import re
import math
from typing import Optional

# ══════════════════════════════════════════════════════════════════════════════
# 1. SALES ESTIMATOR v2 — category-specific BSR → monthly sales
# ══════════════════════════════════════════════════════════════════════════════

CATEGORY_MULTIPLIERS = {
    "kitchen": {"base": 8000, "decay": 0.55},
    "home":    {"base": 7000, "decay": 0.55},
    "sports":  {"base": 6000, "decay": 0.52},
    "toys":    {"base": 9000, "decay": 0.58},
    "beauty":  {"base": 7500, "decay": 0.54},
    "health":  {"base": 6500, "decay": 0.53},
    "tools":   {"base": 5000, "decay": 0.50},
    "office":  {"base": 5500, "decay": 0.51},
    "pet":     {"base": 6000, "decay": 0.52},
    "garden":  {"base": 4500, "decay": 0.50},
    "default": {"base": 6000, "decay": 0.52},
}

def estimate_sales_v2(bsr: int, category: str = "default", price: float = 25.0) -> dict:
    cat = CATEGORY_MULTIPLIERS.get(category.lower(), CATEGORY_MULTIPLIERS["default"])
    if bsr <= 0:
        bsr = 50000
    monthly_sales = int(cat["base"] * math.pow(bsr, -cat["decay"]) * 1000)
    monthly_sales = max(1, min(monthly_sales, 50000))
    monthly_revenue = round(monthly_sales * price, 2)
    daily_sales = round(monthly_sales / 30, 1)

    # Confidence based on BSR range
    if bsr < 5000:
        confidence = "High"
    elif bsr < 50000:
        confidence = "Medium"
    else:
        confidence = "Low"

    return {
        "monthly_sales": monthly_sales,
        "daily_sales": daily_sales,
        "monthly_revenue": monthly_revenue,
        "confidence": confidence,
        "category": category,
        "bsr": bsr,
        "price": price,
        "annual_revenue": round(monthly_revenue * 12, 2),
    }


# ══════════════════════════════════════════════════════════════════════════════
# 2. REVIEW ANALYZER — sentiment + patterns
# ══════════════════════════════════════════════════════════════════════════════

POSITIVE_WORDS = ["great","excellent","perfect","love","amazing","best","good","quality","easy","fast","recommend","happy","pleased","works","nice","fantastic","awesome","solid","durable","comfortable"]
NEGATIVE_WORDS = ["poor","bad","terrible","worst","broken","useless","cheap","waste","disappointed","defective","fragile","flimsy","slow","wrong","missing","return","refund","fake","stopped","horrible"]
QUALITY_WORDS  = ["quality","durable","sturdy","solid","premium","cheap","flimsy","fragile","well-made","poorly made"]
DELIVERY_WORDS = ["fast","quick","slow","delivery","shipping","arrived","late","early","package","damaged"]
VALUE_WORDS    = ["worth","value","price","expensive","cheap","affordable","overpriced","deal","bargain"]

def analyze_reviews(rating: float, review_count: int, title: str = "") -> dict:
    """Analyze product based on available data — real NLP would need actual review text."""

    # Star distribution estimate
    if rating >= 4.5:
        stars = {"5": 70, "4": 20, "3": 5, "2": 3, "1": 2}
    elif rating >= 4.0:
        stars = {"5": 55, "4": 25, "3": 10, "2": 5, "1": 5}
    elif rating >= 3.5:
        stars = {"5": 40, "4": 25, "3": 15, "2": 10, "1": 10}
    elif rating >= 3.0:
        stars = {"5": 30, "4": 20, "3": 20, "2": 15, "1": 15}
    else:
        stars = {"5": 20, "4": 15, "3": 15, "2": 20, "1": 30}

    positive_pct = stars["5"] + stars["4"]
    negative_pct = stars["1"] + stars["2"]

    # Sentiment
    if rating >= 4.3:
        sentiment = "Very Positive"
        sentiment_color = "#34d399"
    elif rating >= 3.8:
        sentiment = "Positive"
        sentiment_color = "#86efac"
    elif rating >= 3.3:
        sentiment = "Mixed"
        sentiment_color = "#fbbf24"
    elif rating >= 2.8:
        sentiment = "Negative"
        sentiment_color = "#fb923c"
    else:
        sentiment = "Very Negative"
        sentiment_color = "#f87171"

    # Review velocity
    if review_count > 10000:
        velocity = "Saturated"
    elif review_count > 1000:
        velocity = "High"
    elif review_count > 200:
        velocity = "Medium"
    elif review_count > 50:
        velocity = "Growing"
    else:
        velocity = "Low"

    # Market opportunity from reviews
    if review_count < 100 and rating >= 4.0:
        opportunity = "High — low reviews, good rating. Easy entry."
    elif review_count < 500 and rating >= 3.8:
        opportunity = "Medium — manageable competition."
    elif review_count > 1000 and rating < 3.8:
        opportunity = "Medium — saturated but poor quality. Can disrupt."
    elif review_count > 1000 and rating >= 4.2:
        opportunity = "Low — strong competition, hard to enter."
    else:
        opportunity = "Medium — assess further."

    return {
        "rating": rating,
        "review_count": review_count,
        "sentiment": sentiment,
        "sentiment_color": sentiment_color,
        "positive_pct": positive_pct,
        "negative_pct": negative_pct,
        "star_distribution": stars,
        "review_velocity": velocity,
        "market_opportunity": opportunity,
        "common_positives": ["Good quality", "Fast delivery", "Value for money", "Easy to use", "As described"][:3],
        "common_negatives": ["Quality issues", "Poor packaging", "Not as described"][:2] if negative_pct > 15 else [],
        "entry_barrier": "Low" if review_count < 100 else "Medium" if review_count < 500 else "High",
        "entry_barrier_color": "#34d399" if review_count < 100 else "#fbbf24" if review_count < 500 else "#f87171",
    }


# ══════════════════════════════════════════════════════════════════════════════
# 3. BUY BOX ANALYZER
# ══════════════════════════════════════════════════════════════════════════════

def analyze_buy_box(price: float, rating: float, review_count: int, is_fba: bool = True, is_prime: bool = True) -> dict:
    score = 0
    factors = []

    # Price competitiveness
    if price < 20:
        score += 25
        factors.append({"factor": "Price", "status": "Competitive", "color": "#34d399", "detail": f"${price:.2f} — low price helps win buy box"})
    elif price < 50:
        score += 20
        factors.append({"factor": "Price", "status": "Moderate", "color": "#fbbf24", "detail": f"${price:.2f} — average price range"})
    else:
        score += 10
        factors.append({"factor": "Price", "status": "High", "color": "#f87171", "detail": f"${price:.2f} — high price hurts buy box chances"})

    # FBA
    if is_fba:
        score += 30
        factors.append({"factor": "Fulfillment", "status": "FBA ✓", "color": "#34d399", "detail": "FBA strongly favored for buy box"})
    else:
        score += 5
        factors.append({"factor": "Fulfillment", "status": "FBM", "color": "#f87171", "detail": "FBM sellers rarely win buy box"})

    # Prime
    if is_prime:
        score += 20
        factors.append({"factor": "Prime", "status": "Eligible ✓", "color": "#34d399", "detail": "Prime eligibility required for buy box"})
    else:
        score += 0
        factors.append({"factor": "Prime", "status": "Not Eligible", "color": "#f87171", "detail": "No Prime = very low buy box chance"})

    # Seller rating
    if rating >= 4.5:
        score += 15
        factors.append({"factor": "Seller Rating", "status": "Excellent", "color": "#34d399", "detail": f"{rating}★ — Amazon favors high-rated sellers"})
    elif rating >= 4.0:
        score += 10
        factors.append({"factor": "Seller Rating", "status": "Good", "color": "#fbbf24", "detail": f"{rating}★ — acceptable for buy box"})
    else:
        score += 3
        factors.append({"factor": "Seller Rating", "status": "Needs Work", "color": "#f87171", "detail": f"{rating}★ — low rating hurts buy box"})

    # Review count
    if review_count > 100:
        score += 10
        factors.append({"factor": "Reviews", "status": "Established", "color": "#34d399", "detail": f"{review_count:,} reviews — trusted seller signal"})
    else:
        score += 5
        factors.append({"factor": "Reviews", "status": "New Seller", "color": "#fbbf24", "detail": f"{review_count} reviews — building trust"})

    score = min(score, 100)

    if score >= 75:
        verdict = "Strong Buy Box Candidate"
        verdict_color = "#34d399"
    elif score >= 50:
        verdict = "Moderate Chance"
        verdict_color = "#fbbf24"
    elif score >= 25:
        verdict = "Low Chance"
        verdict_color = "#fb923c"
    else:
        verdict = "Unlikely to Win"
        verdict_color = "#f87171"

    return {
        "score": score,
        "verdict": verdict,
        "verdict_color": verdict_color,
        "factors": factors,
        "tips": [
            "Use FBA for best buy box eligibility",
            "Price within 2% of lowest FBA offer",
            "Maintain 95%+ seller feedback rating",
            "Keep order defect rate below 1%",
            "Ship on time — late shipments hurt score",
        ][:3]
    }


# ══════════════════════════════════════════════════════════════════════════════
# 4. NICHE FINDER
# ══════════════════════════════════════════════════════════════════════════════

def analyze_niche(keyword: str, avg_bsr: float, avg_reviews: float, avg_price: float, product_count: int = 100) -> dict:
    # Demand score (lower BSR = higher demand)
    if avg_bsr < 5000:
        demand = 95
    elif avg_bsr < 20000:
        demand = 80
    elif avg_bsr < 50000:
        demand = 60
    elif avg_bsr < 100000:
        demand = 40
    else:
        demand = 20

    # Competition score (lower reviews = less competition)
    if avg_reviews < 50:
        competition = 10   # low competition
    elif avg_reviews < 200:
        competition = 30
    elif avg_reviews < 500:
        competition = 55
    elif avg_reviews < 1000:
        competition = 75
    else:
        competition = 90

    # Price attractiveness
    if 20 <= avg_price <= 60:
        price_score = 90
        price_note = "Sweet spot for FBA ($20-$60)"
    elif 10 <= avg_price < 20:
        price_score = 60
        price_note = "Low margin risk under $20"
    elif 60 < avg_price <= 100:
        price_score = 75
        price_note = "Good margin, lower volume"
    else:
        price_score = 40
        price_note = "High price = niche market"

    # Opportunity score
    opportunity = round((demand * 0.4) + ((100 - competition) * 0.4) + (price_score * 0.2))

    if opportunity >= 70:
        verdict = "🟢 Great Niche"
        verdict_color = "#34d399"
    elif opportunity >= 50:
        verdict = "🟡 Decent Niche"
        verdict_color = "#fbbf24"
    elif opportunity >= 35:
        verdict = "🟠 Challenging"
        verdict_color = "#fb923c"
    else:
        verdict = "🔴 Avoid"
        verdict_color = "#f87171"

    est_monthly_sales = estimate_sales_v2(int(avg_bsr), price=avg_price)["monthly_sales"]
    market_size = round(est_monthly_sales * avg_price * product_count / 1000, 0)

    return {
        "keyword": keyword,
        "opportunity_score": opportunity,
        "verdict": verdict,
        "verdict_color": verdict_color,
        "demand_score": demand,
        "competition_score": competition,
        "price_score": price_score,
        "price_note": price_note,
        "avg_bsr": int(avg_bsr),
        "avg_reviews": int(avg_reviews),
        "avg_price": avg_price,
        "est_monthly_sales": est_monthly_sales,
        "market_size_k": market_size,
        "recommendation": (
            "Low competition — great entry opportunity!" if competition < 30
            else "Medium competition — differentiate product." if competition < 60
            else "High competition — need strong differentiation."
        )
    }


# ══════════════════════════════════════════════════════════════════════════════
# 5. KEYWORD RESEARCH
# ══════════════════════════════════════════════════════════════════════════════

def generate_keywords(seed_keyword: str, category: str = "") -> dict:
    seed = seed_keyword.lower().strip()
    words = seed.split()

    # Generate variations
    modifiers_prefix = ["best", "top", "premium", "cheap", "professional", "heavy duty", "large", "small", "portable", "electric"]
    modifiers_suffix = ["for home", "for kitchen", "for office", "set", "kit", "bundle", "with lid", "non stick", "stainless steel", "organic"]

    keywords = []

    # Main keyword
    keywords.append({
        "keyword": seed,
        "search_volume": "High",
        "competition": "High",
        "opportunity": "Low",
        "type": "Head"
    })

    # Long tail variations
    for mod in modifiers_prefix[:5]:
        kw = f"{mod} {seed}"
        keywords.append({
            "keyword": kw,
            "search_volume": "Medium",
            "competition": "Medium",
            "opportunity": "Medium",
            "type": "Long-tail"
        })

    for mod in modifiers_suffix[:5]:
        kw = f"{seed} {mod}"
        keywords.append({
            "keyword": kw,
            "search_volume": "Low",
            "competition": "Low",
            "opportunity": "High",
            "type": "Long-tail"
        })

    # Backend keywords (for listing)
    backend = [seed] + words + [f"{words[-1]} set", f"buy {seed}", f"{seed} amazon"]

    return {
        "seed_keyword": seed,
        "total_keywords": len(keywords),
        "keywords": keywords,
        "backend_keywords": backend,
        "top_opportunity": [k for k in keywords if k["opportunity"] == "High"][:3],
        "tip": f"Target '{seed} for home' or '{seed} set' — lower competition, high buyer intent."
    }