import re
import math
import pandas as pd
from typing import List

STOPWORDS = set("""
a an and the or for nor but so yet of to in on with at by from as is are was were be being been
i you he she it we they them us our your their this that these those here there
""".split())

def dedupe_sentences(text: str) -> str:
    parts = re.split(r'(?<=[.!?])\s+', text.strip())
    seen, out = set(), []
    for p in parts:
        norm = re.sub(r'\s+', ' ', p.strip().lower())
        if norm and norm not in seen:
            seen.add(norm); out.append(p.strip())
    return " ".join(out).strip()

def strip_labels(text: str) -> str:
    patterns = [r'^\s*(hook|body|takeaway|cta):\s*', r'^\s*(Hook|Body|Takeaway|CTA):\s*']
    lines = text.splitlines()
    cleaned = []
    for line in lines:
        L = line
        for p in patterns:
            L = re.sub(p, '', L)
        cleaned.append(L)
    return "\n".join(cleaned).strip()

def load_posts(file) -> pd.DataFrame:
    name = file.name.lower()
    if name.endswith(".csv"):
        df = pd.read_csv(file)
    elif name.endswith(".json"):
        df = pd.read_json(file, lines=False)
    else:
        raise ValueError("Upload CSV or JSON.")
    cand = [c for c in df.columns if c.lower() in ("text","post","content","body")]
    if not cand:
        raise ValueError("Dataset must include 'text' (or post/content/body).")
    if "text" not in df.columns:
        df["text"] = df[cand[0]]
    df["text"] = df["text"].fillna("").astype(str)
    return df[["text"]]

def simple_rake(text, min_len=2, max_len=3, top_k=12):
    words = re.findall(r"[A-Za-z0-9#+\-_/']+", text.lower())
    phrases, cur = [], []
    for w in words:
        if w in STOPWORDS:
            if cur:
                phrases.append(" ".join(cur)); cur=[]
        else:
            cur.append(w)
    if cur:
        phrases.append(" ".join(cur))
    freq, degree, scores = {}, {}, {}
    for ph in phrases:
        toks = ph.split()
        for t in toks:
            freq[t] = freq.get(t,0)+1
            degree[t] = degree.get(t,0)+(len(toks)-1)
    for ph in phrases:
        scores[ph] = sum((degree.get(t,0)+1)/(freq.get(t,1)) for t in ph.split())
    ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
    return [p for p,_ in ranked if min_len <= len(p.split()) <= max_len][:top_k]

def tfidf_builder(texts: List[str], top_k=8):
    docs = [re.findall(r"[A-Za-z0-9#+\-_/']+", t.lower()) for t in texts]
    vocab = {}
    for d in docs:
        for w in set(d):
            vocab[w] = vocab.get(w,0)+1
    N = len(docs)
    def score(text):
        doc = re.findall(r"[A-Za-z0-9#+\-_/']+", text.lower())
        tf = {}
        for w in doc:
            tf[w] = tf.get(w,0)+1
        scores = {}
        for w,c in tf.items():
            df = vocab.get(w,1)
            idf = math.log((N+1)/(df+1))+1
            scores[w] = (c/len(doc))*idf
        ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
        return [w for w,_ in ranked[:top_k]]
    return score

def extract_keywords(topic: str, df: pd.DataFrame|None) -> List[str]:
    if df is not None and len(df):
        sample = df["text"].sample(min(30, len(df)), random_state=42).tolist()
        rake_kw = simple_rake(" ".join(sample + [topic]), min_len=2, max_len=3, top_k=12)
        tfidf_fn = tfidf_builder(df["text"].tolist(), top_k=8)
        kw2 = tfidf_fn(topic + " " + " ".join(sample[:5]))
        raw = rake_kw + kw2
    else:
        raw = simple_rake(topic, min_len=1, max_len=2, top_k=8)
    seen, out = set(), []
    for k in raw:
        k2 = re.sub(r"\s+"," ",k.strip().lower())
        if k2 and k2 not in seen:
            seen.add(k2); out.append(k2)
    return out[:12]