import uuid
import json
from typing import List, Dict, Any, Tuple
from openpyxl import load_workbook

# XLSX to JSON conversion functions
def detect_table_and_paragraphs(worksheet) -> Tuple[List[List[str]], List[Dict[str, Any]]]:
    data = []
    max_col = worksheet.max_column
    max_row = worksheet.max_row

    for row in worksheet.iter_rows(min_row=1, max_row=max_row, values_only=True):
        if any(cell is not None for cell in row):
            data.append([str(cell).strip() if cell is not None else "" for cell in row])

    table_data = []
    paragraph_texts = []
    in_table = False

    for row in data:
        # Count non-empty cells
        non_empty = [cell for cell in row if cell.strip()]
        if len(non_empty) >= 2 and any(cell.strip().replace('.', '', 1).isdigit() for cell in non_empty):
            in_table = True
            table_data.append(row)
        elif in_table and len(non_empty) >= 2:
            table_data.append(row)
        else:
            paragraph = " ".join(non_empty)
            if paragraph:
                paragraph_texts.append(paragraph)

    # Post-process paragraphs into structured format
    paragraphs = [
        {
            "uid": str(uuid.uuid4()),
            "order": i + 1,
            "text": text
        } for i, text in enumerate(paragraph_texts)
    ]

    return table_data, paragraphs

def xlsx_to_json(file_path) -> Dict[str, Any]:
    workbook = load_workbook(file_path, data_only=True)
    worksheet = workbook.active
    
    table_data, paragraphs = detect_table_and_paragraphs(worksheet)
    
    json_data = {
        "table": {
            "uid": str(uuid.uuid4()),
            "table": table_data
        },
        "paragraphs": paragraphs,
        "questions": []
    }
    
    return json_data

def json_to_jsonl(json_data: Dict[str, Any]) -> str:
    return json.dumps(json_data, ensure_ascii=False)

def json_to_markdown(json_data: Dict[str, Any]) -> str:
    markdown_content = "## Data Tabel\n\n"
    
    # Convert table to markdown
    table = json_data["table"]["table"]
    if table:
        markdown_content += "| " + " | ".join(table[0]) + " |\n"
        markdown_content += "| " + " | ".join(["---"] * len(table[0])) + " |\n"
        for row in table[1:]:
            markdown_content += "| " + " | ".join(row) + " |\n"
    
    # Add paragraphs
    markdown_content += "\n## Konteks/Paragraf\n\n"
    for para in json_data["paragraphs"]:
        markdown_content += f"{para['order']}. {para['text']}\n\n"
    
    return markdown_content

# Prompt creation function
def create_prompt(table_data: Dict[str, Any], question: str) -> str:
    table = table_data["table"]["table"]
    table_md = "\n".join(["| " + " | ".join(row) + " |" for row in table])
    
    text_content = "\n".join([p["text"] for p in table_data["paragraphs"]])
    
    prompt = f"""### Instruction
Given a table and a list of texts in the following, answer the question posed using the following six-step process:
1. Step 1: Predict the type of question being asked. Store this prediction in the variable {{question_type}}.
2. Step 2: Extract the relevant strings or numerical values from the provided table or texts. Store them in {{evidence}}.
3. Step 3: If {{question_type}} is Arithmetic, generate an equation in {{equation}}. Otherwise, put N.A..
4. Step 4: Compute the final answer and store in {{answer}}.
5. Step 5: Predict the answer's scale in {{scale}}. One of: none, percent, thousand, million, billion.
6. Step 6: Based on the {{answer}} and {{question_type}}, generate a short and logical recommendation, business insight, or next action. Store it in {{action}}.

### Table
{table_md}

### Text
{text_content}

### Question
{question}

### Answer"""
    
    return prompt