import whisper
from dotenv import load_dotenv
import os
from rapidfuzz import process, fuzz
import pandas as pd
import json
import google.generativeai as genai
import re
from pathlib import Path

# Initialized Modules
from modules.mapping import mapping_employee, mapping_merchant, mapping_product, mapping_unit
from modules.formatting import format_date

# # Load the .env from the parent directory of this file
# env_path = Path(__file__).resolve().parent.parent / ".env"
# load_dotenv(dotenv_path=env_path)

load_dotenv()
# Trancribe Model: Whisper
transcribe_model = whisper.load_model("turbo", download_root= os.getenv("XDG_CACHE_HOME", "/app/.cache"))
# Load the Gemini model
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
MODEL_NAME = os.getenv("MODEL_NAME")
genai.configure(api_key=GEMINI_API_KEY)
# Gemini Model
LLM_model = genai.GenerativeModel(MODEL_NAME)

# Line Split Function
def line():
    print("=" * 30)

# Audio to raw text
def process_audio(audio_path, transcribe_model):
    try:
        transcript = transcribe_model.transcribe(audio_path)
        return transcript["text"]
    except Exception as e:
        print(f"Trancribe failed: {e}")
        return ""
    
# Parsing audio-text
def parse_audio_text(text, extract_model):
    prompt = f"""
    Dưới đây là nội dung hóa đơn bằng tiếng Việt. Hãy trích xuất tên đại lý mua (seller), tên đại lý bán (buyer), tên sản phẩm (product_name), đơn vị tính (unit), số lượng theo từng đơn hàng (quantity), ngày đặt hàng (order_date).

    Văn bản:
    {text}

    Trả về kết quả dạng JSON:
    {{
    "order_1": {{
        "seller": "...",
        "buyer": "...",
        "product_name": "...",
        "unit": "...",
        "quantity": "...",
        "order_date": "..."
    }},
    ...
    }}
    """
    response = extract_model.generate_content(prompt)

    try:
        content = response.text
        # Use regex to extract the JSON part
        match = re.search(r"\{[\s\S]*\}", content)
        if match:
            json_str = match.group(0)
            extracted_json = json.loads(json_str)

            # Format the date string
            for order in extracted_json.values():
                if "order_date" in order:
                    order["order_date"] = format_date(date_str= order["order_date"])

            return list(extracted_json.values())  # List of orders
        else:
            raise ValueError("No valid JSON found in Gemini output")

    except Exception as e:
        print("Failed to parse JSON from LLM response:", e)
        return []
    
# Audio Handling Function
def audio_process(audio_path, order_id):
    print(f"Start process audio file: {os.path.basename(audio_path)}")
    line()

    # Audio to Text
    raw_text = process_audio(
        audio_path=audio_path, 
        transcribe_model=transcribe_model
    )
    print(f"Transcript is done. Transcription: {raw_text}")
    line()

    # Text to JSON
    extracted_information = parse_audio_text(
        text=raw_text,
        extract_model=LLM_model
    )
    print(f"Extracted Information.")
    line()

    # Mapping
    merchant_mapped_data = mapping_merchant(
        information=extracted_information,
        json_path=os.getenv("MERCHANT_JSON_PATH"),
        normalization_rule=os.getenv("NORMALIZATION_RULE_PATH")
    )

    unit_merchant_mapped_data = mapping_unit(
        information=merchant_mapped_data,
        json_path=os.getenv("UNIT_JSON_PATH"),
        normalization_rule=os.getenv("NORMALIZATION_RULE_PATH")
    )

    product_unit_merchant_mapped_data = mapping_product(
        information= unit_merchant_mapped_data, 
        json_path= os.getenv("PRODUCT_JSON_PATH"),
        normalization_rule= os.getenv("NORMALIZATION_RULE_PATH")
        )

    # Skipping employee
    processed_data = product_unit_merchant_mapped_data

    # Assign order id
    for item in processed_data:
        item["order_id"] = order_id

    print(f"Successfully mapped data (merchant + unit).")
    line()

    return processed_data