Update app.py

app.py

@@ -1,109 +1,120 @@
-import streamlit as st
 import numpy as np 
 import pandas as pd 
-import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt 
+import pandas_datareader as data
 import yfinance as yf
-yf.pdr_override()
-from pandas_datareader import data as pdr
-from sklearn.preprocessing import MinMaxScaler
-from keras.models import load_model
-
-start = '2005-01-01'
-end = '2022-12-31'
-
-st.title("Stock Market Trend Predictor")
-
-user_input = st.text_input("Enter the stock ticker", "TATAPOWER.NS")
-df = pdr.get_data_yahoo(user_input, start, end)
-df = df.reset_index()
-df = df.drop(["Date","Adj Close"], axis=1)
-
-st.subheader("Data from year 2005 to 2022:")
-st.write(df.describe())
-
-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)
-
-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)
-
-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=(12,6))
-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)
-
-#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))])
-
-#Scaling
-scaler = MinMaxScaler(feature_range=(0,1))
-data_training_arr = scaler.fit_transform(data_training)
-
-#Split data in x_train and y_train
-x_train = []
-y_train = []
-for i in range(100, data_training_arr.shape[0]):
-    x_train.append(data_training_arr[i-100: i])
-    y_train.append(data_training_arr[i, 0])
-x_train, y_train = np.array(x_train), np.array(y_train)
-
-#Load the model
-model = load_model("keras_model1.h5")
-
-past_100_days = data_training.tail(100)
-final_test_df=pd.concat([past_100_days,data_testing],ignore_index=True)
-print("Final_test_df")
-print(final_test_df)
-input_data = scaler.fit_transform(final_test_df)
-print("input_data")
-print(input_data.shape)
-print(input_data)
-
-#Split data in x_test and y_test
-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])
+import tensorflow as tf
+
+from keras.models import  load_model
+import streamlit as st 
+
+start = '2010-01-01'
+end = '2023-7-30'
+
+
+st.title('Stock Future Predicter')
+
+use_input = st.text_input('Enter stock Ticker', 'AAPL')##############
+
+if st.button('Predict'):
+    df = yf.download(use_input, start ,end )
+
     
-x_test, y_test = np.array(x_test), np.array(y_test)
-print(x_test.shape)
-print(y_test.shape)
-print("y_test")
-print(y_test)
-
-y_predicted = model.predict(x_test)
-print("Predicted")
-print(y_predicted)
-
-sc = scaler.scale_
-print(sc)
-scale_factor = 1/sc[0]
-
-y_predicted = y_predicted * scale_factor
-print(y_predicted)
-y_test = y_test * scale_factor
-print(y_test)
-
-st.subheader("Original Stock Price Vs Predicted Stock Price:")
-fig2 = plt.figure(figsize=(12,6))
-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)
+    #describing data 
+    st.subheader('Data From 2010-2023')
+    st.write(df.describe())
+
+    #maps 
+
+    st.subheader('closing Price VS Time Chart ')
+    fig = plt.figure(figsize=(10,5))
+    plt.plot(df.Close , color = 'yellow')
+    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')
+    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()
+    fig = plt.figure(figsize=(10,5))
+    plt.plot(ma100 , color = 'red')
+    plt.plot(ma200, color = 'green')
+    plt.plot(df.Close , color = 'yellow')
+    plt.legend()
+    st.pyplot(fig)
+
+
+    #spltting data into train test 
+    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
+    scaler = MinMaxScaler(feature_range = (0,1))
+
+    data_training_array = scaler.fit_transform(data_training)
+
+
+
+
+    #load Model 
+
+    model = load_model('model.h5')
+
+    #testing past 
+    pass_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)
+
+    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])
+    x_test, y_test = np.array(x_test), np.array(y_test)
+
+    y_predicted = model.predict(x_test)
+
+    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')
+        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.legend()
+        st.pyplot(fig2)
+
+
+    def main():
+        st.title('Stock Price Predicted Analysis')
+        
+        # Call the function to plot the transparent graph
+        plot_transparent_graph()
+
+        # Other interactive elements and text can be added here as needed
+        # ...
+
+    if __name__ == "__main__":
+        main()