PocketVeto/backend/app/services/llm_service.py

"""
LLM provider abstraction.

All providers implement generate_brief(doc_text, bill_metadata) -> ReverseBrief.
Select provider via LLM_PROVIDER env var.
"""
import json
import logging
import re
from abc import ABC, abstractmethod
from dataclasses import dataclass, field

from app.config import settings

logger = logging.getLogger(__name__)

SYSTEM_PROMPT = """You are a nonpartisan legislative analyst specializing in translating complex \
legislation into clear, accurate summaries for informed citizens. You analyze bills objectively \
without political bias.

Always respond with valid JSON matching exactly this schema:
{
  "summary": "2-4 paragraph plain-language summary of what this bill does",
  "key_points": [
    {"text": "specific concrete fact", "citation": "Section X(y)", "quote": "verbatim excerpt from bill ≤80 words", "label": "cited_fact"}
  ],
  "risks": [
    {"text": "legitimate concern or challenge", "citation": "Section X(y)", "quote": "verbatim excerpt from bill ≤80 words", "label": "cited_fact"}
  ],
  "deadlines": [{"date": "YYYY-MM-DD or null", "description": "what happens on this date"}],
  "topic_tags": ["healthcare", "taxation"]
}

Rules:
- summary: Explain WHAT the bill does, not whether it is good or bad. Be factual and complete.
- key_points: 5-10 specific, concrete things the bill changes, authorizes, or appropriates. \
Each item MUST include "text" (your claim), "citation" (the section number, e.g. "Section 301(a)(2)"), \
"quote" (a verbatim excerpt of ≤80 words from that section that supports your claim), and "label".
- risks: Legitimate concerns from any perspective — costs, implementation challenges, \
constitutional questions, unintended consequences. Include at least 2 even for benign bills. \
Each item MUST include "text", "citation", "quote", and "label" just like key_points.
- label: "cited_fact" if the claim is directly and explicitly stated in the quoted text. \
"inference" if the claim is an analytical interpretation, projection, or implication that goes \
beyond what the text literally says (e.g. projected costs, likely downstream effects, \
constitutional questions). When in doubt, use "inference".
- deadlines: Only include if explicitly stated in the text. Use null for date if a deadline \
is mentioned without a specific date. Empty list if none.
- topic_tags: 3-8 lowercase tags. Prefer these standard tags: healthcare, taxation, defense, \
education, immigration, environment, housing, infrastructure, technology, agriculture, judiciary, \
foreign-policy, veterans, social-security, trade, budget, energy, banking, transportation, \
public-lands, labor, civil-rights, science.

Respond with ONLY valid JSON. No preamble, no explanation, no markdown code blocks."""

MAX_TOKENS_DEFAULT = 6000
MAX_TOKENS_OLLAMA = 3000
TOKENS_PER_CHAR = 0.25  # rough approximation: 4 chars ≈ 1 token


@dataclass
class ReverseBrief:
    summary: str
    key_points: list[dict]
    risks: list[dict]
    deadlines: list[dict]
    topic_tags: list[str]
    llm_provider: str
    llm_model: str


def smart_truncate(text: str, max_tokens: int) -> str:
    """Truncate bill text intelligently if it exceeds token budget."""
    approx_tokens = len(text) * TOKENS_PER_CHAR
    if approx_tokens <= max_tokens:
        return text

    # Keep first 75% of budget for the preamble (purpose section)
    # and last 25% for effective dates / enforcement sections
    preamble_chars = int(max_tokens * 0.75 / TOKENS_PER_CHAR)
    tail_chars = int(max_tokens * 0.25 / TOKENS_PER_CHAR)
    omitted_chars = len(text) - preamble_chars - tail_chars

    return (
        text[:preamble_chars]
        + f"\n\n[... {omitted_chars:,} characters omitted for length ...]\n\n"
        + text[-tail_chars:]
    )


AMENDMENT_SYSTEM_PROMPT = """You are a nonpartisan legislative analyst. A bill has been updated \
and you must summarize what changed between the previous and new version.

Always respond with valid JSON matching exactly this schema:
{
  "summary": "2-3 paragraph plain-language description of what changed in this version",
  "key_points": [
    {"text": "specific change", "citation": "Section X(y)", "quote": "verbatim excerpt from new version ≤80 words", "label": "cited_fact"}
  ],
  "risks": [
    {"text": "new concern introduced by this change", "citation": "Section X(y)", "quote": "verbatim excerpt from new version ≤80 words", "label": "cited_fact"}
  ],
  "deadlines": [{"date": "YYYY-MM-DD or null", "description": "new deadline added"}],
  "topic_tags": ["healthcare", "taxation"]
}

Rules:
- summary: Focus ONLY on what is different from the previous version. Be specific.
- key_points: List concrete additions, removals, or modifications in this version. \
Each item MUST include "text" (your claim), "citation" (the section number, e.g. "Section 301(a)(2)"), \
"quote" (a verbatim excerpt of ≤80 words from the NEW version that supports your claim), and "label".
- risks: Only include risks that are new or changed relative to the previous version. \
Each item MUST include "text", "citation", "quote", and "label" just like key_points.
- label: "cited_fact" if the claim is directly and explicitly stated in the quoted text. \
"inference" if the claim is an analytical interpretation, projection, or implication that goes \
beyond what the text literally says. When in doubt, use "inference".
- deadlines: Only new or changed deadlines. Empty list if none.
- topic_tags: Same standard tags as before — include any new topics this version adds.

Respond with ONLY valid JSON. No preamble, no explanation, no markdown code blocks."""


def build_amendment_prompt(new_text: str, previous_text: str, bill_metadata: dict, max_tokens: int) -> str:
    half = max_tokens // 2
    truncated_new = smart_truncate(new_text, half)
    truncated_prev = smart_truncate(previous_text, half)
    return f"""A bill has been updated. Summarize what changed between the previous and new version.

BILL METADATA:
- Title: {bill_metadata.get('title', 'Unknown')}
- Sponsor: {bill_metadata.get('sponsor_name', 'Unknown')} \
({bill_metadata.get('party', '?')}-{bill_metadata.get('state', '?')})
- Latest Action: {bill_metadata.get('latest_action_text', 'None')} \
({bill_metadata.get('latest_action_date', 'Unknown')})

PREVIOUS VERSION:
{truncated_prev}

NEW VERSION:
{truncated_new}

Produce the JSON amendment summary now:"""


def build_prompt(doc_text: str, bill_metadata: dict, max_tokens: int) -> str:
    truncated = smart_truncate(doc_text, max_tokens)
    return f"""Analyze this legislation and produce a structured brief.

BILL METADATA:
- Title: {bill_metadata.get('title', 'Unknown')}
- Sponsor: {bill_metadata.get('sponsor_name', 'Unknown')} \
({bill_metadata.get('party', '?')}-{bill_metadata.get('state', '?')})
- Introduced: {bill_metadata.get('introduced_date', 'Unknown')}
- Chamber: {bill_metadata.get('chamber', 'Unknown')}
- Latest Action: {bill_metadata.get('latest_action_text', 'None')} \
({bill_metadata.get('latest_action_date', 'Unknown')})

BILL TEXT:
{truncated}

Produce the JSON brief now:"""


def parse_brief_json(raw: str | dict, provider: str, model: str) -> ReverseBrief:
    """Parse and validate LLM JSON response into a ReverseBrief."""
    if isinstance(raw, str):
        # Strip markdown code fences if present
        raw = re.sub(r"^```(?:json)?\s*", "", raw.strip())
        raw = re.sub(r"\s*```$", "", raw.strip())
        data = json.loads(raw)
    else:
        data = raw

    return ReverseBrief(
        summary=str(data.get("summary", "")),
        key_points=list(data.get("key_points", [])),
        risks=list(data.get("risks", [])),
        deadlines=list(data.get("deadlines", [])),
        topic_tags=list(data.get("topic_tags", [])),
        llm_provider=provider,
        llm_model=model,
    )


class LLMProvider(ABC):
    @abstractmethod
    def generate_brief(self, doc_text: str, bill_metadata: dict) -> ReverseBrief:
        pass

    @abstractmethod
    def generate_amendment_brief(self, new_text: str, previous_text: str, bill_metadata: dict) -> ReverseBrief:
        pass

    @abstractmethod
    def generate_text(self, prompt: str) -> str:
        pass


class OpenAIProvider(LLMProvider):
    def __init__(self, model: str | None = None):
        from openai import OpenAI
        self.client = OpenAI(api_key=settings.OPENAI_API_KEY)
        self.model = model or settings.OPENAI_MODEL

    def generate_brief(self, doc_text: str, bill_metadata: dict) -> ReverseBrief:
        prompt = build_prompt(doc_text, bill_metadata, MAX_TOKENS_DEFAULT)
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": prompt},
            ],
            response_format={"type": "json_object"},
            temperature=0.1,
        )
        raw = response.choices[0].message.content
        return parse_brief_json(raw, "openai", self.model)

    def generate_amendment_brief(self, new_text: str, previous_text: str, bill_metadata: dict) -> ReverseBrief:
        prompt = build_amendment_prompt(new_text, previous_text, bill_metadata, MAX_TOKENS_DEFAULT)
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": AMENDMENT_SYSTEM_PROMPT},
                {"role": "user", "content": prompt},
            ],
            response_format={"type": "json_object"},
            temperature=0.1,
        )
        raw = response.choices[0].message.content
        return parse_brief_json(raw, "openai", self.model)

    def generate_text(self, prompt: str) -> str:
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.3,
        )
        return response.choices[0].message.content or ""


class AnthropicProvider(LLMProvider):
    def __init__(self, model: str | None = None):
        import anthropic
        self.client = anthropic.Anthropic(api_key=settings.ANTHROPIC_API_KEY)
        self.model = model or settings.ANTHROPIC_MODEL

    def generate_brief(self, doc_text: str, bill_metadata: dict) -> ReverseBrief:
        prompt = build_prompt(doc_text, bill_metadata, MAX_TOKENS_DEFAULT)
        response = self.client.messages.create(
            model=self.model,
            max_tokens=4096,
            system=SYSTEM_PROMPT + "\n\nIMPORTANT: Respond with ONLY valid JSON. No other text.",
            messages=[{"role": "user", "content": prompt}],
        )
        raw = response.content[0].text
        return parse_brief_json(raw, "anthropic", self.model)

    def generate_amendment_brief(self, new_text: str, previous_text: str, bill_metadata: dict) -> ReverseBrief:
        prompt = build_amendment_prompt(new_text, previous_text, bill_metadata, MAX_TOKENS_DEFAULT)
        response = self.client.messages.create(
            model=self.model,
            max_tokens=4096,
            system=AMENDMENT_SYSTEM_PROMPT + "\n\nIMPORTANT: Respond with ONLY valid JSON. No other text.",
            messages=[{"role": "user", "content": prompt}],
        )
        raw = response.content[0].text
        return parse_brief_json(raw, "anthropic", self.model)

    def generate_text(self, prompt: str) -> str:
        response = self.client.messages.create(
            model=self.model,
            max_tokens=1024,
            messages=[{"role": "user", "content": prompt}],
        )
        return response.content[0].text


class GeminiProvider(LLMProvider):
    def __init__(self, model: str | None = None):
        import google.generativeai as genai
        genai.configure(api_key=settings.GEMINI_API_KEY)
        self._genai = genai
        self.model_name = model or settings.GEMINI_MODEL

    def _make_model(self, system_prompt: str):
        return self._genai.GenerativeModel(
            model_name=self.model_name,
            generation_config={"response_mime_type": "application/json", "temperature": 0.1},
            system_instruction=system_prompt,
        )

    def generate_brief(self, doc_text: str, bill_metadata: dict) -> ReverseBrief:
        prompt = build_prompt(doc_text, bill_metadata, MAX_TOKENS_DEFAULT)
        response = self._make_model(SYSTEM_PROMPT).generate_content(prompt)
        return parse_brief_json(response.text, "gemini", self.model_name)

    def generate_amendment_brief(self, new_text: str, previous_text: str, bill_metadata: dict) -> ReverseBrief:
        prompt = build_amendment_prompt(new_text, previous_text, bill_metadata, MAX_TOKENS_DEFAULT)
        response = self._make_model(AMENDMENT_SYSTEM_PROMPT).generate_content(prompt)
        return parse_brief_json(response.text, "gemini", self.model_name)

    def generate_text(self, prompt: str) -> str:
        model = self._genai.GenerativeModel(
            model_name=self.model_name,
            generation_config={"temperature": 0.3},
        )
        response = model.generate_content(prompt)
        return response.text


class OllamaProvider(LLMProvider):
    def __init__(self, model: str | None = None):
        self.base_url = settings.OLLAMA_BASE_URL.rstrip("/")
        self.model = model or settings.OLLAMA_MODEL

    def _generate(self, system_prompt: str, user_prompt: str) -> str:
        import requests as req
        full_prompt = f"{system_prompt}\n\n{user_prompt}"
        response = req.post(
            f"{self.base_url}/api/generate",
            json={"model": self.model, "prompt": full_prompt, "stream": False, "format": "json"},
            timeout=300,
        )
        response.raise_for_status()
        raw = response.json().get("response", "")
        try:
            return raw
        except Exception:
            strict = f"{full_prompt}\n\nCRITICAL: Your response MUST be valid JSON only."
            r2 = req.post(
                f"{self.base_url}/api/generate",
                json={"model": self.model, "prompt": strict, "stream": False, "format": "json"},
                timeout=300,
            )
            r2.raise_for_status()
            return r2.json().get("response", "")

    def generate_brief(self, doc_text: str, bill_metadata: dict) -> ReverseBrief:
        prompt = build_prompt(doc_text, bill_metadata, MAX_TOKENS_OLLAMA)
        raw = self._generate(SYSTEM_PROMPT, prompt)
        try:
            return parse_brief_json(raw, "ollama", self.model)
        except (json.JSONDecodeError, KeyError) as e:
            logger.warning(f"Ollama JSON parse failed, retrying: {e}")
            raw2 = self._generate(
                SYSTEM_PROMPT,
                prompt + "\n\nCRITICAL: Your response MUST be valid JSON only. No text before or after the JSON object."
            )
            return parse_brief_json(raw2, "ollama", self.model)

    def generate_amendment_brief(self, new_text: str, previous_text: str, bill_metadata: dict) -> ReverseBrief:
        prompt = build_amendment_prompt(new_text, previous_text, bill_metadata, MAX_TOKENS_OLLAMA)
        raw = self._generate(AMENDMENT_SYSTEM_PROMPT, prompt)
        try:
            return parse_brief_json(raw, "ollama", self.model)
        except (json.JSONDecodeError, KeyError) as e:
            logger.warning(f"Ollama amendment JSON parse failed, retrying: {e}")
            raw2 = self._generate(
                AMENDMENT_SYSTEM_PROMPT,
                prompt + "\n\nCRITICAL: Your response MUST be valid JSON only. No text before or after the JSON object."
            )
            return parse_brief_json(raw2, "ollama", self.model)

    def generate_text(self, prompt: str) -> str:
        import requests as req
        response = req.post(
            f"{self.base_url}/api/generate",
            json={"model": self.model, "prompt": prompt, "stream": False},
            timeout=120,
        )
        response.raise_for_status()
        return response.json().get("response", "")


def get_llm_provider(provider: str | None = None, model: str | None = None) -> LLMProvider:
    """Factory — returns the configured LLM provider.

    Pass ``provider`` and/or ``model`` explicitly (e.g. from DB overrides) to bypass env defaults.
    """
    if provider is None:
        provider = settings.LLM_PROVIDER
    provider = provider.lower()
    if provider == "openai":
        return OpenAIProvider(model=model)
    elif provider == "anthropic":
        return AnthropicProvider(model=model)
    elif provider == "gemini":
        return GeminiProvider(model=model)
    elif provider == "ollama":
        return OllamaProvider(model=model)
    raise ValueError(f"Unknown LLM_PROVIDER: '{provider}'. Must be one of: openai, anthropic, gemini, ollama")


_BILL_TYPE_LABELS: dict[str, str] = {
    "hr": "H.R.",
    "s": "S.",
    "hjres": "H.J.Res.",
    "sjres": "S.J.Res.",
    "hconres": "H.Con.Res.",
    "sconres": "S.Con.Res.",
    "hres": "H.Res.",
    "sres": "S.Res.",
}

_TONE_INSTRUCTIONS: dict[str, str] = {
    "short": "Keep the letter brief — 6 to 8 sentences total.",
    "polite": "Use a respectful, formal, and courteous tone throughout the letter.",
    "firm": "Use a direct, firm tone that makes clear the constituent's strong conviction.",
}


def generate_draft_letter(
    bill_label: str,
    bill_title: str,
    stance: str,
    recipient: str,
    tone: str,
    selected_points: list[str],
    include_citations: bool,
    zip_code: str | None,
    rep_name: str | None = None,
    llm_provider: str | None = None,
    llm_model: str | None = None,
) -> str:
    """Generate a plain-text constituent letter draft using the configured LLM provider."""
    vote_word = "YES" if stance == "yes" else "NO"
    chamber_word = "House" if recipient == "house" else "Senate"
    tone_instruction = _TONE_INSTRUCTIONS.get(tone, _TONE_INSTRUCTIONS["polite"])

    points_block = "\n".join(f"- {p}" for p in selected_points)

    citation_instruction = (
        "You may reference the citation label for each point (e.g. 'as noted in Section 3') if it adds clarity."
        if include_citations
        else "Do not include any citation references."
    )

    location_line = f"The constituent is writing from ZIP code {zip_code}." if zip_code else ""

    if rep_name:
        title = "Senator" if recipient == "senate" else "Representative"
        salutation_instruction = f'- Open with "Dear {title} {rep_name},"'
    else:
        salutation_instruction = f'- Open with "Dear {chamber_word} Member,"'

    prompt = f"""Write a short constituent letter to a {chamber_word} member of Congress.

RULES:
- {tone_instruction}
- 6 to 12 sentences total.
- {salutation_instruction}
- Second sentence must be a clear, direct ask: "Please vote {vote_word} on {bill_label}."
- The body must reference ONLY the points listed below — do not invent any other claims or facts.
- {citation_instruction}
- Close with a brief sign-off and the placeholder "[Your Name]".
- Plain text only. No markdown, no bullet points, no headers, no partisan framing.
- Do not mention any political party.

BILL: {bill_label} — {bill_title}
STANCE: Vote {vote_word}
{location_line}

SELECTED POINTS TO REFERENCE:
{points_block}

Write the letter now:"""

    return get_llm_provider(provider=llm_provider, model=llm_model).generate_text(prompt)