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app.py

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+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 gradio as gr
+import warnings
+import os
+
+# Suppress warnings
+warnings.filterwarnings("ignore")
+
+# Constants
+PREDICTION_DAYS = 30
+TIME_STEP = 60
+DATA_YEARS = 3
+
+# Load model
+model = load_model('stock_price_model.h5')
+model.make_predict_function()  # For faster inference
+
+
+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))
+
+    # Create dataset using sliding window
+    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()
+
+    # Main price line
+    fig.add_trace(go.Scatter(
+        x=df.index,
+        y=df['Close'],
+        name='Actual Price',
+        line=dict(color='blue')
+    ))
+
+    # Prediction line
+    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='orange')
+        ))
+
+    # Future prediction
+    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='green')
+        ))
+
+    fig.update_layout(
+        title=title,
+        template='plotly_dark',
+        margin=dict(l=20, r=20, t=40, b=20)
+    )
+    return fig
+
+
+def predict_stock(stock_symbol):
+    """Main prediction function for Gradio"""
+    try:
+        df = get_stock_data(stock_symbol)
+        X, y, scaler = prepare_data(df)
+
+        # Make predictions
+        y_pred = model.predict(X)
+        y_pred = scaler.inverse_transform(y_pred)
+
+        # Future prediction
+        future_prices = predict_future(
+            model,
+            scaler.transform(df['Close'].values.reshape(-1, 1)),
+            scaler
+        )
+
+        # Create plots
+        main_plot = create_plot(
+            df,
+            pred_data=y_pred,
+            title=f"{stock_symbol} Price Prediction"
+        )
+
+        future_plot = create_plot(
+            df,
+            future_data=future_prices,
+            title=f"{stock_symbol} 30-Day Forecast"
+        )
+
+        # Technical indicators
+        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='blue')))
+        tech_fig.add_trace(go.Scatter(
+            x=df.index, y=df['SMA_50'],
+            name='50-Day SMA', line=dict(color='orange')))
+        tech_fig.add_trace(go.Scatter(
+            x=df.index, y=df['SMA_200'],
+            name='200-Day SMA', line=dict(color='red')))
+        tech_fig.update_layout(
+            title=f"{stock_symbol} Technical Indicators",
+            template='plotly_dark'
+        )
+
+        return (
+            f"${df['Close'].iloc[-1]:.2f}",
+            df.index[-1].strftime('%Y-%m-%d'),
+            main_plot,
+            future_plot,
+            tech_fig
+        )
+
+    except Exception as e:
+        raise gr.Error(f"Prediction failed: {str(e)}")
+
+
+# Gradio Interface
+with gr.Blocks(title="Stock Prediction", theme=gr.themes.Default()) as demo:
+    gr.Markdown("# 📈 Real-Time Stock Predictor")
+    gr.Markdown("Predict stock prices using LSTM neural networks")
+
+    with gr.Row():
+        stock_input = gr.Textbox(
+            label="Stock Symbol (Examples: TSLA, AAPL, MSFT, AMZN, GOOG, AEP)",
+            value="TSLA",
+            placeholder="Enter stock symbol (e.g. AAPL, MSFT)"
+        )
+        submit_btn = gr.Button("Predict", variant="primary")
+
+    with gr.Row():
+        with gr.Column():
+            last_price = gr.Textbox(label="Last Price")
+            last_date = gr.Textbox(label="Last Date")
+
+    with gr.Tabs():
+        with gr.Tab("Price Prediction"):
+            main_plot = gr.Plot(label="Price Prediction")
+        with gr.Tab("30-Day Forecast"):
+            future_plot = gr.Plot(label="Future Prediction")
+        with gr.Tab("Technical Indicators"):
+            tech_plot = gr.Plot(label="Technical Analysis")
+
+    submit_btn.click(
+        fn=predict_stock,
+        inputs=stock_input,
+        outputs=[last_price, last_date, main_plot, future_plot, tech_plot]
+    )
+
+# For Hugging Face Spaces
+demo.launch(debug=False)

requirements.txt

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+absl-py==2.2.2
+astunparse==1.6.3
+beautifulsoup4==4.13.4
+blinker==1.9.0
+certifi==2025.4.26
+cffi==1.17.1
+charset-normalizer==3.4.2
+click==8.1.8
+colorama==0.4.6
+contourpy==1.3.2
+curl_cffi==0.10.0
+cycler==0.12.1
+Flask==3.1.0
+flatbuffers==25.2.10
+fonttools==4.57.0
+frozendict==2.4.6
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.71.0
+h5py==3.13.0
+idna==3.10
+itsdangerous==2.2.0
+Jinja2==3.1.6
+joblib==1.5.0
+keras==3.9.2
+kiwisolver==1.4.8
+libclang==18.1.1
+lxml==5.4.0
+Markdown==3.8
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.1
+mdurl==0.1.2
+ml_dtypes==0.5.1
+multitasking==0.0.11
+namex==0.0.9
+narwhals==1.38.0
+numpy==2.1.3
+opt_einsum==3.4.0
+optree==0.15.0
+packaging==25.0
+pandas==2.2.3
+pandas-datareader==0.10.0
+peewee==3.18.1
+pillow==11.2.1
+platformdirs==4.3.7
+plotly==6.0.1
+protobuf==5.29.4
+pycparser==2.22
+Pygments==2.19.1
+pyparsing==3.2.3
+PyQt6==6.7.1
+PyQt6-Qt6==6.7.3
+PyQt6_sip==13.10.0
+python-dateutil==2.9.0.post0
+pytz==2025.2
+pywin32==308
+requests==2.32.3
+rich==14.0.0
+scikit-learn==1.6.1
+scipy==1.15.2
+setuptools==80.3.1
+six==1.17.0
+soupsieve==2.7
+tensorboard==2.19.0
+tensorboard-data-server==0.7.2
+tensorflow==2.19.0
+tensorflow-io-gcs-filesystem==0.31.0
+termcolor==3.1.0
+threadpoolctl==3.6.0
+typing_extensions==4.13.2
+tzdata==2025.2
+urllib3==2.4.0
+Werkzeug==3.1.3
+wheel==0.45.1
+yfinance==0.2.58
+wrapt==1.17.2
+beautifulsoup4==4.13.4
+blinker==1.9.0
+gradio>=3.0

stock_price_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7f88398ddd0d648c749790aefbabfaa5c90b383a5b557011228e300274d797f6
+size 682368