(
     page_title=" Stonks Trends Prediction", #The page title, shown in the browser tab.(should be Placement Details)
@@ -17,158 +18,216 @@ st.set_page_config(
             'About': 'https://www.linkedin.com/in/harsh-kashyap-79b87b193/', #A markdown string to show in the About dialog. Used my linkedIn id
      }
 )
+def load_lottieurl(url: str):
+        r = requests.get(url) #Make a request to a web page, and return the status code:
+        if r.status_code != 200: #200 is the HTTP status code for "OK", a successful response.
+            return None
+        return r.json() #return the animated gif
 
 from datetime import date
 from datetime import timedelta
 today = date.today()
 # Yesterday date
 yesterday = today - timedelta(days = 1)
-start='2008-01-01'
+start='2010-01-01'
 end=yesterday;
 
+
+if(today.isoweekday()==1):
+    current = yesterday = today - timedelta(days = 2)
+else:  
+    current = yesterday = today - timedelta(days = 1)
+    
+
+
 st.title(":computer:  Stock Market Predictor") #Title heading of the page
 st.markdown("##") 
 
+with st.sidebar:
+    st.title("World Market")
+    st.title("NIFTY")
+    nif = data.DataReader('^NSEI','yahoo',current)['Close']
+    st.header(nif.iloc[0].round(2))
+    st.markdown("""---""")
+    
+    st.title("SENSEX")
+    sen = data.DataReader('^BSESN','yahoo',current)['Close']
+    st.header(sen.iloc[0].round(2))
+    st.markdown("""---""")
+    
+    st.title("S&P FUTURES")
+    sp = data.DataReader('ES=F','yahoo',current)['Close']
+    st.header(sp.iloc[0].round(2))
+    st.markdown("""---""")
+    
+    st.title("GOLD")
+    gold = data.DataReader('GC=F','yahoo',current)['Close']
+    st.header(gold.iloc[0].round(2))
+    st.markdown("""---""")
+    
+    st.title("DOW")
+    dow = data.DataReader('YM=F','yahoo',current)['Close']
+    st.header(dow.iloc[0].round(2))
+    st.markdown("""---""")
+    
+    st.title("NASDAQ")
+    nas = data.DataReader('NQ=F','yahoo',current)['Close']
+    st.header(nas.iloc[0].round(2))
+    st.markdown("""---""")
+    
+    st.title("CRUDE OIL")
+    gold = data.DataReader('CL=F','yahoo',current)['Close']
+    st.header(gold.iloc[0].round(2))
+    st.markdown("""---""")
+
+   
 
 
 st.subheader("Enter Stock Ticker")
 user_input=st.text_input('','AAPL')
-df=data.DataReader(user_input,'yahoo',start,end)
-date=df.index
-
-#Checks if which parameters in hsc_s which is named as branch in sidebar is checked or not and display results accordingly
-def load_lottieurl(url: str):
-    r = requests.get(url) #Make a request to a web page, and return the status code:
-    if r.status_code != 200: #200 is the HTTP status code for "OK", a successful response.
-        return None
-    return r.json() #return the animated gif
-
-left_column, right_column = st.columns(2) #Columns divided into two parts
-with left_column:
-    dashboard1 = load_lottieurl("https://assets10.lottiefiles.com/packages/lf20_kuhijlvx.json") #get the animated gif from file
-    st_lottie(dashboard1, key="Dashboard1", height=400) #change the size to height 400
-with right_column:
-    dashboard2 = load_lottieurl("https://assets10.lottiefiles.com/packages/lf20_i2eyukor.json") #get the animated gif from file
-    st_lottie(dashboard2, key="Dashboard2", height=400) #change the size to height 400
-
-
-st.markdown("""---""")
-#Describing data 
-st.subheader('Data from 2008 to '+str(end.year))
-st.write(df.describe())
-
-st.markdown("""---""")
-#Visualisations
-st.subheader("Closing Price vs Time Chart of "+str(user_input)) #Header
-#plot a line graph
-fig_line = px.line(
-    df,  
-    x = df.index,  
-    y = "Close", 
-    width=1400, #width of the chart
-    height=750, #height of the chart
-)
-#remove the background of the back label
-fig_line.update_layout(
-    plot_bgcolor="rgba(0,0,0,0)",  #rgba means transparent
-    xaxis=(dict(showgrid=False)) #dont show the grid
-)
-#plot the chart
-st.plotly_chart(fig_line, use_container_width=True)
-st.markdown("""---""")
-
-
-st.subheader("Closing Price vs Time with 100MA of "+str(user_input)) #Header
-ma100=df.Close.rolling(100).mean()
-#plot a line graph
-fig_line = px.line( 
-    ma100,
-    x = df.index,  
-    y = ma100, 
-    width=1400, #width of the chart
-    height=750, #height of the chart
-)
-#remove the background of the back label
-fig_line.update_layout(
-    plot_bgcolor="rgba(0,0,0,0)",  #rgba means transparent
-    xaxis=(dict(showgrid=False)) #dont show the grid
-)
-#plot the chart
-st.plotly_chart(fig_line, use_container_width=True)
-st.markdown("""---""")
-
-
-st.subheader("Closing Price vs Time with 1 year moving average of  "+str(user_input)) #Header
-ma365=df.Close.rolling(365).mean()
-#plot a line graph
-fig_line = px.line( 
-    ma365,
-    x = df.index,  
-    y = ma365, 
-    width=1400, #width of the chart
-    height=750, #height of the chart
-)
-#remove the background of the back label
-fig_line.update_layout(
-    plot_bgcolor="rgba(0,0,0,0)",  #rgba means transparent
-    xaxis=(dict(showgrid=False)) #dont show the grid
-)
-#plot the chart
-st.plotly_chart(fig_line, use_container_width=True)
-st.markdown("""---""")
-
 
-
-#Splitting data into training and testing
-
-data_training= pd.DataFrame(df['Close'][0:int(len(df)*0.7)])
-data_testing= pd.DataFrame(df['Close'][int(len(df)*0.7):int(len(df))])
-ydate= date[int(len(df)*0.7):int(len(df))]
-print(data_training.shape)
-print(data_testing.shape)
-
-#normalising data
-from sklearn.preprocessing import MinMaxScaler
-scaler=MinMaxScaler(feature_range=(0,1))
-
-dataset_train = scaler.fit_transform(data_training)
-dataset_test = scaler.transform(data_testing)
-
-def create_dataset(df):
-    x = []
-    y = []
-    for i in range(50, df.shape[0]):
-        x.append(df[i-50:i, 0])
-        y.append(df[i, 0])
-    x = np.array(x)
-    y = np.array(y)
-    return x,y
-
-#Creating dataset
-x_train, y_train = create_dataset(dataset_train)
-x_test, y_test = create_dataset(dataset_test)
-
-x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
-x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
-
-#Load my model
-model=load_model('stock_prediction.h5')
-
-predictions = model.predict(x_test)
-predictions = scaler.inverse_transform(predictions)
-y_test_scaled = scaler.inverse_transform(y_test.reshape(-1, 1))
-
-cydate=ydate[50:]
-st.markdown("""---""")
-st.subheader("Actual Vs Predicted Price Graph for "+user_input)
-fig, ax = plt.subplots(figsize=(16,8))
-ax.set_facecolor('#000041')
-ax.plot(cydate,y_test_scaled, color='red', label='Original price')
-plt.plot(cydate,predictions, color='cyan', label='Predicted price')
-plt.xlabel("Date")
-plt.ylabel("Price")
-plt.title("Stocks for the company "+str(user_input))
-plt.legend()
-st.pyplot(fig)
-
-st.markdown("""---""")
+val=True
+try:
+    df = data.DataReader(user_input,'yahoo',start,end)
+except:
+    val=False
+    st.write("Wrong ticker. Select again")
+    st.markdown("""---""")
+    error = load_lottieurl("https://assets9.lottiefiles.com/packages/lf20_k1rx9jox.json") #get the animated gif from file
+    st_lottie(error, key="Dashboard1", height=400) #change the size to height 400
+
+if val==True:
+    date=df.index
+
+    #Checks if which parameters in hsc_s which is named as branch in sidebar is checked or not and display results accordingly
+    
+
+    left_column, right_column = st.columns(2) #Columns divided into two parts
+    with left_column:
+        dashboard1 = load_lottieurl("https://assets10.lottiefiles.com/packages/lf20_kuhijlvx.json") #get the animated gif from file
+        st_lottie(dashboard1, key="Dashboard1", height=400) #change the size to height 400
+    with right_column:
+        dashboard2 = load_lottieurl("https://assets10.lottiefiles.com/packages/lf20_i2eyukor.json") #get the animated gif from file
+        st_lottie(dashboard2, key="Dashboard2", height=400) #change the size to height 400
+
+
+    st.markdown("""---""")
+    #Describing data 
+    st.subheader('Data from 2008 to '+str(end.year))
+    st.write(df.describe())
+
+    st.markdown("""---""")
+    #Visualisations
+    st.subheader("Closing Price vs Time Chart of "+str(user_input)) #Header
+    #plot a line graph
+    fig_line = px.line(
+        df,  
+        x = df.index,  
+        y = "Close", 
+        width=1400, #width of the chart
+        height=750, #height of the chart
+    )
+    #remove the background of the back label
+    fig_line.update_layout(
+        plot_bgcolor="rgba(0,0,0,0)",  #rgba means transparent
+        xaxis=(dict(showgrid=False)) #dont show the grid
+    )
+    #plot the chart
+    st.plotly_chart(fig_line, use_container_width=True)
+    st.markdown("""---""")
+
+
+    st.subheader("Closing Price vs Time with 100MA of "+str(user_input)) #Header
+    ma100=df.Close.rolling(100).mean()
+    #plot a line graph
+    fig_line = px.line( 
+        ma100,
+        x = df.index,  
+        y = ma100, 
+        width=1400, #width of the chart
+        height=750, #height of the chart
+    )
+    #remove the background of the back label
+    fig_line.update_layout(
+        plot_bgcolor="rgba(0,0,0,0)",  #rgba means transparent
+        xaxis=(dict(showgrid=False)) #dont show the grid
+    )
+    #plot the chart
+    st.plotly_chart(fig_line, use_container_width=True)
+    st.markdown("""---""")
+
+
+    st.subheader("Closing Price vs Time with 1 year moving average of  "+str(user_input)) #Header
+    ma365=df.Close.rolling(365).mean()
+    #plot a line graph
+    fig_line = px.line( 
+        ma365,
+        x = df.index,  
+        y = ma365, 
+        width=1400, #width of the chart
+        height=750, #height of the chart
+    )
+    #remove the background of the back label
+    fig_line.update_layout(
+        plot_bgcolor="rgba(0,0,0,0)",  #rgba means transparent
+        xaxis=(dict(showgrid=False)) #dont show the grid
+    )
+    #plot the chart
+    st.plotly_chart(fig_line, use_container_width=True)
+    st.markdown("""---""")
+
+
+
+    #Splitting data into training and testing
+
+    data_training= pd.DataFrame(df['Close'][0:int(len(df)*0.7)])
+    data_testing= pd.DataFrame(df['Close'][int(len(df)*0.7):int(len(df))])
+    ydate= date[int(len(df)*0.7):int(len(df))]
+    print(data_training.shape)
+    print(data_testing.shape)
+
+    #normalising data
+    
+    scaler=MinMaxScaler(feature_range=(0,1))
+
+    dataset_train = scaler.fit_transform(data_training)
+    dataset_test = scaler.transform(data_testing)
+
+    def create_dataset(df):
+        x = []
+        y = []
+        for i in range(50, df.shape[0]):
+            x.append(df[i-50:i, 0])
+            y.append(df[i, 0])
+        x = np.array(x)
+        y = np.array(y)
+        return x,y
+
+    #Creating dataset
+    x_train, y_train = create_dataset(dataset_train)
+    x_test, y_test = create_dataset(dataset_test)
+
+    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
+    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
+
+    #Load my model
+    model=load_model('stock_prediction.h5')
+
+    predictions = model.predict(x_test)
+    predictions = scaler.inverse_transform(predictions)
+    y_test_scaled = scaler.inverse_transform(y_test.reshape(-1, 1))
+
+    cydate=ydate[50:]
+    st.markdown("""---""")
+    st.subheader("Actual Vs Predicted Price Graph for "+user_input)
+    fig, ax = plt.subplots(figsize=(16,8))
+    ax.set_facecolor('#000041')
+    ax.plot(cydate,y_test_scaled, color='red', label='Original price')
+    plt.plot(cydate,predictions, color='cyan', label='Predicted price')
+    plt.xlabel("Date")
+    plt.ylabel("Price")
+    plt.title("Stocks for the company "+str(user_input))
+    plt.legend()
+    st.pyplot(fig)
+
+    st.markdown("""---""")