#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import os
import joblib
import numpy as np
import librosa
import gradio as gr
import time
import re
from transformers import pipeline
from huggingface_hub import hf_hub_download
from deepface import DeepFace
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline


# --- 1. 下載並載入 SVM 模型 ---
# 這裡 repo_id 填你的模型倉庫路徑，例如 "GCLing/emotion-svm-model"
# filename 填上傳到該倉庫的檔案名，例如 "svm_emotion_model.joblib"
print("Downloading SVM model from Hugging Face Hub...")
model_path = hf_hub_download(repo_id="GCLing/emotion-svm-model", filename="svm_emotion_model.joblib")
print(f"SVM model downloaded to: {model_path}")
svm_model = joblib.load(model_path)
print("SVM model loaded.")

# --- 2. 載入文字情緒分析模型 ---
zero_shot = pipeline("zero-shot-classification", model="joeddav/xlm-roberta-large-xnli")
candidate_labels = ["joy", "sadness", "anger", "fear", "surprise", "disgust"]
label_map_en2cn = {
    "joy": "高興", "sadness": "悲傷", "anger": "憤怒",
    "fear": "恐懼", "surprise": "驚訝", "disgust": "厭惡"
}
emo_keywords = {
    "happy": ["開心","快樂","愉快","喜悦","喜悅","歡喜","興奮","高興"],
    "angry": ["生氣","憤怒","不爽","發火","火大","氣憤"],
    "sad": ["傷心","難過","哭","難受","心酸","憂","悲","哀","痛苦","慘","愁"],
    "surprise": ["驚訝","意外","嚇","驚詫","詫異","訝異","好奇"],
    "fear": ["怕","恐懼","緊張","懼","膽怯","畏"]
}
# 简单否定词列表
negations = ["不","沒","沒有","別","勿","非"]

# --- 3. 聲音特徵擷取函式 ---
def extract_feature(signal: np.ndarray, sr: int) -> np.ndarray:
    """
    從一段音訊 signal (numpy array) 和取樣率 sr 計算 MFCC 特徵 (13 維)，
    並回傳平均與變異組成的特徵向量 (共 26 維)。
    """
    # librosa 載入後 signal 為 float numpy array
    mfcc = librosa.feature.mfcc(y=signal, sr=sr, n_mfcc=13)
    # axis=1: 每個 MFCC 維度對時間做平均與變異數
    return np.concatenate([np.mean(mfcc, axis=1), np.var(mfcc, axis=1)])

# --- 4. 三種預測函式 ---

def predict_face(img):
    print("predict_face called, img is None?", img is None)
    if img is None:
        return {}
    try:
        res = DeepFace.analyze(img, actions=["emotion"], detector_backend="opencv")
        # 省略 list/dict 处理...
        # 直接取第一张人脸
        if isinstance(res, list):
            first = res[0] if res else {}
            emo = first.get("emotion", {}) if isinstance(first, dict) else {}
        else:
            emo = res.get("emotion", {}) if isinstance(res, dict) else {}
        print("predict_face result:", emo)
        return emo
    except Exception as e:
        print("DeepFace.analyze error:", e)
        return {}




def predict_voice(audio_path: str):
    # 如果没有录音文件路径，直接返回空字典或提示
    if not audio_path:
        # 可打印日志，帮助调试
        print("predict_voice: 收到 None 或空 audio_path，跳過分析")
        return {}
    try:
        signal, sr = librosa.load(audio_path, sr=None)
        # 提取特征
        feat = extract_feature(signal, sr)  # 你的特征提取函数
        probs = svm_model.predict_proba([feat])[0]
        labels = svm_model.classes_
        return {labels[i]: float(probs[i]) for i in range(len(labels))}
    except Exception as e:
        print("predict_voice error:", e)
        return {}



    
def predict_text_mixed(text: str):
    """
    先用 keyword_emotion 规则；若未命中再用 zero-shot 分类，
    返回 {中文标签: float_score} 的 dict，供 gr.Label 显示。
    """
    if not text or text.strip() == "":
        return {}
    # 规则优先
    res = keyword_emotion(text)
    if res:
        # 只返回最高那一项及其比例，也可返回完整分布
        top_emo = max(res, key=res.get)
        # 可将英文 key 转成中文，若需要
        # mapping: happy->高兴, angry->愤怒, etc.
        mapping = {
            "happy": "高兴",
            "angry": "愤怒",
            "sad": "悲伤",
            "surprise": "惊讶",
            "fear": "恐惧"
        }
        cn = mapping.get(top_emo, top_emo)
        return {cn: res[top_emo]}
    # 规则未命中，zero-shot fallback
    try:
        out = zero_shot(text, candidate_labels=candidate_labels,
                        hypothesis_template="这句话表达了{}情绪")
        result = {}
        for lab, sc in zip(out["labels"], out["scores"]):
            cn = label_map_en2cn.get(lab.lower(), lab)
            result[cn] = float(sc)
        return result
    except Exception as e:
        print("zero-shot error:", e)
        return {"中性": 1.0}





# --- 5. 建立 Gradio 介面 ---
with gr.Blocks() as demo:
    with gr.TabItem("臉部情緒"):
        with gr.Row():
            webcam = gr.Image(source="webcam", streaming=True, type="numpy", label="攝像頭畫面")
            face_out = gr.Label(label="情緒分布")
        webcam.stream(fn=predict_face, inputs=webcam, outputs=face_out)



        # 其餘 Tab 可按原先寫法，或用 Blocks 方式
        with gr.TabItem("語音情緒"):
            audio = gr.Audio(sources="microphone", streaming=False, type="filepath", label="錄音")
            audio_output = gr.Label(label="語音情緒結果")
            # 用 change/submit 触发：录音结束后调用 predict_voice
            audio.change(fn=predict_voice, inputs=audio, outputs=audio_output)

        with gr.TabItem("文字情緒"):
            gr.Markdown("### 文字情緒 分析 (规则+zero-shot)")
            with gr.Row():
                text = gr.Textbox(lines=3, placeholder="請輸入中文文字…")
                text_out = gr.Label(label="文字情緒結果")
            text.submit(fn=predict_text_mixed, inputs=text, outputs=text_out)


    

if __name__ == "__main__":
    
    demo.launch()
  # 不要传 server_name 或 server_port