agents/graph.py

"""
agents/graph.py — Post processing pipeline for feelin'
"""

from __future__ import annotations
from typing import TypedDict

from agents.tools import detect_bragging, classify_emotion, PERSONAS
from data.db import insert_post, flag_brag, update_post_scores

CAT_EMOJIS = {
    "hilarious": "😂",
    "tragic":    "😢",
    "unhinged":  "😤",
    "awkward":   "😬",
    "chaotic":   "🤯",
}

RANK_MSGS = {
    1:  "🥇 **#1 all time.** Frame this.",
    2:  "🥈 **#2.** Somebody above you had a worse day.",
    3:  "🥉 **#3.** Bronze is still a medal in suffering.",
    4:  "**Top 5.** This is the content we came for.",
    5:  "**Top 5.** Solid.",
    10: "**Top 10.** You're in the conversation.",
}


class FeelState(TypedDict):
    post_text: str
    post_id: str
    is_brag: bool
    brag_roast: str
    scores: dict
    top_category: str
    top_score: float
    reaction: str
    response_message: str
    response_type: str  # "success" | "brag" | "error"


def run_post_pipeline(text: str) -> dict:
    """Full pipeline: submit → detect → classify → respond."""
    state: FeelState = {
        "post_text": text,
        "post_id": "",
        "is_brag": False,
        "brag_roast": "",
        "scores": {},
        "top_category": "",
        "top_score": 0.0,
        "reaction": "",
        "response_message": "",
        "response_type": "success",
    }

    # Node 1: write to DB
    state["post_id"] = insert_post(text)

    # Node 2: brag detection
    brag_result = detect_bragging(text)
    state["is_brag"] = brag_result.get("is_brag", False)
    state["brag_roast"] = brag_result.get("roast", "")
    confidence = brag_result.get("confidence", 0)

    if state["is_brag"] and confidence > 0.6:
        flag_brag(state["post_id"], state["brag_roast"])
        roast = state["brag_roast"] or "LinkedIn called. They want their energy back."
        state["response_type"] = "brag"
        state["response_message"] = _compose_brag_rejection(roast)
        return state

    # Node 3: emotion classification
    emotion_result = classify_emotion(text)
    state["scores"]       = emotion_result.get("scores", {})
    state["top_category"] = emotion_result.get("top_category", "chaotic")
    state["top_score"]    = emotion_result.get("top_score", 5.0)
    state["reaction"]     = emotion_result.get("reaction", "")

    # Node 4: update DB scores + get rank
    update_post_scores(
        state["post_id"],
        state["scores"],
        state["top_category"],
        state["top_score"],
        state["reaction"],
    )
    rank = _compute_rank(state["post_id"], state["top_category"])

    # Node 5: compose response
    state["response_message"] = _compose_success_response(state, rank)
    return state


def _compute_rank(post_id: str, category: str) -> int:
    """Returns 1-based rank in the given category."""
    from data.db import get_leaderboard
    posts = get_leaderboard(category, limit=20)
    for i, p in enumerate(posts):
        if p["id"] == post_id:
            return i + 1
    return len(posts) + 1


def _compose_brag_rejection(roast: str) -> str:
    return f"""## 🚩 Brag Detected

Our AI smelled the LinkedIn energy from miles away.

> *{roast}*

This is a **no-brag zone**. Come back when something has gone terribly wrong.

---
*Try again with a story about a meeting that could've been an email.*"""


def _compose_success_response(state: FeelState, rank: int) -> str:
    cat   = state["top_category"]
    score = state["top_score"]
    emoji = CAT_EMOJIS.get(cat, "🤯")
    persona = PERSONAS.get(cat, {})
    scores = state["scores"]

    # Score bar (visual)
    bar_filled = int(score)
    bar = "█" * bar_filled + "░" * (10 - bar_filled)

    # Rank message
    rank_msg = ""
    for threshold, msg in RANK_MSGS.items():
        if rank <= threshold:
            rank_msg = msg
            break
    if not rank_msg:
        rank_msg = f"Rank #{rank} — every confession counts."

    # Score breakdown
    breakdown = " · ".join(
        [f"{CAT_EMOJIS[k]} {v:.1f}" for k, v in sorted(scores.items(), key=lambda x: -x[1])]
    ) if scores else ""

    # Podcast mention
    podcast_tease = ""
    if score >= 7.0 and persona.get("name"):
        podcast_tease = f"\n\n🎙️ *{persona['name']} might read this on **{persona['show']}**.*"

    reaction_line = f"\n\n> *\"{state['reaction']}\"*" if state["reaction"] else ""

    return f"""## {emoji} {cat.upper()} · {score:.1f}/10

`{bar}` **{score:.1f}**

{rank_msg}{reaction_line}

---
**Score breakdown:** {breakdown}{podcast_tease}"""