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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_datareader as data
import yfinance as yf
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 )
#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()
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