import os
import json
import threading
import gradio as gr
import statistics
from scipy import stats

DATA_DIR = './storage'
DATA_FILE = os.path.join(DATA_DIR, 'guesses.json')
lock = threading.Lock()

def ensure_data_directory():
    os.makedirs(DATA_DIR, exist_ok=True)

def load_guesses():
    ensure_data_directory()
    if not os.path.exists(DATA_FILE):
        with open(DATA_FILE, 'w') as f:
            json.dump([], f)
    with open(DATA_FILE, 'r') as f:
        return json.load(f)

def save_guesses(guesses):
    with open(DATA_FILE, 'w') as f:
        json.dump(guesses, f)

def add_guess(guess):
    with lock:
        guesses = load_guesses()
        guesses.append(guess)
        save_guesses(guesses)
        
        n = len(guesses)
        average = sum(guesses) / n
        
        if n >= 2:
            # Calculate sample standard deviation
            s = statistics.stdev(guesses)
            # Calculate standard error
            SE = s / (n ** 0.5)
            # Degrees of freedom
            df = n - 1
            # Confidence level (e.g., 95%)
            confidence_level = 0.95
            alpha = 1 - confidence_level
            # Calculate critical t-value
            t_value = stats.t.ppf(1 - alpha/2, df)
            # Calculate margin of error
            ME = t_value * SE
            # Calculate confidence interval
            ci_lower = average - ME
            ci_upper = average + ME
            # Prepare output message
            return (
                f"Your guess has been recorded.\n"
                f"Current average of all guesses: {average:.2f}\n"
                f"95% confidence interval: ({ci_lower:.2f}, {ci_upper:.2f})"
            )
        else:
            # Not enough data to compute confidence interval
            return (
                f"Your guess has been recorded.\n"
                f"Current average of all guesses: {average:.2f}\n"
                f"Not enough data to compute confidence interval."
            )

demo = gr.Interface(
    fn=add_guess,
    inputs=gr.Number(label="Enter your guess"),
    outputs="text",
    title="Collective Guessing Game",
    description="Submit your guess and contribute to the global average!"
)


demo.launch()