# !pip install mistune
import mistune
from mistune.plugins.table import table
from jinja2 import Template
import re
import os

def md_to_html(md_text):
    renderer = mistune.HTMLRenderer()
    markdown_renderer = mistune.Markdown(renderer, plugins=[table])
    html_content = markdown_renderer(md_text)
    return html_content.replace('\n', '')

####------------------------------ OPTIONAL--> User id and persistant data storage-------------------------------------####
from datetime import datetime
import psycopg2

from dotenv import load_dotenv, find_dotenv

# Load environment variables from .env file
load_dotenv("keys.env")

TOGETHER_API_KEY = os.getenv('TOGETHER_API_KEY')
BRAVE_API_KEY = os.getenv('BRAVE_API_KEY')
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
HELICON_API_KEY = os.getenv("HELICON_API_KEY")
SUPABASE_USER = os.environ['SUPABASE_USER']
SUPABASE_PASSWORD = os.environ['SUPABASE_PASSWORD']

def insert_data(user_id, user_query, subtopic_query, response, html_report):
    # Connect to your database
    conn = psycopg2.connect(
    dbname="postgres",
    user=SUPABASE_USER,
    password=SUPABASE_PASSWORD,
    host="aws-0-us-west-1.pooler.supabase.com",
    port="5432"
)
    cur = conn.cursor()
    insert_query = """
    INSERT INTO research_pro_chat_v2 (user_id, user_query, subtopic_query, response, html_report, created_at)
    VALUES (%s, %s, %s, %s, %s, %s);
    """
    cur.execute(insert_query, (user_id,user_query, subtopic_query, response, html_report, datetime.now()))
    conn.commit()
    cur.close()
    conn.close()

####-----------------------------------------------------END----------------------------------------------------------####


import ast
from fpdf import FPDF
import re
import pandas as pd
import nltk
nltk.download('stopwords')
nltk.download('punkt')
import requests
import json
from retry import retry
from concurrent.futures import ThreadPoolExecutor, as_completed
from bs4 import BeautifulSoup
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from brave import Brave
from together import Together
from langchain_core.output_parsers import JsonOutputParser
from fuzzy_json import loads
from half_json.core import JSONFixer
from openai import OpenAI

llm_default_small = "llama3-8b-8192"
llm_default_medium = "llama3-70b-8192"

SysPromptJson = "You are now in the role of an expert AI who can extract structured information from user request. Both key and value pairs must be in double quotes. You must respond ONLY with a valid JSON file. Do not add any additional comments."
SysPromptList = "You are now in the role of an expert AI who can extract structured information from user request. All elements must be in double quotes. You must respond ONLY with a valid python List. Do not add any additional comments."
SysPromptDefault = "You are an expert AI, complete the given task. Do not add any additional comments."

import tiktoken # Used to limit tokens
encoding = tiktoken.encoding_for_model("gpt-3.5-turbo") # Instead of Llama3 using available option/ replace if found anything better

def limit_tokens(input_string, token_limit=8000):
    """
    Limit tokens sent to the model
    """
    return encoding.decode(encoding.encode(input_string)[:token_limit])

def together_response(message, model=llm_default_small, SysPrompt = SysPromptDefault,temperature=0.2):
  
  client = OpenAI(
    api_key=GROQ_API_KEY, 
    base_url="https://gateway.hconeai.com/openai/v1", 
    default_headers={ 
        "Helicone-Auth": f"Bearer {HELICON_API_KEY}",
        "Helicone-Target-Url": "https://api.groq.com"
    }
    )

  messages=[{"role": "system", "content": SysPrompt},{"role": "user", "content": message}]

  response = client.chat.completions.create(
      model=model,
      messages=messages,
      temperature=temperature,
  )
  return response.choices[0].message.content


def json_from_text(text):
    """
    Extracts JSON from text using regex and fuzzy JSON loading.
    """
    match = re.search(r'\{[\s\S]*\}', text)
    if match:
      json_out = match.group(0)
    else:
      json_out = text
    try:
        # Using fuzzy json loader
        return loads(json_out)
    except Exception:
        # Using JSON fixer/ Fixes even half json/ Remove if you need an exception
        fix_json = JSONFixer()
        return loads(fix_json.fix(json_out).line)

def remove_stopwords(text):
    stop_words = set(stopwords.words('english'))
    words = word_tokenize(text)
    filtered_text = [word for word in words if word.lower() not in stop_words]
    return ' '.join(filtered_text)

def rephrase_content(content, query):
    return together_response(f"You are an information retriever,ignore everything you know, return only the\
     numerical or quantitative data regarding the query: {{{query}}} structured into markdown tables only \
    , using the scraped context:{{{limit_tokens(content)}}}")

class Scraper:
    def __init__(self, user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"):
        self.session = requests.Session()
        self.session.headers.update({"User-Agent": user_agent})

    @retry(tries=3, delay=1)
    def fetch_content(self, url):
        try:
            response = self.session.get(url, timeout=2)
            if response.status_code == 200:
                return response.text
        except requests.exceptions.RequestException as e:
            print(f"Error fetching page content for {url}: {e}")
        return None

def extract_main_content(html):
    if html:
        soup = BeautifulSoup(html, 'lxml')
        paragraphs = soup.find_all('p')
        text = ' '.join(p.get_text() for p in paragraphs)
        return text
    return ""

def process_content(url, query):
    scraper = Scraper()
    html_content = scraper.fetch_content(url)
    if html_content:
        content = extract_main_content(html_content)
        if content:
            rephrased_content = rephrase_content(remove_stopwords(content)[:4096*4], query)
            return rephrased_content, url
    return "", url

def fetch_and_extract_content(urls, query):
    with ThreadPoolExecutor(max_workers=len(urls)) as executor:
        future_to_url = {executor.submit(process_content, url, query): url for url in urls}
        all_text_with_urls = [future.result() for future in as_completed(future_to_url)]

    return all_text_with_urls

def search_brave(query, num_results=5):

    brave = Brave(BRAVE_API_KEY)

    search_results = brave.search(q=query, count=num_results)

    return [url.__str__() for url in search_results.urls]

def generate_report_with_reference(full_data):
    """
    Generate HTML report with references and saves pdf report to "generated_pdf_report.pdf"
    """
    pdf = FPDF()
    with open("report_with_references_template.html") as f: # src/research-pro/app_v1.5_online/
        html_template = f.read()

    # Loop through each row in your dataset
    html_report = ''
    idx = 1
    for subtopic_data in full_data:

        md_report = md_to_html(subtopic_data['md_report'])
        # Convert the string representation of a list of tuples back to a list of tuples
        references = ast.literal_eval(subtopic_data['text_with_urls'])
        
        collapsible_blocks = []
        for ref_idx, reference in enumerate(references):
            ref_text = md_to_html(reference[0])
            ref_url = reference[1]
            urls_html = ''.join(f'<a href="{ref_url}"> {ref_url}</a>')
            
            collapsible_block = '''
            <details>
                <summary>Reference {}: {}</summary>
                <div>
                    <p>{}</p>
                    <ul>{}</ul>
                </div>
            </details>
            '''.format(ref_idx+1, urls_html, ref_text, urls_html)
            
            collapsible_blocks.append(collapsible_block)

        references_html = '\n'.join(collapsible_blocks)
        
        template = Template(html_template)
        html_page = template.render(md_report=md_report, references=references_html)
        
        pdf.add_page()
        pdf_report = f"<h1><strong>Report {idx}</strong></h1>"+md_report+f"<h1><strong>References for Report {idx}</strong></h1>"+references_html

        pdf.write_html(pdf_report.encode('ascii', 'ignore').decode('ascii')) # Filter non-asci characters
        html_report += html_page
        idx+=1

    pdf.output("generated_pdf_report.pdf")
    return html_report

def write_dataframes_to_excel(dataframes_list, filename):
    """
    input: [df_list1, df_list2, ..]
    saves filename.xlsx
    """
    try:
        with pd.ExcelWriter(filename, engine="openpyxl") as writer:
            for idx, dataframes in enumerate(dataframes_list):
                startrow = 0
                for idx2, df in enumerate(dataframes):
                    df.to_excel(writer, sheet_name=f"Sheet{idx+1}", startrow=startrow, index=False)
                    startrow += len(df) + 2
    except:
        # Empty dataframe due to no tables found, file is not written
        pass

def extract_tables_from_html(html_file):
    """
    input: html_file
    output: [df1,df2,df3,..]
    """
    # Initialize an empty list to store the dataframes
    dataframes = []

    # Open the HTML file and parse it with BeautifulSoup
    soup = BeautifulSoup(html_file, 'html.parser')

    # Find all the tables in the HTML file
    tables = soup.find_all('table')

    # Iterate through each table
    for table in tables:
        # Extract the table headers
        headers = [th.text for th in table.find_all('th')]

        # Extract the table data
        rows = table.find_all('tr')
        data = []
        for row in rows:
            row_data = [td.text for td in row.find_all('td')]
            data.append(row_data)

        # Create a dataframe from the headers and data
        df = pd.DataFrame(data, columns=headers)

        # Append the dataframe to the list of dataframes
        dataframes.append(df)

    # Return the list of dataframes
    return dataframes