Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+
+def lstm_forecast(file, forecast_months):
+    # Load and preprocess data
+    df = pd.read_csv(file)
+    df['Month'] = pd.to_datetime(df['Month'])
+    df.set_index('Month', inplace=True)
+    df = df.sort_index()
+
+    if 'Spend' not in df.columns:
+        raise ValueError("CSV must contain a 'Spend' column.")
+
+    data = df[['Spend']]
+    
+    # Scale data
+    scaler = MinMaxScaler()
+    scaled_data = scaler.fit_transform(data)
+
+    # Create sequences
+    def create_sequences(data, seq_length):
+        X, y = [], []
+        for i in range(len(data) - seq_length):
+            X.append(data[i:i+seq_length])
+            y.append(data[i+seq_length])
+        return np.array(X), np.array(y)
+
+    seq_length = 12
+    X, y = create_sequences(scaled_data, seq_length)
+    X = X.reshape((X.shape[0], X.shape[1], 1))
+
+    # Build LSTM model
+    model = Sequential([
+        LSTM(64, return_sequences=False, input_shape=(seq_length, 1)),
+        Dense(1)
+    ])
+    model.compile(optimizer='adam', loss='mse')
+    model.fit(X, y, epochs=30, batch_size=1, verbose=0)
+
+    # Forecast future values
+    forecast_input = scaled_data[-seq_length:].reshape((1, seq_length, 1))
+    forecast = []
+
+    for _ in range(forecast_months):
+        pred = model.predict(forecast_input, verbose=0)[0]
+        forecast.append(pred)
+        forecast_input = np.append(forecast_input[:, 1:, :], [[pred]], axis=1)
+
+    forecast = scaler.inverse_transform(forecast)
+
+    # Create forecast dates
+    last_date = df.index[-1]
+    forecast_dates = pd.date_range(start=last_date + pd.DateOffset(months=1), periods=forecast_months, freq='M')
+
+    # Plotting
+    plt.figure(figsize=(12, 6))
+    plt.plot(df.index, df['Spend'], label='Historical Spend', marker='o')
+    plt.plot(forecast_dates, forecast, label='LSTM Forecast', marker='o', color='green')
+    plt.title("Credit Card Spend Forecast with LSTM")
+    plt.xlabel("Month")
+    plt.ylabel("Spend")
+    plt.grid(True)
+    plt.legend()
+
+    return plt.gcf()
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=lstm_forecast,
+    inputs=[
+        gr.File(label="Upload credit_card_spend.csv"),
+        gr.Slider(minimum=6, maximum=24, step=1, value=12, label="Months to Forecast")
+    ],
+    outputs=gr.Plot(label="LSTM Forecast Plot"),
+    title="📊 Credit Card Spend Forecast using LSTM",
+    description="Upload a CSV with 'Month' and 'Spend' columns to forecast future trends using an LSTM model."
+)
+
+interface.launch()