# libraries
from flask import Flask, render_template, request, redirect, url_for, flash, session, send_from_directory
import os
import logging
from utility.utils import extract_text_from_images, Data_Extractor, json_to_llm_str, process_extracted_text, process_resume_data
from backup.backup import NER_Model
from paddleocr import PaddleOCR

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    handlers=[
        logging.StreamHandler()  # Remove FileHandler and log only to the console
    ]
)

# Flask App
app = Flask(__name__)
app.secret_key = 'your_secret_key'
app.config['UPLOAD_FOLDER'] = 'uploads/'
app.config['RESULT_FOLDER'] = 'uploads/'

UPLOAD_FOLDER = 'static/uploads/'
RESULT_FOLDER = 'static/results/'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(RESULT_FOLDER, exist_ok=True)

if not os.path.exists(app.config['UPLOAD_FOLDER']):
    os.makedirs(app.config['UPLOAD_FOLDER'])

if not os.path.exists(app.config['RESULT_FOLDER']):
    os.makedirs(app.config['RESULT_FOLDER'])

# Set the PaddleOCR home directory to a writable location
os.environ['PADDLEOCR_HOME'] = os.path.join(app.config['UPLOAD_FOLDER'], '.paddleocr')  # Change made here

@app.route('/')
def index():
    uploaded_files = session.get('uploaded_files', [])
    logging.info(f"Accessed index page, uploaded files: {uploaded_files}")
    return render_template('index.html', uploaded_files=uploaded_files)

@app.route('/upload', methods=['POST'])
def upload_file():
    if 'files' not in request.files:
        flash('No file part')
        logging.warning("No file part found in the request")
        return redirect(request.url)

    files = request.files.getlist('files')  # Get multiple files
    if not files or all(file.filename == '' for file in files):
        flash('No selected files')
        logging.warning("No files selected for upload")
        return redirect(request.url)

    uploaded_files = []
    for file in files:
        if file:
            filename = file.filename
            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
            uploaded_files.append(filename)
            logging.info(f"Uploaded file: {filename}")

    session['uploaded_files'] = uploaded_files
    flash('Files successfully uploaded')
    logging.info(f"Files successfully uploaded: {uploaded_files}")
    return redirect(url_for('index'))

@app.route('/remove_file')
def remove_file():
    uploaded_files = session.get('uploaded_files', [])
    for filename in uploaded_files:
        file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        if os.path.exists(file_path):  # Check if the file exists before trying to remove it
            os.remove(file_path)
            logging.info(f"Removed file: {filename}")
        else:
            logging.warning(f"File not found for removal: {filename}")

    session.pop('uploaded_files', None)
    flash('Files successfully removed')
    logging.info("All uploaded files removed")
    return redirect(url_for('index'))

@app.route('/process', methods=['POST'])
def process_file():
    uploaded_files = session.get('uploaded_files', [])
    if not uploaded_files:
        flash('No files selected for processing')
        logging.warning("No files selected for processing")
        return redirect(url_for('index'))

    # Create a list of file paths for the extracted text function
    file_paths = [os.path.join(app.config['UPLOAD_FOLDER'], filename) for filename in uploaded_files]
    logging.info(f"Processing files: {file_paths}")

    extracted_text = {}  # Initialize extracted_text  # Change made here
    try:
        # Extract text from all images
        extracted_text, processed_Img = extract_text_from_images(file_paths, RESULT_FOLDER)
        logging.info(f"Extracted text: {extracted_text}")
        logging.info(f"Processed images: {processed_Img}")

        # Call the Gemma model API and get the professional data
        llmText = json_to_llm_str(extracted_text)
        logging.info(f"LLM text: {llmText}")
        
        LLMdata = Data_Extractor(llmText)
        logging.info(f"LLM data: {LLMdata}")

    except Exception as e:
        logging.error(f"Error during LLM processing: {e}")
        logging.info("Running backup model...")
        
        # Run the backup model in case of an exception
        if extracted_text:  # Ensure extracted_text has a value before using it  # Change made here
            text = json_to_llm_str(extracted_text)
            LLMdata = NER_Model(text)
            logging.info(f"NER model data: {LLMdata}")
        else:
            logging.warning("No extracted text available for backup model")  # Change made here

    cont_data = process_extracted_text(extracted_text)
    logging.info(f"Contextual data: {cont_data}")
    
    # Storing the parsed results
    processed_data = process_resume_data(LLMdata, cont_data, extracted_text)
    logging.info(f"Processed data: {processed_data}")

    session['processed_data'] = processed_data
    session['processed_Img'] = processed_Img
    flash('Data processed and analyzed successfully')
    logging.info("Data processed and analyzed successfully")
    return redirect(url_for('result'))

@app.route('/result')
def result():
    processed_data = session.get('processed_data', {})
    processed_Img = session.get('processed_Img', {})
    logging.info(f"Displaying results: Data - {processed_data}, Images - {processed_Img}")
    return render_template('result.html', data=processed_data, Img=processed_Img)

@app.route('/uploads/<filename>')
def uploaded_file(filename):
    logging.info(f"Serving file: {filename}")
    return send_from_directory(app.config['UPLOAD_FOLDER'], filename)

if __name__ == '__main__':
    logging.info("Starting Flask app")
    app.run(debug=True)