Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import yfinance as yf | |
| import datetime as dt | |
| import plotly.graph_objects as go | |
| from sklearn.preprocessing import MinMaxScaler | |
| from tensorflow.keras.models import load_model | |
| import warnings | |
| import requests | |
| from streamlit_lottie import st_lottie | |
| import os | |
| warnings.filterwarnings("ignore") | |
| st.set_page_config(page_title="Real-Time Stock Predictor", layout="wide",page_icon="📈") | |
| PREDICTION_DAYS = 30 | |
| TIME_STEP = 60 | |
| DATA_YEARS = 3 | |
| model = load_model('stock_price_model.h5') | |
| model.make_predict_function() | |
| def load_lottieurl(url): | |
| r = requests.get(url) | |
| if r.status_code != 200: | |
| return None | |
| return r.json() | |
| lottie_stock = load_lottieurl("https://assets10.lottiefiles.com/packages/lf20_1pxqjqps.json") | |
| lottie_loading = load_lottieurl("https://assets2.lottiefiles.com/packages/lf20_usmfx6bp.json") | |
| st.markdown( | |
| """ | |
| <style> | |
| body, .main { | |
| background: linear-gradient(135deg, #232526 0%, #414345 100%) !important; | |
| color: #fff; | |
| font-family: 'Segoe UI', 'Roboto', sans-serif; | |
| } | |
| .stTabs [data-baseweb="tab"] { | |
| font-size: 18px; | |
| font-weight: bold; | |
| color: #00b894; | |
| } | |
| .stTextInput>div>div>input { | |
| background: #222; | |
| color: #fff; | |
| border-radius: 8px; | |
| border: 1px solid #00b894; | |
| } | |
| .stButton>button { | |
| background: linear-gradient(90deg, #00b894 0%, #00cec9 100%); | |
| color: #fff; | |
| font-weight: bold; | |
| border-radius: 8px; | |
| border: none; | |
| transition: 0.3s; | |
| } | |
| .stButton>button:hover { | |
| background: linear-gradient(90deg, #00cec9 0%, #00b894 100%); | |
| color: #222; | |
| } | |
| .card { | |
| background: rgba(34, 49, 63, 0.85); | |
| border-radius: 16px; | |
| padding: 24px; | |
| margin-bottom: 24px; | |
| box-shadow: 0 4px 24px 0 rgba(0,0,0,0.2); | |
| } | |
| .price-up {color: #00ff99; font-weight: bold; font-size: 1.5em;} | |
| .price-down {color: #ff7675; font-weight: bold; font-size: 1.5em;} | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| def preprocess_data(df): | |
| """Process yfinance data""" | |
| df.columns = [col[0] if isinstance(col, tuple) else col for col in df.columns] | |
| df = df.reset_index().rename(columns={'index': 'Date'}) | |
| df = df[['Date', 'High', 'Low', 'Open', 'Close', 'Volume']] | |
| df['Date'] = pd.to_datetime(df['Date']) | |
| df.set_index('Date', inplace=True) | |
| return df | |
| def get_stock_data(stock_symbol): | |
| """Fetch stock data with caching""" | |
| end_date = dt.datetime.now() | |
| start_date = end_date - dt.timedelta(days=365 * DATA_YEARS) | |
| df = yf.download(stock_symbol, start=start_date, end=end_date) | |
| return preprocess_data(df) | |
| def prepare_data(df): | |
| """Prepare data for LSTM prediction""" | |
| scaler = MinMaxScaler() | |
| scaled_data = scaler.fit_transform(df['Close'].values.reshape(-1, 1)) | |
| X = np.array([scaled_data[i:i + TIME_STEP, 0] | |
| for i in range(len(scaled_data) - TIME_STEP - 1)]) | |
| y = scaled_data[TIME_STEP + 1:, 0] | |
| return X.reshape(X.shape[0], TIME_STEP, 1), y, scaler | |
| def predict_future(model, data, scaler): | |
| """Generate future predictions""" | |
| last_data = data[-TIME_STEP:].reshape(1, TIME_STEP, 1) | |
| future_preds = np.zeros(PREDICTION_DAYS, dtype='float32') | |
| for i in range(PREDICTION_DAYS): | |
| next_pred = model.predict(last_data, verbose=0)[0, 0] | |
| future_preds[i] = next_pred | |
| last_data = np.roll(last_data, -1, axis=1) | |
| last_data[0, -1, 0] = next_pred | |
| return scaler.inverse_transform(future_preds.reshape(-1, 1)) | |
| def create_plot(df, pred_data=None, future_data=None, title=""): | |
| """Create interactive Plotly figure""" | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatter( | |
| x=df.index, | |
| y=df['Close'], | |
| name='Actual Price', | |
| line=dict(color='#00b894') | |
| )) | |
| if pred_data is not None: | |
| fig.add_trace(go.Scatter( | |
| x=df.index[TIME_STEP + 1:], | |
| y=pred_data[:, 0], | |
| name='Predicted', | |
| line=dict(color='#fdcb6e') | |
| )) | |
| if future_data is not None: | |
| future_dates = pd.date_range( | |
| start=df.index[-1], | |
| periods=PREDICTION_DAYS + 1 | |
| )[1:] | |
| fig.add_trace(go.Scatter( | |
| x=future_dates, | |
| y=future_data[:, 0], | |
| name='30-Day Forecast', | |
| line=dict(color='#0984e3', dash='dash') | |
| )) | |
| fig.update_layout( | |
| title=title, | |
| template='plotly_dark', | |
| margin=dict(l=20, r=20, t=40, b=20), | |
| font=dict(family='Segoe UI, Roboto, sans-serif', color='#fff'), | |
| plot_bgcolor='rgba(34,49,63,0.95)', | |
| paper_bgcolor='rgba(34,49,63,0.95)' | |
| ) | |
| return fig | |
| def main(): | |
| st_lottie(lottie_stock, height=120, key="header_anim") | |
| st.markdown(""" | |
| <h1 style='text-align: center; color: #00b894; font-size: 3em; font-weight: bold;'>Real-Time Stock Predictor</h1> | |
| <p style='text-align: center; color: #fff; font-size: 1.2em;'>Predict stock prices using LSTM neural networks</p> | |
| """, unsafe_allow_html=True) | |
| col1, col2, col3 = st.columns([1,2,1]) | |
| with col2: | |
| stock_symbol = st.text_input( | |
| "Stock Symbol (Examples: TSLA, AAPL, MSFT, AMZN, GOOG, AEP)", | |
| value="TSLA" | |
| ) | |
| predict_btn = st.button("Predict", use_container_width=True) | |
| if predict_btn and stock_symbol: | |
| with st.spinner('Predicting...'): | |
| st_lottie(lottie_loading, height=80, key="loading_anim") | |
| try: | |
| df = get_stock_data(stock_symbol) | |
| X, y, scaler = prepare_data(df) | |
| y_pred = model.predict(X) | |
| y_pred = scaler.inverse_transform(y_pred) | |
| future_prices = predict_future( | |
| model, | |
| scaler.transform(df['Close'].values.reshape(-1, 1)), | |
| scaler | |
| ) | |
| last_price = df['Close'].iloc[-1] | |
| last_date = df.index[-1].strftime('%Y-%m-%d') | |
| prev_price = df['Close'].iloc[-2] if len(df) > 1 else last_price | |
| price_class = "price-up" if last_price >= prev_price else "price-down" | |
| price_icon = "🔺" if last_price >= prev_price else "🔻" | |
| st.markdown(f""" | |
| <div class='card' style='text-align:center;'> | |
| <span class='{price_class}'>{price_icon} ${last_price:.2f}</span><br> | |
| <span style='color:#b2bec3;'>Last Date: {last_date}</span> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| tab1, tab2, tab3 = st.tabs([ | |
| "📈 Price Prediction", "⏳ 30-Day Forecast", "📊 Technical Indicators"]) | |
| with tab1: | |
| st.plotly_chart(create_plot(df, pred_data=y_pred, title=f"{stock_symbol} Price Prediction"), use_container_width=True) | |
| with tab2: | |
| st.plotly_chart(create_plot(df, future_data=future_prices, title=f"{stock_symbol} 30-Day Forecast"), use_container_width=True) | |
| with tab3: | |
| df['SMA_50'] = df['Close'].rolling(50).mean() | |
| df['SMA_200'] = df['Close'].rolling(200).mean() | |
| tech_fig = go.Figure() | |
| tech_fig.add_trace(go.Scatter(x=df.index, y=df['Close'], name='Price', line=dict(color='#00b894'))) | |
| tech_fig.add_trace(go.Scatter(x=df.index, y=df['SMA_50'], name='50-Day SMA', line=dict(color='#fdcb6e'))) | |
| tech_fig.add_trace(go.Scatter(x=df.index, y=df['SMA_200'], name='200-Day SMA', line=dict(color='#d63031'))) | |
| tech_fig.update_layout(title=f"{stock_symbol} Technical Indicators", template='plotly_dark', | |
| font=dict(family='Segoe UI, Roboto, sans-serif', color='#fff'), | |
| plot_bgcolor='rgba(34,49,63,0.95)', | |
| paper_bgcolor='rgba(34,49,63,0.95)') | |
| st.plotly_chart(tech_fig, use_container_width=True) | |
| except Exception as e: | |
| st.error(f"Prediction failed: {str(e)}") | |
| else: | |
| pass | |
| if __name__ == "__main__": | |
| main() |