import gradio as gr

def message():
    return "🚧 Under Construction 🚧\n\nCheck back soon!"

demo = gr.Interface(
    fn=message,
    inputs=[],
    outputs=gr.Textbox(label="Status"),
    title="Site Under Construction",
    description="This application is currently being updated. Please check back later!"
)

demo.launch()
# import gradio as gr
# import numpy as np
# import pandas as pd
# import tensorflow as tf
# import joblib
# from tensorflow.keras.models import load_model

# # Load the trained LSTM model
# model = load_model("lstm_gru_model5.h5")

# # Load the MinMaxScaler
# scaler = joblib.load("forex_scaler222.pkl")

# def preprocess_input(data):
#     """Preprocess input data for LSTM model."""
#     if len(data) < 60:
#         mean_value = np.mean(data)  # Compute mean of given prices
#         data = data + [mean_value] * (60 - len(data))  # Fill missing spots

#     data = np.array(data).reshape(1, -1)  # Reshape for MinMaxScaler
#     scaled_data = scaler.transform(data)  # Scale the input
#     return scaled_data.reshape(1, 60, 1)  # Reshape for LSTM



# def predict_forex(prices):
#     """Predict the next forex price based on the input sequence."""
#     try:
#         input_data = [float(price) for price in prices.split(",")]
        
#         # Ensure enough input data
#         if len(input_data) < 60:
#             return "Please provide at least 60 previous forex prices."
        
#         # Use last 60 prices
#         preprocessed_data = preprocess_input(input_data[-60:])
#         prediction = model.predict(preprocessed_data)

#         # Convert back to original scale
#         predicted_price = scaler.inverse_transform(prediction)[0][0]
#         return f"Predicted Next Price: {predicted_price:.5f}"
#     except Exception as e:
#         return f"Error: {str(e)}"


# def batch_predict(file):
#     """Batch prediction for CSV files."""
#     try:
#         df = pd.read_csv(file)
#         if "prices" not in df.columns:
#             return "CSV must have a 'prices' column with historical data."
        
#         df["predictions"] = df["prices"].rolling(window=10).apply(lambda x: predict_forex(",".join(map(str, x))) if len(x) == 10 else None)
#         return df.dropna()
#     except Exception as e:
#         return f"Error: {str(e)}"

# # Gradio UI
# demo = gr.Interface(
#     fn=predict_forex,
#     inputs=gr.Textbox(label="Enter last 10 forex prices (comma-separated)"),
#     outputs=gr.Textbox(label="Predicted Next Price"),
#     title="Forex Price Predictor",
#     description="Enter the last 10 forex prices to predict the next price. Upload CSV for batch predictions.",
#     examples=[
#         ["1.2345,1.2350,1.2360,1.2370,1.2380,1.2390,1.2400,1.2410,1.2420,1.2430"]
#     ],
#     allow_flagging="never"
# )

# batch_demo = gr.Interface(
#     fn=batch_predict,
#     inputs=gr.File(label="Upload CSV"),
#     outputs=gr.Dataframe(label="Predictions"),
#     title="Batch Prediction",
#     description="Upload a CSV with a 'prices' column for batch predictions."
# )

# gr.TabbedInterface([demo, batch_demo], ["Single Prediction", "Batch Prediction"]).launch()