Update app.py

app.py

@@ -1,78 +1,30 @@
-import numpy as np
+import streamlit as st
 import pandas as pd
-import matplotlib.pyplot as plt
-from sklearn.preprocessing import MinMaxScaler
-from keras.models import Sequential
-from keras.layers import Dense, LSTM, Dropout
+from sklearn.linear_model import LinearRegression
 
-data = pd.read_csv('Google_train_data.csv')
-data["Close"] = pd.to_numeric(data.Close, errors='coerce')
-data = data.dropna()
-trainData = data.iloc[:, 4:5].values
+# Load the dataset
+data = pd.read_csv('google_stock.csv')
 
-sc = MinMaxScaler(feature_range=(0, 1))
-trainData = sc.fit_transform(trainData)
+# Split the data into features and target
+X = data[['Open', 'High', 'Low', 'Volume']]
+y = data['Close']
 
-X_train = []
-y_train = []
+# Train the model
+model = LinearRegression()
+model.fit(X, y)
 
-for i in range(60, 1149):  # 60: timestep // 1149: length of the data
-    X_train.append(trainData[i - 60:i, 0])
-    y_train.append(trainData[i, 0])
+# Streamlit app
+st.title('Google Stock Price Prediction')
 
-X_train, y_train = np.array(X_train), np.array(y_train)
-X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
+# 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:')
 
-model = Sequential()
-model.add(LSTM(units=100, return_sequences=True, input_shape=(X_train.shape[1], 1)))
-model.add(Dropout(0.2))
+# Make prediction
+predicted_close = model.predict([[open_price, high_price, low_price, volume]])
 
-model.add(LSTM(units=100, return_sequences=True))
-model.add(Dropout(0.2))
-
-model.add(LSTM(units=100, return_sequences=True))
-model.add(Dropout(0.2))
-
-model.add(LSTM(units=100, return_sequences=False))
-model.add(Dropout(0.2))
-
-model.add(Dense(units=1))
-
-model.compile(optimizer='adam', loss="mean_squared_error")
-hist = model.fit(X_train, y_train, epochs=20, batch_size=32, verbose=2)
-
-plt.plot(hist.history['loss'])
-plt.title('Training model loss')
-plt.ylabel('loss')
-plt.xlabel('epoch')
-plt.legend(['train'], loc='upper left')
-plt.show()
-
-testData = pd.read_csv('Google_test_data.csv')
-testData["Close"] = pd.to_numeric(testData.Close, errors='coerce')
-testData = testData.dropna()
-testData = testData.iloc[:, 4:5]
-y_test = testData.iloc[60:, 0:].values
-
-# input array for the model
-inputClosing = testData.iloc[:, 0:].values
-inputClosing_scaled = sc.transform(inputClosing)
-
-X_test = []
-length = len(testData)
-timestep = 60
-
-for i in range(timestep, length):
-    X_test.append(inputClosing_scaled[i - timestep:i, 0])
-
-X_test = np.array(X_test)
-y_pred = model.predict(X_test)
-predicted_price = sc.inverse_transform(y_pred)
-
-plt.plot(y_test, color='red', label='Actual Stock Price')
-plt.plot(predicted_price, color='green', label='Predicted Stock Price')
-plt.title('Google stock price prediction')
-plt.xlabel('Time')
-plt.ylabel('Stock Price')
-plt.legend()
-plt.show()
+# Display the prediction
+st.subheader('Predicted Close Price')
+st.write(predicted_close[0])