import requests
import re

def get_docket_ids(search_term):
    url = f"https://api.regulations.gov/v4/dockets"
    params = {
        'filter[searchTerm]': search_term,
        'api_key': "your-api-key"
    }
    response = requests.get(url, params=params)
    if response.status_code == 200:
        data = response.json()
        dockets = data['data']
        docket_ids = [docket['id'] for docket in dockets]
        return docket_ids
    else:
        return f"Error: {response.status_code}"

class RegulationsDataFetcher:
    API_KEY = "your-api-key"
    BASE_COMMENT_URL = 'https://api.regulations.gov/v4/comments'
    BASE_DOCKET_URL = 'https://api.regulations.gov/v4/dockets/'
    HEADERS = {
        'X-Api-Key': API_KEY,
        'Content-Type': 'application/json'
    }

    def __init__(self, docket_id):
        self.docket_id = docket_id
        self.docket_url = self.BASE_DOCKET_URL + docket_id
        self.dataset = []

    def fetch_comments(self):
        """Fetch a single page of 25 comments."""
        url = f'{self.BASE_COMMENT_URL}?filter[docketId]={self.docket_id}&page[number]=1&page[size]=25'
        response = requests.get(url, headers=self.HEADERS)
        
        if response.status_code == 200:
            return response.json()
        else:
            print(f'Failed to retrieve comments: {response.status_code}')
            return None

    def get_docket_info(self):
        """Get docket information."""
        response = requests.get(self.docket_url, headers=self.HEADERS)
        
        if response.status_code == 200:
            docket_data = response.json()
            return (docket_data['data']['attributes']['agencyId'],
                    docket_data['data']['attributes']['title'],
                    docket_data['data']['attributes']['modifyDate'], 
                    docket_data['data']['attributes']['docketType'], 
                    docket_data['data']['attributes']['keywords'])
        else:
            print(f'Failed to retrieve docket info: {response.status_code}')
            return None

    def fetch_comment_details(self, comment_url):
        """Fetch detailed information of a comment."""
        response = requests.get(comment_url, headers=self.HEADERS)
        if response.status_code == 200:
            return response.json()
        else:
            print(f'Failed to retrieve comment details: {response.status_code}')
            return None

    def collect_data(self):
        """Collect data and reshape into nested dictionary format."""
        data = self.fetch_comments()
        if not data:
            return None

        docket_info = self.get_docket_info()
        if not docket_info:
            return None

        # Starting out with docket information
        nested_data = {
            "id": self.docket_id,
            "agency": self.docket_id.split('-')[0],
            "title": docket_info[1] if docket_info else "Unknown Title",
            "update_date": docket_info[2].split('T')[0] if docket_info and docket_info[2] else "Unknown Update Date",
            "update_time": docket_info[2].split('T')[1].strip('Z') if docket_info and docket_info[2] and 'T' in docket_info[2] else "Unknown Update Time",
            "purpose": docket_info[3],
            "keywords": docket_info[4],
            "comments": []
        }

        # Going into each docket for comment information
        if 'data' in data:
            for comment in data['data']:
                if len(nested_data["comments"]) >= 10:
                    break

                comment_details = self.fetch_comment_details(comment['links']['self'])
                if 'data' in comment_details and 'attributes' in comment_details['data']:
                    comment_data = comment_details['data']['attributes']

                    # Basic comment text cleaning
                    comment_text = (comment_data.get('comment', '') or '').strip()
                    comment_text = comment_text.replace("<br/>", "").replace("<span style='padding-left: 30px'></span>", "")
                    comment_text = re.sub(r'&[^;]+;', '', comment_text)

                    # Recording detailed comment information
                    if (comment_text and "attached" not in comment_text.lower() and "attachment" not in comment_text.lower() and comment_text.lower() != "n/a"):
                        nested_comment = {
                            "text": comment_text,
                            "comment_id": comment['id'],
                            "comment_url": comment['links']['self'],
                            "comment_date": comment['attributes']['postedDate'].split('T')[0],
                            "comment_time": comment['attributes']['postedDate'].split('T')[1].strip('Z'),
                            "commenter_fname": ((comment_data.get('firstName') or 'Anonymous').split(',')[0]).capitalize(),
                            "commenter_lname": ((comment_data.get('lastName') or 'Anonymous').split(',')[0]).capitalize(),
                            "comment_length": len(comment_text) if comment_text is not None else 0
                        }
                        nested_data["comments"].append(nested_comment)

        return nested_data


# COLLECTING DATA 
    
substance_related_terms = [
    # Types of Opioids
    "opioids",
    "heroin",
    "morphine",
    "fentanyl",
    "methadone",
    "oxycodone",
    "lofexidine",
    "hydrocodone",
    "codeine",
    "tramadol",
    "prescription opioids",
    # Withdrawal Support
    "lofexidine",
    "buprenorphine",
    "naloxone",
    # Related Phrases
    "opioid epidemic",
    "opioid abuse",
    "opioid crisis",
    "opioid overdose",
    "opioid tolerance",
    "opioid treatment program",
    "medication assisted treatment",
    "substance abuse",
    "narcotics",
    "opioid addiction",
    "opioid withdrawal",
    "opioid dependence",
    "opioid use disorder",
    "opioid receptor",
    "pain management",
    "prescription drug abuse",
    "drug addiction treatment",
    "controlled substances",
    "opioid analgesics",
    # Additional Terms
    "naltrexone",
    "opioid detoxification",
    "opioid therapy",
    "chronic pain",
    "opioid agonist",
    "partial opioid agonist",
    "opioid antagonist",
    "drug rehabilitation",
    "overdose prevention",
    "opioid prescribing guidelines",
    "opioid risk tool",
    "opioid alternative",
    "addiction recovery",
    "addiction counseling",
    "opioid education",
    "opioid policy",
    "opioid regulation",
    # Types of Other Substances
    "marijuana",
    "cannabis",
    "THC",
    "CBD",
    "synthetic cannabinoids",
    "alcohol",
    "ethanol",
    "benzodiazepines",
    "cocaine",
    "amphetamine",
    "methamphetamine",
    "MDMA",
    "ecstasy",
    "hallucinogens",
    "LSD",
    "psilocybin",
    "ketamine",
    "inhalants",
    "steroids",
    "tobacco",
    "nicotine",
    # Related Phrases for Other Substances
    "alcohol abuse",
    "alcohol addiction",
    "alcohol dependence",
    "alcohol withdrawal",
    "alcohol treatment",
    "binge drinking",
    "drug abuse",
    "drug addiction",
    "drug dependence",
    "drug withdrawal",
    "drug treatment",
    "substance use disorder",
    "chemical dependency",
    "intoxication",
    "sobriety",
    "recovery program",
    "detoxification",
    "rehabilitation",
    "12-step program",
    "psychoactive drugs",
    "addictive behavior",
    "harm reduction",
    "substance abuse counseling",
    "addiction therapy",
    "substance abuse prevention",
    "drug education",
    "drug policy",
    "drug regulation"
]
    
docket_ids = set()
all_data = []

for term in substance_related_terms:
    docket_ids.update(get_docket_ids(term))

for docket_id in docket_ids:
    fetcher = RegulationsDataFetcher(docket_id)
    docket_data = fetcher.collect_data()
    if docket_data and len(docket_data["comments"]) != 0:
        all_data.append(docket_data)