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

# Function to apply DOS Rules
def apply_dos_rules(data, lookback):
    numbers = data[['N1', 'N2', 'N3', 'N4', 'N5']].values.flatten()
    unique, counts = np.unique(numbers, return_counts=True)
    freq = dict(zip(unique, counts))

    good_numbers = []
    for num, count in freq.items():
        if count >= 1:
            good_numbers.append(num)

    odds = [n for n in good_numbers if n % 2 == 1]
    evens = [n for n in good_numbers if n % 2 == 0]

    if len(odds) >= 3 and len(evens) >= 2:
        final_set = (
            np.random.choice(odds, 3, replace=False).tolist() +
            np.random.choice(evens, 2, replace=False).tolist()
        )
    elif len(odds) >= 2 and len(evens) >= 3:
        final_set = (
            np.random.choice(odds, 2, replace=False).tolist() +
            np.random.choice(evens, 3, replace=False).tolist()
        )
    else:
        final_set = np.random.choice(good_numbers, 5, replace=False).tolist()

    return sorted(final_set)


# Main function for Gradio (Show only predictions)
def predict_numbers(file, lookback_choice):
    df = pd.read_csv(file)
    df = df.dropna()
    df.columns = ['Date', 'N1', 'N2', 'N3', 'N4', 'N5']
    df['Date'] = pd.to_datetime(df['Date'])
    df = df.sort_values('Date', ascending=False).reset_index(drop=True)

    lookback = len(df) if lookback_choice.lower() == "all" else int(lookback_choice)
    subset = df.head(lookback)

    prediction = apply_dos_rules(subset, lookback)

    return f"🎯 Predicted Set:\n{prediction}"


# Path to your uploaded logo
logo_path = "IMG_4963.jpeg"  # Ensure this image is in the same directory as app.py

# Gradio UI
with gr.Blocks() as demo:
    with gr.Row():
        gr.Image(value=logo_path, label="", show_label=False, elem_id="logo", height=120)
    gr.Markdown("## 🎲 Lottery Prediction using DOS Rules")
    
    with gr.Row():
        file_input = gr.File(label="Upload Lottery CSV", file_types=[".csv"])
        lookback_input = gr.Radio(choices=["12", "15", "20", "All"], label="Select Lookback", value="12")
    
    output = gr.Textbox(label="Prediction Result", lines=5)
    btn = gr.Button("Predict")
    btn.click(predict_numbers, inputs=[file_input, lookback_input], outputs=output)

# Detect if running on Hugging Face
is_hf = os.environ.get("SPACE_ID") is not None

if is_hf:
    demo.launch()
else:
    demo.launch(share=True)