import matplotlib.pyplot as plt
from LLM_openai import client, expense_classifier
import datetime
import base64
from io import BytesIO
import re
import json
import matplotlib.dates as mdates

import pandas as pd
import matplotlib.pyplot as plt



def create_plot(x, y):
    # Set up the figure and axis with a dark background
    fig, ax = plt.subplots(figsize=(10, 6), facecolor='#2E3440')  # Dark background
    ax.set_facecolor('#2E3440')  # Match the axis background to the figure

    # Plot the line with a vibrant color
    ax.plot(x, y, marker='o', color='#88C0D0', linewidth=2, markersize=8, label='Expenses')  # Light blue line

    # Initialize variables for labeling
    last_labeled_y = y.iloc[0]  # Start with the first point's y-value
    indices_to_label = [0]  # Always label the first point

    # Loop through the data starting from the second point
    for i in range(1, len(y)):
        if abs(y.iloc[i] - last_labeled_y) >= 15:  # Check if the difference is >= 10
            indices_to_label.append(i)  # Add the index to the list
            last_labeled_y = y.iloc[i]  # Update the last labeled y-value

    # Always include the last point if it's not already labeled
    if len(y) - 1 not in indices_to_label:
        indices_to_label.append(len(y) - 1)

    # Label the selected points with a contrasting color
    for i in indices_to_label:
        ax.text(x.iloc[i], y.iloc[i], f'{y.iloc[i]:.1f}', fontsize=10, color='#ECEFF4', 
                ha='left', va='bottom', bbox=dict(facecolor='#3B4252', alpha=0.8, edgecolor='none'))

    # Customize the axis labels and title
    ax.set_xlabel('Money', fontsize=12, color='#ECEFF4')  # Light gray text
    ax.set_ylabel('Expenses', fontsize=12, color='#ECEFF4')
    ax.set_title('Daily Expenses', fontsize=14, color='#ECEFF4', pad=20)

    # Customize the ticks and grid
    ax.tick_params(axis='both', colors='#ECEFF4')  # Light gray ticks
    ax.set_xticks(x[::5])  # Show every 5th day
    ax.set_xticklabels(x[::5], rotation=45, ha='right', color='#ECEFF4')
    ax.grid(color='#4C566A', linestyle='--', linewidth=0.5, alpha=0.7)  # Subtle grid lines

    # Add a legend
    #ax.legend(loc='upper left', facecolor='#3B4252', edgecolor='none', fontsize=10, labelcolor='#ECEFF4')

    # Adjust layout for better spacing
    plt.tight_layout()
    return fig


# def create_barplot(x, y, xlabel,ylabel, title):
#     fig, ax = plt.subplots()
#     ax.bar(x, y)
#     for i in range(len(x)):
#         ax.text(x[i], y[i], f'{y[i]:.2f}', fontsize=10, ha='left', va='bottom')
#     ax.set_xlabel(xlabel)
#     ax.set_ylabel(ylabel)
#     ax.set_title(title)
#     plt.xticks(rotation=30)
#     return fig

import matplotlib.pyplot as plt

def create_barplot(x, y, xlabel, ylabel, title):
    # Set up the figure and axis with a dark background
    fig, ax = plt.subplots(figsize=(10, 6), facecolor='#2E3440')  # Dark background
    ax.set_facecolor('#2E3440')  # Match the axis background to the figure

    # Create the bar plot with a vibrant color
    bars = ax.bar(x, y, color='#88C0D0', edgecolor='#4C566A', linewidth=1)  # Light blue bars with a subtle border

    # Add text labels on top of each bar
    for i in range(len(x)):
        ax.text(
            x[i], y[i], f'{y[i]:.2f}', 
            fontsize=10, color='#ECEFF4',  # Light gray text
            ha='center', va='bottom',  # Center the text horizontally and place it above the bar
            bbox=dict(facecolor='#3B4252', alpha=0.8, edgecolor='none')  # Semi-transparent background
        )

    # Customize the axis labels and title
    ax.set_xlabel(xlabel, fontsize=12, color='#ECEFF4')  # Light gray text
    ax.set_ylabel(ylabel, fontsize=12, color='#ECEFF4')
    ax.set_title(title, fontsize=14, color='#ECEFF4', pad=20)

    # Customize the ticks and grid
    ax.tick_params(axis='both', colors='#ECEFF4')  # Light gray ticks
    plt.xticks(rotation=30, color='#ECEFF4')  # Rotate x-axis labels for better readability
    ax.grid(color='#4C566A', linestyle='--', linewidth=0.5, alpha=0.7, axis='y')  # Subtle horizontal grid lines

    # Remove spines (borders) for a cleaner look
    for spine in ax.spines.values():
        spine.set_visible(False)

    # Adjust layout for better spacing
    plt.tight_layout()
    return fig


####
##OCR functions



def pil_to_base64(pil_img):
    img_buffer = BytesIO()
    pil_img.save(img_buffer, format='JPEG')
    byte_data = img_buffer.getvalue()
    base64_str = base64.b64encode(byte_data).decode("utf-8")
    return base64_str

def js_to_prefere_the_back_camera_of_mobilephones():
    custom_html = """
    <script>
    const originalGetUserMedia = navigator.mediaDevices.getUserMedia.bind(navigator.mediaDevices);
    
    navigator.mediaDevices.getUserMedia = (constraints) => {
      if (!constraints.video.facingMode) {
        constraints.video.facingMode = {ideal: "environment"};
      }
      return originalGetUserMedia(constraints);
    };
    </script>
    """
    return custom_html

def result_cleaner(text):
    pattern = r'\{[^}]*\}'
    match = re.search(pattern, text)
    match_string=match[0]
    json_dict=json.loads(match_string)


    return json_dict