app.py

import gradio as gr
import os
import shutil
import matplotlib
import traceback
import subprocess
import re

# 1. 核心修复：强制使用非交互式后端
matplotlib.use('Agg')

# 导入你的核心模块
from score_eval import MIDIPitchEvaluator
from pitch_error_filter import SRTPitchErrorFilter
from audio_cliper import AudioClipExtractor
from pitch_animation import PitchAnimationGenerator

# 设定最大支持展示的句段数
MAX_VISIBLE_SEGMENTS = 30

def check_ffmpeg():
    """检查 FFmpeg 是否在环境中可用"""
    try:
        subprocess.check_output(["ffmpeg", "-version"])
        return True
    except Exception:
        return False

def process_pipeline(orig_midi_file, cover_midi_file, orig_audio_file, cover_audio_file, tolerance, severe_rate):
    """
    全流程处理：支持流式视频渲染、逐个展示对比块、过滤短句
    """
    base_dir = os.path.dirname(os.path.abspath(__file__))
    os.chdir(base_dir)

    # --- 初始化状态容器 ---
    log = "🚀 任务启动...\n"
    report_md = "### 📊 等待评分计算..."
    
    # 槽位更新列表：[Log, Report, Row1, V1, A1, Row2, V2, A2...]
    current_slots = []
    for _ in range(MAX_VISIBLE_SEGMENTS):
        current_slots.extend([
            gr.update(visible=False), # Row
            gr.update(value=None),    # Video
            gr.update(value=None)     # Audio
        ])
    
    yield [log, report_md] + current_slots

    if not check_ffmpeg():
        log += "❌ 错误：系统中未检测到 FFmpeg。\n"
        yield [log, report_md] + current_slots
        return

    # 初始化输出目录
    output_dirs = ["results", "filtered_srt", "audio_clips", "pitch_animation"]
    for d in output_dirs:
        dir_path = os.path.join(base_dir, d)
        if os.path.exists(dir_path): shutil.rmtree(dir_path)
        os.makedirs(dir_path, exist_ok=True)

    try:
        # --- Step 1: MIDI 评价 ---
        log += "Step 1: 正在进行 MIDI 评价分析...\n"
        evaluator = MIDIPitchEvaluator(orig_midi_file.name, cover_midi_file.name, tolerance=tolerance)
        if evaluator.evaluate():
            stats = evaluator.statistics
            log += f"✅ Step 1 完成！基础得分: {stats['overall_score']:.1f}\n"
            report_md = f"""
### 📊 MIDI 评价最终报告
| 指标项目 | 统计结果 |
| :--- | :--- |
| **最终评价得分** | **{stats['overall_score']:.1f} / 100** |
| 音高准确率 | {stats['accurate_pitch_percentage']:.2f}% |
| 平均音高偏差 | {stats['mean_abs_pitch_dev']:.2f} 半音 |
| 严重走音比例 | {stats['severe_off_percentage']:.2f}% |
| 节奏总偏差 | {stats['total_time_total']:.3f} 秒 |

---
**📉 扣分详情：**
- **音准惩罚**: `-{100 - stats['base_score']:.1f}`
- **漏唱惩罚**: `-{stats['unmatched_penalty']:.1f}`
- **节奏惩罚**: `-{stats['time_penalty']:.1f}`
            """
        else:
            log += "❌ Step 1 失败。\n"
            yield [log, report_md] + current_slots
            return
        yield [log, report_md] + current_slots

        # --- Step 2 & 3: 筛选与切片 ---
        log += "Step 2: 正在解析并筛选句段...\n"
        srt_path = os.path.join(base_dir, "results", "音高与节奏对比字幕.srt")
        filter_tool = SRTPitchErrorFilter(srt_path, "filtered_srt")
        filter_tool.parse_srt()
        filter_tool.detect_sentences()
        filter_tool.filter_sentences(min_severe_error_rate=severe_rate)
        filter_tool.generate_filtered_srt()
        
        log += "Step 3: 正在切分原唱音频片段...\n"
        yield [log, report_md] + current_slots
        extractor = AudioClipExtractor(os.path.join("filtered_srt", "走音句段汇总报告.txt"), 
                                       orig_audio_file.name, cover_audio_file.name, "audio_clips")
        extractor.clip_audio()

        # --- Step 4: 流式视频渲染 ---
        log += "Step 4: 生成对比视频 (已跳过不足1秒的句段)...\n"
        generator = PitchAnimationGenerator(
            summary_report_path=os.path.join("filtered_srt", "走音句段汇总报告.txt"),
            pitch_srt_path=os.path.join("results", "音高与节奏对比字幕.srt"),
            original_audio_dir=os.path.join("audio_clips", "original"),
            cover_audio_dir=os.path.join("audio_clips", "cover")
        )

        orig_audio_map = {}
        orig_dir = os.path.join("audio_clips", "original")
        if os.path.exists(orig_dir):
            for f in os.listdir(orig_dir):
                m = re.search(r'句段(\d+)', f)
                if m: orig_audio_map[int(m.group(1))] = os.path.join(orig_dir, f)

        # 核心循环渲染：增加时长过滤提示
        display_idx = 0 # 记录实际在界面显示的序号
        for i, segment in enumerate(generator.pitch_segments):
            seq_num = segment['seq_num']
            duration = segment.get('duration', 0)

            # --- 核心修改：时长过滤与日志提示 ---
            if duration < 1.0:
                log += f"⏳ 句段 {seq_num} 时长为 {duration:.2f}s (不足1秒)，跳过视频生成。\n"
                yield [log, report_md] + current_slots
                continue

            if display_idx >= MAX_VISIBLE_SEGMENTS: 
                log += f"⚠️ 已达到最大展示限制，后续句段不再显示。\n"
                break
            
            log += f"🎬 正在渲染句段 {seq_num}...\n"
            yield [log, report_md] + current_slots 
            
            video_path = generator.generate_single_animation(segment)
            
            if video_path and os.path.exists(video_path):
                # 填充到 UI
                slot_ptr = display_idx * 3
                current_slots[slot_ptr] = gr.update(visible=True)
                current_slots[slot_ptr + 1] = gr.update(value=video_path)
                if seq_num in orig_audio_map:
                    current_slots[slot_ptr + 2] = gr.update(value=orig_audio_map[seq_num])
                
                log += f"✅ 句段 {seq_num} 完成并展示。\n"
                display_idx += 1
                yield [log, report_md] + current_slots

        log += f"🎉 全部流程已完成！共展示 {display_idx} 个句段。\n"
        yield [log, report_md] + current_slots

    except Exception as e:
        log += f"❌ 运行异常: {str(e)}\n"
        yield [log, report_md] + current_slots

# --- Gradio 界面设计保持不变 ---
with gr.Blocks(title="MIDI Pitch Analysis Pro", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🎤 MIDI 音准评价与对比可视化系统")
    
    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown("### 📤 文件上传")
            with gr.Row():
                in_orig_midi = gr.File(label="原唱 MIDI", file_types=[".mid"])
                in_cover_midi = gr.File(label="翻唱 MIDI", file_types=[".mid"])
            with gr.Row():
                in_orig_audio = gr.File(label="原唱音频", file_types=["audio"])
                in_cover_audio = gr.File(label="翻唱音频", file_types=["audio"])
            gr.Markdown("### ⚙️ 设置")
            with gr.Row():
                in_tolerance = gr.Slider(0, 2, value=0, step=1, label="音准宽容度")
                in_severe_rate = gr.Slider(5, 60, value=20, step=5, label="严重走音筛选阈值 (%)")
            btn_run = gr.Button("🚀 开始全流程分析", variant="primary")

        with gr.Column(scale=3):
            out_report = gr.Markdown(value="等待分析...")
            out_log = gr.Textbox(label="实时流水线日志", lines=12, interactive=False)
            
    gr.Markdown("---")
    gr.Markdown("### 🎬 句段详细对比 (左侧：音高动画 | 右侧：原唱音频)")

    all_ui_components = []
    for i in range(MAX_VISIBLE_SEGMENTS):
        with gr.Row(variant="panel", visible=False) as row:
            v = gr.Video(label=f"对比动画槽位 {i+1}", interactive=False)
            a = gr.Audio(label=f"原唱片段参考", interactive=False)
            all_ui_components.extend([row, v, a])

    btn_run.click(
        fn=process_pipeline,
        inputs=[in_orig_midi, in_cover_midi, in_orig_audio, in_cover_audio, in_tolerance, in_severe_rate],
        outputs=[out_log, out_report] + all_ui_components
    )

if __name__ == "__main__":
    demo.launch()