#!/opt/homebrew/bin/python3.11
"""
Text Semantic Analysis (PoC)
職責：分析 ASR 數據的語義分佈，生成統計報告並演示搜尋效果。
"""

import sys
import json
import os
import argparse
import numpy as np

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

try:
    from sentence_transformers import SentenceTransformer
    from sklearn.cluster import KMeans

    HAS_DEPS = True
except ImportError:
    HAS_DEPS = False
    print(
        "❌ Missing dependencies. Run: pip install sentence-transformers scikit-learn"
    )
    sys.exit(1)

OUTPUT_DIR = os.getenv("MOMENTRY_OUTPUT_DIR", "./output")


def load_asr_data(uuid):
    path = os.path.join(OUTPUT_DIR, f"{uuid}.asr.json")
    if not os.path.exists(path):
        print(f"❌ ASR file not found: {path}")
        return None
    with open(path, "r") as f:
        return json.load(f)


def run_analysis(uuid, num_topics=5):
    """
    運行語義分析
    """
    print(f"🚀 Starting Semantic Analysis for {uuid}...")

    # 1. 加載數據
    data = load_asr_data(uuid)
    if not data:
        return

    segments = data.get("segments", [])
    texts = [
        seg["text"] for seg in segments if len(seg["text"].strip()) > 5
    ]  # 過濾太短的
    times = [seg["start"] for seg in segments if len(seg["text"].strip()) > 5]

    if not texts:
        print("❌ No valid text found.")
        return

    print(f"✅ Loaded {len(texts)} valid text segments.")

    # 2. 向量化 (使用輕量級模型 all-MiniLM-L6-v2)
    print("🧠 Generating embeddings (this may take a moment)...")
    model = SentenceTransformer("all-MiniLM-L6-v2")
    embeddings = model.encode(texts, show_progress_bar=True)

    # 3. 統計分析：主題聚類 (K-Means)
    print(f"🔍 Identifying ~{num_topics} main topics...")
    kmeans = KMeans(n_clusters=num_topics, random_state=42, n_init=10)
    labels = kmeans.fit_predict(embeddings)

    # 計算每個 Topic 的中心句 (離中心點最近的句子)
    topic_centers = []
    for i in range(num_topics):
        cluster_indices = np.where(labels == i)[0]
        if len(cluster_indices) == 0:
            continue

        cluster_embeddings = embeddings[cluster_indices]
        cluster_texts = [texts[idx] for idx in cluster_indices]
        cluster_times = [times[idx] for idx in cluster_indices]

        # 計算 Cluster Center
        center = np.mean(cluster_embeddings, axis=0)

        # 找最接近中心的文本
        sims = np.dot(cluster_embeddings, center) / (
            np.linalg.norm(cluster_embeddings, axis=1) * np.linalg.norm(center)
        )
        best_idx_in_cluster = np.argmax(sims)

        topic_centers.append(
            {
                "topic_id": i,
                "representative_text": cluster_texts[best_idx_in_cluster],
                "representative_time": cluster_times[best_idx_in_cluster],
                "count": len(cluster_texts),
            }
        )

    # 4. 輸出報告
    print("\n" + "=" * 60)
    print(f"📊 ANALYSIS REPORT FOR {uuid}")
    print("=" * 60)
    for topic in sorted(topic_centers, key=lambda x: x["count"], reverse=True):
        print(f"🔹 Topic {topic['topic_id']} ({topic['count']} segments):")
        print(f"   💬 '{topic['representative_text']}'")
        print(f"   ⏰ Time: {topic['representative_time']:.2f}s")
        print("-" * 40)

    # 5. 演示搜尋 (Search Demo)
    print("\n🔎 SEARCH DEMO")
    print("-" * 60)
    query = input(
        "Enter a search query (e.g., 'money', 'fight', 'love', or press Enter to skip): "
    )
    if query:
        query_vec = model.encode([query])[0]
        sims = np.dot(embeddings, query_vec)

        # 取 Top 3
        top_indices = np.argsort(sims)[-3:][::-1]

        for idx in top_indices:
            print(
                f"✅ Match ({sims[idx] * 100:.1f}%): [{times[idx]:.1f}s] {texts[idx]}"
            )


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Semantic Analysis PoC")
    parser.add_argument("--uuid", default="384b0ff44aaaa1f1", help="Video UUID")
    parser.add_argument(
        "--topics", type=int, default=5, help="Number of topics to find"
    )
    args = parser.parse_args()

    run_analysis(args.uuid, args.topics)