Update app.py

app.py

@@ -1,215 +1,78 @@
-import os
-import time
-import shutil
-import tempfile
-from typing import Tuple
-
+import os, shutil
 from pydub import AudioSegment
 from openai import OpenAI
-
 import gradio as gr
-from fastapi import FastAPI, UploadFile, File, Form, HTTPException
 
-# ========================
-# 🔐 設定
-# ========================
 PASSWORD = os.getenv("APP_PASSWORD", "chou")
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-MAX_SIZE = 25 * 1024 * 1024  # 25MB
-
-if not OPENAI_API_KEY:
-    raise RuntimeError("OPENAI_API_KEY 未設定（請到 HF 的 Secrets 設定）")
-
-client = OpenAI(api_key=OPENAI_API_KEY)
+MAX_SIZE = 25 * 1024 * 1024
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
-print("===== 🚀 啟動中 =====")
-print(f"APP_PASSWORD: {'✅ 已載入' if PASSWORD else '❌ 未載入'}")
-print(f"目前密碼內容：{PASSWORD}")
-
-# ========================
-# 🎧 轉錄核心
-# ========================
-def split_audio_if_needed(path: str) -> list:
+def split_audio(path):
     size = os.path.getsize(path)
     if size <= MAX_SIZE:
         return [path]
     audio = AudioSegment.from_file(path)
     n = int(size / MAX_SIZE) + 1
-    chunk_ms = len(audio) / n
     parts = []
+    chunk_ms = len(audio) / n
     for i in range(n):
         fn = f"chunk_{i+1}.wav"
-        audio[int(i * chunk_ms):int((i + 1) * chunk_ms)].export(fn, format="wav")
+        audio[int(i*chunk_ms):int((i+1)*chunk_ms)].export(fn, format="wav")
         parts.append(fn)
     return parts
 
-def transcribe_core(path: str, model: str = "whisper-1") -> Tuple[str, str]:
-    # iPhone LINE 部分 mp4 其實是 audio-only，這裡只改副檔名避免 MIME 阻擋
+def transcribe_core(path, model="whisper-1"):
     if path.lower().endswith(".mp4"):
         fixed = path[:-4] + ".m4a"
         try:
             shutil.copy(path, fixed)
             path = fixed
-            print("🔧 已自動修正 mp4 → m4a")
         except Exception as e:
-            print(f"⚠️ mp4→m4a 複製失敗：{e}")
+            print(f"⚠️ mp4→m4a 失敗: {e}")
 
-    # 1) Whisper 逐段轉錄（原始：可能有簡體）
-    chunks = split_audio_if_needed(path)
-    raw_parts = []
-    for f in chunks:
-        with open(f, "rb") as af:
-            res = client.audio.transcriptions.create(
-                model=model,
-                file=af,
-                response_format="text"
-            )
-            raw_parts.append(res)
-    full_raw = "\n".join(raw_parts)
+    chunks = split_audio(path)
+    raw = []
+    for c in chunks:
+        with open(c, "rb") as af:
+            txt = client.audio.transcriptions.create(model=model, file=af, response_format="text")
+            raw.append(txt)
+    raw_txt = "\n".join(raw)
 
-    # 2) 僅簡→繁（不意譯）
-    conv_prompt = (
-        "請將以下內容完整轉換為「繁體中文（台灣用語）」：\n"
-        "規則：1) 僅做簡→繁字形轉換；2) 不要意譯或改寫；3) 不要添加任何前後綴。\n"
-        "-----\n" + full_raw
-    )
-    full_trad = client.chat.completions.create(
+    conv = client.chat.completions.create(
         model="gpt-4o-mini",
         messages=[
-            {"role": "system", "content": "你是嚴格的繁體中文轉換器，只進行字形轉換。"},
-            {"role": "user", "content": conv_prompt}
-        ],
-        temperature=0.0,
-    ).choices[0].message.content.strip()
+            {"role":"system","content":"你是嚴格的繁體中文轉換器"},
+            {"role":"user","content":f"將以下內容轉為台灣繁體，不意譯：\n{raw_txt}"}
+        ], temperature=0.0)
+    trad = conv.choices[0].message.content.strip()
 
-    # 3) 摘要（長就條列、短就一句話）
-    sum_prompt = (
-        "請用台灣繁體中文撰寫摘要。若內容資訊多，可條列出重點；"
-        "若內容簡短，請用一句話概述即可。\n\n" + full_trad
-    )
-    summary = client.chat.completions.create(
+    summ = client.chat.completions.create(
         model="gpt-4o-mini",
         messages=[
-            {"role": "system", "content": "你是一位精準且嚴格使用台灣繁體中文的摘要助手。"},
-            {"role": "user", "content": sum_prompt}
-        ],
-        temperature=0.2,
-    ).choices[0].message.content.strip()
+            {"role":"system","content":"你是繁體摘要助手"},
+            {"role":"user","content":f"用條列或一句話摘要：\n{trad}"}
+        ], temperature=0.2)
+    return trad, summ.choices[0].message.content.strip()
 
-    return full_trad, summary
-
-# ========================
-# 🌐 FastAPI 主應用
-# ========================
-app = FastAPI(title="LINE Transcription (Gradio + API)")
-
-@app.get("/health")
-def health():
-    return {"status": "ok", "time": int(time.time())}
-
-@app.post("/api/transcribe")
-async def api_transcribe(
-    file: UploadFile = File(...),
-    token: str = Form(...),
-    model: str = Form("whisper-1")
-):
-    if token != PASSWORD:
-        raise HTTPException(status_code=403, detail="Invalid token")
-
-    # 以原副檔名建立臨時檔，避免沒有副檔名導致 pydub 判斷錯誤
-    suffix = ""
-    if "." in file.filename:
-        suffix = "." + file.filename.rsplit(".", 1)[-1]
-    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
-        tmp.write(await file.read())
-        tmp_path = tmp.name
-
-    try:
-        text, summary = transcribe_core(tmp_path, model=model)
-        return {"text": text, "summary": summary}
-    finally:
-        try:
-            os.remove(tmp_path)
-        except Exception:
-            pass
-
-# ========================
-# 💬 Gradio UI（掛在 /）
-# ========================
-def transcribe_with_password(password, file, model_choice, question):
+def transcribe(password, file):
     if password.strip() != PASSWORD:
-        return "❌ 密碼錯誤", "", "", ""
+        return "❌ 密碼錯誤", "", ""
     if not file:
-        return "⚠️ 未選擇檔案", "", "", ""
-
-    text, summary = transcribe_core(file.name, model=model_choice)
-
-    # 「進一步問 AI」：若使用者有填問題，就用轉錄全文回答
-    followup = ""
-    if question and question.strip():
-        prompt = (
-            "以下是逐字轉錄內容，請用台灣繁體中文回答我的問題：\n\n"
-            f"【逐字稿】\n{text}\n\n"
-            f"【問題】\n{question.strip()}"
-        )
-        followup = client.chat.completions.create(
-            model="gpt-4o-mini",
-            messages=[{"role": "user", "content": prompt}],
-            temperature=0.6,
-        ).choices[0].message.content.strip()
-
-    return "✅ 完成", text, summary, followup
+        return "⚠️ 未選擇檔案", "", ""
+    text, summary = transcribe_core(file.name)
+    return "✅ 完成", text, summary
 
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 🎧 LINE 語音轉錄與摘要工具（支援 .m4a / .mp4｜API + UI）")
-
-    with gr.Row():
-        pw = gr.Textbox(label="輸入密碼", type="password", placeholder="請輸入英文數字")
-        model_dd = gr.Dropdown(
-            ["whisper-1", "gpt-4o-mini-transcribe"],
-            value="whisper-1",
-            label="選擇模型"
-        )
-
-    file_u = gr.File(label="上傳音訊檔（.m4a/.mp3/.wav/.mp4）")
+    gr.Markdown("## 🎧 LINE 語音轉錄與摘要（Hugging Face 版）")
+    pw = gr.Textbox(label="密碼", type="password")
+    f = gr.File(label="上傳音訊檔")
     run = gr.Button("開始轉錄 🚀")
-
     s = gr.Textbox(label="狀態", interactive=False)
-    t = gr.Textbox(label="轉錄結果（已轉繁體）", lines=10)
+    t = gr.Textbox(label="轉錄結果", lines=10)
     su = gr.Textbox(label="AI 摘要", lines=8)
+    run.click(transcribe, [pw, f], [s, t, su])
 
-    with gr.Accordion("💬 進一步問 AI（針對上述逐字稿）", open=False):
-        q = gr.Textbox(label="輸入問題", lines=2, placeholder="例如：幫我整理我該如何回覆對方？")
-        ask = gr.Button("詢問 AI 🤔")
-        ans = gr.Textbox(label="AI 回覆", lines=8)
-
-    # 複製按鈕
-    copy_js = """
-    async (txt) => {
-        try { await navigator.clipboard.writeText(txt); alert("✅ 已複製到剪貼簿！"); }
-        catch(e){ alert("❌ 複製失敗：" + e); }
-    }
-    """
-
-    copy_t = gr.Button("📋 複製逐字稿")
-    copy_su = gr.Button("📋 複製摘要")
-    copy_ans = gr.Button("📋 複製 AI 回覆")
-
-    run.click(transcribe_with_password, [pw, file_u, model_dd, gr.State("")], [s, t, su, ans])
-    ask.click(
-        lambda text, question, pwd, model: transcribe_with_password(pwd, gr.State(None), model, question)[3],
-        [t, q, pw, model_dd],
-        [ans]
-    )
-
-    copy_t.click(fn=None, inputs=t, outputs=None, js=copy_js)
-    copy_su.click(fn=None, inputs=su, outputs=None, js=copy_js)
-    copy_ans.click(fn=None, inputs=ans, outputs=None, js=copy_js)
-
-# 把 Gradio 掛在 FastAPI 根路徑（/）
-app = gr.mount_gradio_app(app, demo, path="/")
+app = demo
 
-# 本地測試才會啟動 uvicorn；在 HF 上不需要
 if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    demo.launch(server_name="0.0.0.0", server_port=7860)