import os
import math
import asyncio

from loguru import logger

from .r2_utils import (
    upload_text_to_minio,
    upload_dataframe_to_minio,
)
from .common_utils import escape_csv_field


BUCKET_NAME = "ai-scientist"


# Function to check relevance and obtain keywords as reason
async def is_relevant(title, abstract, topic, direction, chat_func):
    """ 
    Check if a paper is relevant to a topic and obtain keywords as reason.
    
    Args:
        title (str): Title of the paper.
        abstract (str): Abstract of the paper.
        topic (str): Topic to check relevance against.
        direction (str): Direction to check relevance against.
        chat_func (function): Function to call the chat model.

    Returns:
        bool: True if the paper is relevant, False otherwise.
        str: Keywords that indicate relevance.
    
    """
    relevance_prompt = (
        f"You are an academic expert in {topic}. Identify if the following paper is "
        f"related to '{direction}' and list only the main keywords that indicate relevance:\n\n"
        f"Title: {title}\nAbstract: {abstract}\n\n"
        "Answer format:\n"
        "Relevance: True or False\n"
        "Keywords: [Comma-separated keywords]"
    )
    response = await chat_func(relevance_prompt)
    if response is None:
        return False, "Relevance check unavailable due to server error."

    try:
        response_text = response.choices[0].message.content
        relevance = "True" in response_text
        keywords = response_text.split(
            "Keywords:")[-1].strip() if "Keywords:" in response_text else ""
        return relevance, keywords
    except AttributeError:
        logger.error("Error in chat_func response format:", response)
        return False, "Relevance check failed"


# Modified summarize_abstract function with error handling for failed completion requests
async def summarize_abstract(title, abstract, first_author, chat_func):
    """
    Summarize the abstract of a research paper.
    
    Args:
        title (str): Title of the paper.
        abstract (str): Abstract of the paper.
        first_author (str): Name of the first author.
        chat_func (function): Function to call the chat model.

    Returns:
        str: Summary of the abstract.
    
    """
    formatted_author = reformat_author_name(first_author)
    summary_prompt = (
        f"Write a concise, high-level summary in 2-3 sentences, highlighting the study's "
        f"purpose, specific methodology, main findings, and significance. Avoid generalizing "
        f"or replacing specific method names or entities with vague language. Retain concrete terms "
        f"and clear descriptions of methodology and findings.\n\n"
        f"Title: {title}\nAbstract: {abstract}\n\n"
        f"Summary by {formatted_author} et al.:"
    )

    response = await chat_func(summary_prompt)
    if response is None:
        return "Summary unavailable due to server error."

    try:
        result = response.choices[0].message.content
        result_words = result.split()
        summary = " ".join(result_words)
        return summary
    except AttributeError:
        logger.error("Error in chat_func response format:", response)
        return "Summary unavailable"


# Function to reformat first author name
def reformat_author_name(author_name):
    """
    Reformat the first author name by removing commas.
    
    Args:
        author_name (str): Name of the first author.
        
    Returns:
        str: Reformatted name of the first author.
        
    """
    try:
        return author_name.replace(",", "")
    except AttributeError:
        return "Unknown Author"


# Function to generate 3-5 hierarchical subheadings related to the main topic
async def generate_subheadings(
    relevant_papers_df, main_topic, 
    uuid, customer_name, model_name,
    chat_func
):
    """
    Generate 3-5 hierarchical subheadings related to the main topic based on the summaries of relevant papers.
    
    Args:
        relevant_papers_df: DataFrame containing relevant papers.
        main_topic: Main topic of the research.
        chat_func: Function to send chat messages to the chatbot.
        
    Returns:
        List[str]: List of generated subheadings.
    
    """
    summaries = " ".join(relevant_papers_df['Summary'].tolist())
    prompt = (
        f"The main topic is '{main_topic}'. Based on this topic and the following summaries from relevant research papers, "
        "generate 3-5 hierarchical subheadings that progressively explore the topic. Begin with broader subheadings and "
        "move towards more specific themes, avoiding overlap in scope or content. Subheadings should be distinct and arranged "
        "in a logical order suitable for a structured review.\n\n"
        f"Summaries:\n{summaries}\n\n"
        "Output format:\n- Subheading 1\n- Subheading 2\n- Subheading 3\n..."
    )
    response = await chat_func(prompt)
    subheadings = response.choices[0].message.content.strip().splitlines()
    logger.info("Generated Subheadings:\n" + "\n".join(subheadings))
    
    output_filename = f"{customer_name}/{uuid}/{model_name}/generated_subheadings.txt"
    await upload_text_to_minio(
        bucket_name=BUCKET_NAME,
        object_name=output_filename,
        file_content="\n".join(subheadings)
    )
    logger.info(f"Subheadings saved to {output_filename}")
    return subheadings


# Function to assign summaries to subheadings with minimum allocation of references per subheading
async def assign_subheadings_to_summaries(
    relevant_papers_df, 
    subheadings, 
    uuid, customer_name, model_name,
    chat_func
):
    """
    Assign summaries to subheadings with minimum allocation of references per subheading.
    
    Args:
        relevant_papers_df: DataFrame containing relevant papers.
        subheadings: List of subheadings.
        uuid: Unique identifier for the task.
        customer_name: Name of the customer.
        chat_func: Function to send chat messages to the chatbot.

    Returns:
        DataFrame with assigned subheadings.
    
    """
    total_papers = len(relevant_papers_df)
    min_papers_per_subheading = math.ceil(
        total_papers / (len(subheadings) + 1))

    assigned_subheadings = []
    prompts = []
    for summary in relevant_papers_df['Summary']:
        prompt = (
            "Given the following subheadings and a research paper summary, determine the most appropriate subheading "
            "for this summary. Each subheading should cover a unique aspect of the main topic without overlap. "
            "Select the best-fitting subheading based on thematic relevance and coherence with similar studies.\n\n"
            f"Subheadings:\n{subheadings}\n\n"
            f"Summary:\n{summary}\n\n"
            "Output format:\nSubheading: [Chosen subheading]"
        )
        prompts.append(prompt)
    responses = await asyncio.gather(
        *(chat_func(prompt) for prompt in prompts)
    )
    for response in responses:
        assigned_subheading = response.choices[0].message.content.split(": ")[1]
        assigned_subheadings.append(assigned_subheading)

    relevant_papers_df['Assigned Subheading'] = assigned_subheadings

    # Ensure minimum papers per subheading
    counts = relevant_papers_df['Assigned Subheading'].value_counts().to_dict()
    for subheading in subheadings:
        if counts.get(subheading, 0) < min_papers_per_subheading:
            extra_summaries = relevant_papers_df[relevant_papers_df['Assigned Subheading'] != subheading].sample(
                min_papers_per_subheading - counts.get(subheading, 0)
            )
            relevant_papers_df.loc[extra_summaries.index,
                                   'Assigned Subheading'] = subheading

    prefix = f"{customer_name}/{uuid}/{model_name}/"
    output_dir = prefix

    csv_filename = os.path.join(output_dir, f"assigned_subheadings.csv")

    # relevant_papers_df.to_csv(csv_filename, index=False, encoding='utf-8')
    await upload_dataframe_to_minio(
        bucket_name=BUCKET_NAME,
        object_name=csv_filename,
        df=relevant_papers_df,
    )

    logger.info(f"Assigned subheadings saved to {csv_filename}")
    logger.info(f"Found {len(relevant_papers_df)} related papers")

    return relevant_papers_df


# Function to create expanded paragraphs with required reference count and consistent reference indexing
async def create_paragraphs_by_subheading(
    relevant_papers_df, subheadings, main_topic, 
    uuid, customer_name, model_name,
    chat_func
):
    """
    Create expanded paragraphs by subheading with required reference count and consistent reference indexing.
    
    Args:
        relevant_papers_df (pd.DataFrame): DataFrame containing relevant papers and their summaries.
        subheadings (list): List of subheadings for the review paper.
        main_topic (str): Main topic of the review paper.
        uuid (str): UUID of the task.
        customer_name (str): Name of the customer.
        chat_func (function): Function to send chat messages to the chatbot.
        
    Returns:
        list: List of paragraphs with subheadings and consistent reference indexing.
    
    """
    paragraphs = []

    # Introduction
    intro_prompt = (
        f"Write a concise and advanced introductory paragraph for a scientific review paper on '{main_topic}'. "
        "Introduce the topic, its importance, and the scope of the review. The introduction should provide a logical "
        "setup for the following subheadings.\n\n"
        "Output format:\n[Write introduction here]"
    )
    intro_response = await chat_func(intro_prompt)
    intro_paragraph = intro_response.choices[0].message.content.strip()
    paragraphs.append(f"**Introduction**\n{intro_paragraph}\n")

    # Body paragraphs based on subheadings with consistent reference numbering
    reference_map = {}
    used_references = []
    total_papers = len(relevant_papers_df)
    min_papers_per_subheading = math.ceil(
        total_papers / (len(subheadings) + 1))
    ref_counter = 1

    paragraph_prompts = []
    for subheading in subheadings:
        relevant_summaries = relevant_papers_df[relevant_papers_df['Assigned Subheading'] == subheading]

        new_references = []
        summaries_text = []
        for idx, (summary, title, author, pub_date) in relevant_summaries[['Summary', 'Title', 'First Author', 'Publication Date']].iterrows():
            if title not in reference_map:
                reference_map[title] = ref_counter
                ref_counter += 1
            ref_index = reference_map[title]
            summaries_text.append(f"{summary} [Ref: {ref_index}]")
            new_references.append((title, author, pub_date))

        # Compose prompt to generate an extended paragraph with at least 800 words
        paragraph_prompt = (
            f"Write an 800-word thematic and critical paragraph under the subheading '{subheading}' for a scientific review on '{main_topic}'. "
            f"Combine the following summaries into a coherent, well-structured paragraph discussing the studies’ objectives, findings, "
            "and methodologies. Use advanced academic language, include in-text citations in the format [Ref: number], and avoid repeating "
            "content from previous sections. Provide critical insights and comparative analysis where relevant.\n\n"
            f"Summaries:\n{' '.join(summaries_text)}\n\n"
            "Output format:\n[Write paragraph here]"
        )

        paragraph_prompts.append(paragraph_prompt)
        used_references.extend(new_references)

    paragraph_responses = await asyncio.gather(
        *(chat_func(para_prompt)
          for para_prompt in paragraph_prompts)
    )
    for subheading, paragraph_response in \
            zip(subheadings, paragraph_responses):
        paragraph = f"**{subheading}**\n{paragraph_response.choices[0].message.content.strip()}\n"
        paragraphs.append(paragraph)

    # Conclusion
    conclusion_prompt = (
        f"Write a concluding paragraph for a scientific review on '{main_topic}'. Summarize the main points discussed in the previous sections, "
        "highlight the significance of the research, and suggest possible future directions or applications.\n\n"
        "Output format:\n[Write conclusion here]"
    )
    conclusion_response = await chat_func(conclusion_prompt)
    conclusion_paragraph = conclusion_response.choices[0].message.content.strip()
    paragraphs.append(f"**Conclusion**\n{conclusion_paragraph}\n")

    # References section (only used references)
    references = "\n".join(
        [f"[Ref: {reference_map[title]}] {title}, {author}, {pub_date}" 
         for title, author, pub_date in used_references]
    )
    paragraphs.append(f"**References**\n{references}")

    # Compile paragraphs into final content
    final_content = "\n\n".join(paragraphs)

    # Save grouped summaries to CSV with customer_name and current date
    prefix = f"{customer_name}/{uuid}/{model_name}/"
    output_dir = prefix

    csv_filename = os.path.join(output_dir, f"grouped_summaries.csv")
    output_filename = os.path.join(output_dir, f"review_non_refined.txt")
    # Prepare data for CSV
    grouped_data = relevant_papers_df[['Assigned Subheading', 'Summary']]
    # grouped_data.to_csv(csv_filename, index=False, encoding='utf-8')
    await upload_dataframe_to_minio(
        bucket_name=BUCKET_NAME,
        object_name=csv_filename,
        df=grouped_data
    )
    
    await upload_text_to_minio(
        bucket_name=BUCKET_NAME,
        object_name=output_filename,
        file_content=final_content
    )

    logger.info(f"\nGrouped summaries saved to {csv_filename}")
    logger.info(f"Non-refined review saved to {output_filename}")
    return final_content


# Function to enhance language and readability to meet Nature journal style
async def enhance_language_readability(
    content, 
    uuid, customer_name, model_name,
    chat_func
):
    """
    Enhance the language and readability of the given content to meet the style of the *Nature* journal.
    
    Args:
        content (str): The content to enhance.
        chat_func (function): The function to use for the chat completion.
        
    Returns:
        str: The enhanced content.
    
    """
    # Separate sections based on paragraph breaks
    sections = content.split("\n\n")
    enhanced_sections = []
    prompts = []
    for section in sections:
        prompt = (
            "Enhance the following text to align with the writing style of *Nature* journal. Refine language to be sophisticated and objective, "
            "using advanced vocabulary and a factual tone. Ensure a high level of lexical diversity and rhythm, with alternating sentence lengths "
            "and varied structures for readability. Avoid emotional, speculative, or conversational language, focusing on objective analysis.\n\n"
            f"Text:\n{section}\n\n"
            "Output format:\n[Enhanced text here]"
        )
        prompts.append(prompt)

    responses = await asyncio.gather(
        *(chat_func(prompt) for prompt in prompts)
    )
    for response in responses:
        enhanced_section = response.choices[0].message.content.strip()
        enhanced_sections.append(enhanced_section)

    enhanced_content = "\n\n".join(enhanced_sections)
    await upload_text_to_minio(
        bucket_name=BUCKET_NAME,
        object_name=f"{customer_name}/{uuid}/{model_name}/review_paper.txt",
        file_content=enhanced_content
    )
    
    return enhanced_content


async def process_papers(
    dataframe, topic, direction, 
    uuid, customer_name, model_name,
    chat_func
):
    """
    Process the given papers to extract relevant information and save it to a CSV file.
    
    Args:
        dataframe (pandas.DataFrame): The DataFrame containing the papers.
        topic (str): The topic to filter the papers by.
        direction (str): The direction to filter the papers by.
        uuid (str): The UUID of the task.
        customer_name (str): The name of the customer.
        chat_func (function): The function to use for the chat completion.
        
    Returns:
        pandas.DataFrame: The DataFrame containing the relevant papers.
    
    """
    # Duplicate, no need
    # relevant_rows = []  # List to collect relevant rows for DataFrame creation

    # Set up the output directory and CSV file
    # output_dir = os.path.join(customer_name)
    # os.makedirs(output_dir, exist_ok=True)
    prefix = f"{customer_name}/{uuid}/{model_name}/"
    output_dir = prefix

    output_path = os.path.join(output_dir, "relevant_papers.csv")

    # Create or clear the output file at the beginning
    # with open(output_path, 'w', newline='', encoding='utf-8') as f:
    #     writer = csv.writer(f, quoting=csv.QUOTE_ALL)
    #     writer.writerow(["Journal Title", "Publication Date", "Title", "First Author", "Summary", "Is Relevant", "Relevance Keywords"])  # Writing header
    texts = ""
    fieldnames = ["Journal Title", "Publication Date", "Title",
                  "First Author", "Summary", "Is Relevant", "Relevance Keywords"]
    texts += ",".join([escape_csv_field(x) for x in fieldnames]) + "\n"

    titles = []
    abstracts = []
    journal_titles = []
    pubd_dates = []
    first_authors = []
    summaries = []
    for idx, row in dataframe.iterrows():
        title = row["TI"]
        abstract = row["AB"]
        journal_title = row["JT"]
        pub_date = row["DCOM"]
        first_author = row["FAU-frist"]

        titles.append(title)
        abstracts.append(abstract)
        journal_titles.append(journal_title)
        pubd_dates.append(pub_date)
        first_authors.append(first_author)

    relevants = await asyncio.gather(
        *(is_relevant(
            title, abstract, topic, direction, chat_func
        ) for title, abstract in zip(titles, abstracts))
    )

    is_relevant_flags = [relevant[0] for relevant in relevants]
    relevance_keywords = [relevant[1] for relevant in relevants]

    rtitles = []
    rabstracts = []
    rjournal_titles = [] 
    rpubd_dates = []
    rfirst_authors = []
    rflags = []
    rkeywords = []

    for (
        rflag, rkeyword, title, abstarct, first_author, journal_title, pub_date
    ) in zip(
        is_relevant_flags, relevance_keywords,
        titles, abstracts, first_authors, journal_titles, pubd_dates
    ):
        if rflag:
            rtitles.append(title)
            rabstracts.append(abstarct)
            rfirst_authors.append(first_author)
            rjournal_titles.append(journal_title)
            rpubd_dates.append(pub_date)
            rflags.append(rflag)
            rkeywords.append(rkeyword)

    summaries = await asyncio.gather(
        *(summarize_abstract(
            title, abstract, first_author, chat_func
        ) for title, abstract, first_author in
            zip(rtitles, rabstracts, rfirst_authors)
        )
    )

    for (
        summary,
        journal_title, pub_date, title, first_author,
        rflag, rkeyword
    ) in zip(
        summaries,
        rjournal_titles, rpubd_dates, rtitles, rfirst_authors,
        rflags, rkeywords
    ):
        journal_title = escape_csv_field(journal_title)
        pub_date = escape_csv_field(pub_date)
        title = escape_csv_field(title)
        first_author = escape_csv_field(first_author)
        summary = escape_csv_field(summary)
        rkeyword = escape_csv_field(rkeyword)

        texts += ",".join([
            str(x) for x in [
                journal_title, pub_date, title, first_author,
                summary, rflag, rkeyword
            ]
        ]) + "\n"

        # Print the added summary and keywords
        logger.info(f"Added summary: {summary}")
        logger.info(f"Relevance Keywords: {rkeyword}")

    # Create the relevant DataFrame to return
    # relevant_df = pd.DataFrame(relevant_rows)
    # return relevant_df
    await upload_text_to_minio(
        bucket_name=BUCKET_NAME,
        object_name=output_path,
        file_content=texts
    )

    return output_path


async def translate_to_chinese_before_references(
    text,
    uuid, customer_name, model_name,
    chat_func
):
    """
    Translates the content of a text file to Chinese, keeping the '**References**' section in English.
    
    Args:
        text (str): The content of the text file.
        output_filename (str): The name of the output file.
        chat_func (function): The function to use for translation.
        
    Returns:
        str: The translated content.
    
    """
    lines = text.split("\n")

    # Step 3: 找到 '**References**' 行的索引
    references_index = None
    for i, line in enumerate(lines):
        if line.strip() == "**References**":
            references_index = i
            break

    # Step 4: 根据找到的索引分割内容
    if references_index is not None:
        main_content_lines = lines[:references_index]
        references_content_lines = lines[references_index:]
    else:
        # 如果没有找到 '**References**'，则认为整个内容为正文
        main_content_lines = lines
        references_content_lines = []

    # 将正文内容拼接为一个字符串
    main_content = "\n".join(main_content_lines)

    # Step 5: 分段处理正文内容进行翻译
    sections = main_content.split("\n\n")
    translated_sections = []

    prompts = []
    
    for section in sections:
        # 简化 prompt，只要求翻译正文内容
        prompt = (
            "Translate the following text to academic Chinese:\n\n"
            f"Text:\n{section}\n\n"
            "Output format:\n[Translated Chinese text here]"
        )
        prompts.append(prompt)
    
    responses = await asyncio.gather(
        *(chat_func(prompt) for prompt in prompts)
    )
    for response in responses:
        translated_section = response.choices[0].message.content.strip()
        translated_sections.append(translated_section)

    # Step 6: 将翻译后的正文拼接
    translated_content = "\n\n".join(translated_sections)

    # Step 7: 合并翻译后的正文和 References 部分
    if references_content_lines:
        references_content = "\n".join(references_content_lines)
        final_content = translated_content + "\n\n" + references_content
    else:
        final_content = translated_content

    # Step 8: 保存结果到新的文件
    output_filename = f"{customer_name}/{uuid}/{model_name}/review_paper_translated.txt"
    await upload_text_to_minio(
        bucket_name=BUCKET_NAME,
        object_name=output_filename,
        file_content=final_content
    )

    logger.info(f"\nTranslated content saved to {output_filename}")
    return output_filename


# Main function to automate the review paper creation process with language enhancement step
async def create_review_paper(
    relevant_papers_df,
    main_topic,
    uuid, customer_name, model_name,
    chat_func,
    translate_to_cn=False
):
    """
    Main function to automate the review paper creation process with language enhancement step.
    
    Args:
        relevant_papers_df (pd.DataFrame): DataFrame containing relevant papers.
        main_topic (str): Main topic of the review paper.
        uuid (str): Unique identifier for the review paper.
        customer_name (str): Name of the customer.
        chat_func (function): Function to handle chat interactions.
        translate_to_cn (bool): Flag to indicate if translation to Chinese is required.
    
    Returns:
        None
    
    """
    
    # Step 1: Generate subheadings related to the main topic
    subheadings = await generate_subheadings(
        relevant_papers_df, main_topic,
        chat_func
    )

    # Step 2: Assign each summary to a subheading
    relevant_papers_df = await assign_subheadings_to_summaries(
        relevant_papers_df, subheadings, 
        uuid, customer_name, model_name,
        chat_func
    )

    # Step 3: Create paragraphs by subheading, with introductory and concluding sections, and references
    review_content = await create_paragraphs_by_subheading(
        relevant_papers_df, subheadings, main_topic, 
        uuid, customer_name, model_name,
        chat_func
    )

    # Step 4: Enhance language and readability
    enhanced_content = await enhance_language_readability(
        review_content,
        chat_func
    )

    prefix = f"{customer_name}/{uuid}/{model_name}/"
    output_dir = prefix

    output_filename = os.path.join(output_dir, "review_paper.txt")

    # Step: Translate to Chinese
    if translate_to_cn:
        await translate_to_chinese_before_references(
            enhanced_content,
            output_filename.replace(".txt", "_cn.txt"),
            chat_func
        )

    # Step 6: Save the generated content to a text file
    # with open(output_filename, "w", encoding="utf-8") as f:
    #     f.write(enhanced_content)
    await upload_text_to_minio(
        bucket_name=BUCKET_NAME,
        object_name=output_filename,
        file_content=enhanced_content
    )

    logger.info(f"\nReview paper saved to {output_filename}")
    return output_filename