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app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from statsmodels.tsa.seasonal import seasonal_decompose
+from sklearn.preprocessing import MinMaxScaler
+from keras.preprocessing.sequence import TimeseriesGenerator
+from keras.models import Sequential
+from keras.layers import Dense, LSTM
+import yfinance as yf
+
+
+
+# Function to execute the entire code, handling potential errors
+def forecast_stock(ticker, period, interval):
+    try:
+        # Download data
+        df = yf.download(ticker, period=period, interval=interval)
+        df = df.asfreq('D').fillna(method='ffill')
+
+        # Filter columns
+        ts = df[['Close']]
+
+        # Perform seasonal decomposition
+        decomposition = seasonal_decompose(ts, model='additive')
+
+        # Train/test split
+        ts_train = ts.iloc[:int(ts.size * .8)]
+        ts_test = ts.iloc[int(ts.size * .8):]
+
+        # Normalize the data
+        scaler = MinMaxScaler()
+        scaler.fit(ts_train.values)
+        scaled_ts_train_values = scaler.transform(ts_train.values)
+        scaled_ts_test_values = scaler.transform(ts_test.values)
+
+        # Create LSTM model
+        n_input = 24
+        n_features = 1
+        generator = TimeseriesGenerator(scaled_ts_train_values, scaled_ts_train_values, length=n_input, batch_size=1)
+        model = Sequential()
+        model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))
+        model.add(Dense(1))
+        model.compile(optimizer='adam', loss='mse')
+        model.fit(generator, epochs=50, verbose=0)
+
+        # Make predictions
+        test_predictions = []
+        first_eval_batch = scaled_ts_train_values[-n_input:]
+        current_batch = first_eval_batch.reshape((1, n_input, n_features))
+        for i in range(len(ts_test)):
+            current_pred = model.predict(current_batch, verbose=0)[0]
+            test_predictions.append(current_pred)
+            current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)
+
+        true_predictions = scaler.inverse_transform(test_predictions)
+
+        
+        fig = plt.figure(figsize=(10, 6))
+        plt.plot(ts.index, ts.values, label='Original Data')
+        plt.plot(ts_test.index, true_predictions, label='Forecasted Data')
+        plt.legend()
+        plt.xlabel('Time')
+        plt.ylabel('Value')
+        plt.title('Stock Price Forecast')
+
+        return fig
+
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return gr.Markdown(f"An error occurred: {e}")  # Display error in interface
+
+tickers_info="""
+        **Ticker Examples**
+        ---
+        **Common Stocks**
+
+        - **AAPL:** Apple Inc.
+        - **MSFT:** Microsoft Corp.
+        - **GOOG:** Alphabet Inc. (Google)
+        - **AMZN:** Amazon.com Inc.
+        - **TSLA:** Tesla Inc.
+        - **FB:** Meta Platforms Inc.
+
+        **Indices**
+
+        - **^GSPC:** S&P 500 Index
+        - **^IXIC:** Nasdaq Composite Index
+        - **^DJI:** Dow Jones Industrial Average
+
+        **ETFs ️**
+
+        - **SPY:** SPDR S&P 500 ETF Trust
+        - **QQQ:** Invesco QQQ Trust
+        - **IWM:** iShares Russell 2000 ETF
+        """
+
+
+
+with gr.Blocks() as demo:
+    gr.Interface(
+        forecast_stock,  # Function to execute on submit
+        [
+            gr.Textbox(label="Ticker", placeholder="e.g., AAPL, MSFT, GOOG"),
+            gr.Textbox(label="Period", placeholder="e.g., 1mo, 5y, max"),
+            gr.Textbox(label="Interval", placeholder="e.g., 1d, 1wk, 1h"),
+            
+        ],
+        "plot",  # Output type
+        live=False,  # Disable live updates
+        title="Stock Price Forecast",
+        description="Enter a stock ticker, desired data period, and interval to generate a forecast.",
+        examples = [
+        ["AAPL", "2mo", "1d"],  # Apple stock, data for 2 months, daily intervals
+        ["GOOG", "1y", "1d"],   # Google stock, data for 1 year, daily intervals
+        ["MSFT", "5y", "1wk"],  # Microsoft stock, data for 5 years, weekly intervals
+        ["TSLA", "max", "1h"],  # Tesla stock, maximum available data, hourly intervals
+        ["AMZN", "1y", "1h"],   # Amazon stock, data for 1 year, hourly intervals
+        ["NVDA", "3mo", "1d"],  # NVIDIA stock, data for 3 months, daily intervals
+        ["FB", "1y", "1wk"],    # Meta Platforms (Facebook) stock, data for 1 year, weekly intervals
+        ["JNJ", "2y", "1d"],    # Johnson & Johnson stock, data for 2 years, daily intervals
+        ["BAC", "6mo", "1d"],   # Bank of America stock, data for 6 months, daily intervals
+        ["XOM", "1y", "1wk"],   # Exxon Mobil stock, data for 1 year, weekly intervals
+        ]
+    )
+    with gr.Accordion("Open for More info"):
+        gr.Markdown(tickers_info)
+
+demo.launch()

requirements.txt

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+gradio
+numpy
+pandas
+matplotlib
+statsmodels
+scikit-learn
+keras
+yfinance
+tensorflow