Update app.py

app.py

@@ -1,30 +1,33 @@
 import streamlit as st
 import pandas as pd
+import numpy as np
 from sklearn.linear_model import LinearRegression
+import matplotlib.pyplot as plt
 
-# Load the dataset
-data = pd.read_csv('Google_test_data.csv')
+# Load the stock data from the CSV file
+data = pd.read_csv('google_stock.csv')
 
-# Split the data into features and target
-X = data[['Open', 'High', 'Low', 'Volume']]
+# Prepare the data for prediction
+X = np.arange(len(data)).reshape(-1, 1)
 y = data['Close']
 
-# Train the model
+# Train the linear regression model
 model = LinearRegression()
 model.fit(X, y)
 
-# Streamlit app
-st.title('Google Stock Price Prediction')
+# Predict the stock prices
+predictions = model.predict(X)
 
-# Input form for user to enter values
-open_price = st.number_input('Enter the opening price:')
-high_price = st.number_input('Enter the high price:')
-low_price = st.number_input('Enter the low price:')
-volume = st.number_input('Enter the volume:')
+# Plot the actual and predicted stock prices
+plt.plot(data['Date'], y, label='Actual')
+plt.plot(data['Date'], predictions, label='Predicted')
+plt.xlabel('Date')
+plt.ylabel('Stock Price')
+plt.title('Google Stock Price Prediction')
+plt.legend()
 
-# Make prediction
-predicted_close = model.predict([[open_price, high_price, low_price, volume]])
+# Remove the Streamlit default layout
+st.set_page_config(layout="wide")
 
-# Display the prediction
-st.subheader('Predicted Close Price')
-st.write(predicted_close[0])
+# Display the graph in Streamlit
+st.pyplot(plt)