import pandas as pd
import gradio as gr
import numpy as np

# Load dataset
DATA_PATH = "AAPL_stock_data_finalversion (1).csv"
data = pd.read_csv(DATA_PATH)

# Ensure the dataset has the required columns
data['Date'] = pd.to_datetime(data['Date'])
data['DateNumeric'] = data['Date'].map(pd.Timestamp.toordinal)

# Define prediction function
def stock_analysis(start_date, end_date):
    try:
        # Filter data by date range
        filtered_data = data[(data['Date'] >= pd.to_datetime(start_date)) & (data['Date'] <= pd.to_datetime(end_date))]

        if filtered_data.empty:
            return "No data available for the given date range.", None

        # Prepare data for forecasting
        dates_numeric = filtered_data['DateNumeric'].values
        prices = filtered_data['Close'].values

        # Simple linear trend for predictions
        slope, intercept = np.polyfit(dates_numeric, prices, 1)

        # Predict next 30 business days
        last_date = filtered_data['Date'].iloc[-1]
        future_dates = pd.date_range(last_date, periods=30, freq='B')
        future_dates_numeric = future_dates.map(pd.Timestamp.toordinal)
        future_predictions = slope * future_dates_numeric + intercept

        # Create DataFrame for predictions
        future_df = pd.DataFrame({
            'Date': future_dates,
            'Predicted Close Price': future_predictions
        })

        return "Analysis completed!", future_df

    except Exception as e:
        return f"An error occurred: {str(e)}", None

# Define Gradio app
with gr.Blocks() as app:
    gr.Markdown("# Stock Price Prediction App")

    with gr.Row():
        start_date_input = gr.Textbox(label="Start Date", placeholder="YYYY-MM-DD")
        end_date_input = gr.Textbox(label="End Date", placeholder="YYYY-MM-DD")

    analyze_button = gr.Button("Analyze")

    output_message = gr.Textbox(label="Message", interactive=False)
    prediction_table = gr.Dataframe(label="Future Predictions")

    analyze_button.click(
        stock_analysis,
        inputs=[start_date_input, end_date_input],
        outputs=[output_message, prediction_table]
    )

# Launch the app
app.launch()