Update app.py

app.py

@@ -1,3 +1,4 @@
+import streamlit as st 
 import numpy as np 
 import pandas as pd 
 import matplotlib.pyplot as plt
@@ -6,7 +7,7 @@ yf.pdr_override()
 from pandas_datareader import data as pdr
 import tensorflow as tf
 from keras.models import  load_model
-import streamlit as st 
+from sklearn.preprocessing import MinMaxScaler
 
 # Start and the End dates and the stock ticker
 start = '2005-01-01'
@@ -17,91 +18,78 @@ st.title("Stock Market Trend Predictor")
 use_input = st.text_input('Enter stock Ticker', stock_ticker)
 if st.button('Analyze'):
     df = pdr.get_data_yahoo(use_input, start)
-    sorted_df = df.sort_index(ascending=False)
 
-    #describing data 
+    #View Data 
     st.subheader("Data from year 2005 to till date:")
-    st.dataframe(sorted_df,use_container_width=True)
+    st.dataframe(df.sort_index(ascending=False),use_container_width=True)
 
-    #maps 
-
-    st.subheader('closing Price VS Time Chart ')
-    fig = plt.figure(figsize=(10,5))
-    plt.plot(df.Close , color = 'yellow')
+    #Plot Graph for Closing Price Vs the Time
+    st.subheader("Closing Price VS Time Chart:")
+    fig = plt.figure(figsize=(12,6))
+    plt.plot(df.Close,label="Closing Price")
     plt.legend()
     st.pyplot(fig)
 
-    st.subheader('closing Price VS Time Chart with 100 moving Average  ')
-    ma100= df.Close.rolling(100).mean()
-    fig = plt.figure(figsize=(10,5))
-    plt.plot(ma100, color = 'red')
-    plt.plot(df.Close , color = 'yellow')
+    #Plot Graph for Closing Price Vs the Time with 100 Moving Average
+    moving_avg_100 = df.Close.rolling(100).mean()
+    st.subheader("Closing Price VS Time Chart With 100Moving Average:")
+    fig = plt.figure(figsize=(12,6))
+    plt.plot(df.Close, label="Closing Price")
+    plt.plot(moving_avg_100,'red', label="100 Moving Average")
     plt.legend()
     st.pyplot(fig)
 
 
-    st.subheader('closing Price VS Time Chart with 100 & 200 moving Average  ')
-    ma100= df.Close.rolling(100).mean()
-    ma200= df.Close.rolling(200).mean()
+    #Plot Graph for Closing Price Vs the Time with 100 moving Average and 200 Moving Average 
+    moving_avg_200 = df.Close.rolling(200).mean()
+    st.subheader("Closing Price VS Time Chart With 100Moving Average and 200Moving Average:")
     fig = plt.figure(figsize=(10,5))
-    plt.plot(ma100 , color = 'red')
-    plt.plot(ma200, color = 'green')
-    plt.plot(df.Close , color = 'yellow')
+    plt.plot(df.Close, label="Closing Price")
+    plt.plot(moving_avg_100,'red', label="100 Moving Average")
+    plt.plot(moving_avg_200,'green', label="200 Moving Average")
     plt.legend()
     st.pyplot(fig)
 
-
-    #spltting data into train test 
+    #Spliting Data in Training and Testing Data 
     data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])
     data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70):int(len(df))])
 
-    print(' taining ', data_training.shape)
-    print(' testing ', data_testing.shape)
-
-    from sklearn.preprocessing import MinMaxScaler
+    #Scale the training data between 0 and 1
     scaler = MinMaxScaler(feature_range = (0,1))
-
     data_training_array = scaler.fit_transform(data_training)
 
-
-
-
-    #load Model 
-
+    #Load the pre-trained model
     model = load_model('model.h5')
 
-    #testing past 
-    pass_100_days = data_training.tail(100)
-
+    #Testing Past 
+    past_100_days = data_training.tail(100)
     final_df = pd.concat([pass_100_days, data_testing], ignore_index=True)
-
-    input_data = scaler.fit_transform(final_df)
+    input_test_data = scaler.fit_transform(final_df)
 
     x_test = []
     y_test = []
-
-    for i in range(100 , input_data.shape[0]):
-        x_test.append(input_data[i-100:i])
-        y_test.append(input_data[i,0])
+    for i in range(100 , input_test_data.shape[0]):
+        x_test.append(input_test_data[i-100:i])
+        y_test.append(input_test_data[i,0])
     x_test, y_test = np.array(x_test), np.array(y_test)
 
+    #Make Predictions
     y_predicted = model.predict(x_test)
 
+    #Get the scale factor from the scaler and get the original value from the scaled values
     scaler = scaler.scale_
     scale_factor = 1/scaler[0]
     y_predicted = y_predicted*scale_factor
     y_test = y_test*scale_factor
 
-
-    #final graph 
-    def plot_transparent_graph():
-        st.subheader('prediction vs Original')
+    #Plot Final Graph
+    def plot_final_graph():
+        st.subheader("Original Stock Price Vs Predicted Stock Price:")
         fig2 = plt.figure(figsize= (12,6))
-        plt.plot(y_test , 'b', label = 'Original Price')
-        plt.plot(y_predicted , 'r', label = 'prdicted Price')
-        plt.style.use('dark_background')
-        plt.xlabel('time')
-        plt.ylabel('price')
+        plt.plot(y_test, 'blue', label="Original Stock Price")
+        plt.plot(y_predicted, 'red', label="Predicted Stock Price")
+        plt.xlabel('Time')
+        plt.ylabel('Price')
         plt.legend()
         st.pyplot(fig2)
 
@@ -109,11 +97,9 @@ if st.button('Analyze'):
     def main():
         st.title('Stock Price Predicted Analysis')
         
-        # Call the function to plot the transparent graph
-        plot_transparent_graph()
+        #Call the function to plot the final graph
+        plot_final_graph()
 
-        # Other interactive elements and text can be added here as needed
-        # ...
 
     if __name__ == "__main__":
         main()