OK 版本 (無 Toekn 認證)

app.py

@@ -1,80 +1,111 @@
 import os
-import gradio as gr
-from fastapi import FastAPI, UploadFile, Form, HTTPException
-from fastapi.responses import JSONResponse
+import time
+import shutil
+from pydub import AudioSegment
 from openai import OpenAI
-
-# === 初始化 ===
-print("===== 🚀 啟動中 =====")
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-APP_PASSWORD = os.getenv("APP_PASSWORD")
-
-print(f"OPENAI_API_KEY: {OPENAI_API_KEY[:10]}********")
-print(f"APP_PASSWORD: {APP_PASSWORD}")
-
-client = OpenAI(api_key=OPENAI_API_KEY)
-
-# === FastAPI 主要後端 ===
-app = FastAPI(title="LINE Audio Transcriber")
-
-@app.get("/ping")
-async def ping():
-    return {"status": "ok", "APP_PASSWORD": bool(APP_PASSWORD)}
-
-@app.post("/api/transcribe")
-async def transcribe_api(file: UploadFile, token: str = Form(...)):
-    print(f"📥 收到請求: {file.filename}, token={token}")
-
-    if not APP_PASSWORD:
-        raise HTTPException(status_code=500, detail="APP_PASSWORD not set.")
-    if token != APP_PASSWORD:
-        raise HTTPException(status_code=403, detail="Forbidden: invalid token")
-
-    temp_path = f"/tmp/{file.filename}"
-    with open(temp_path, "wb") as f:
-        f.write(await file.read())
-
-    # whisper 轉錄
-    with open(temp_path, "rb") as audio_file:
-        transcript = client.audio.transcriptions.create(model="whisper-1", file=audio_file)
-    text = transcript.text.strip()
-
-    # AI 摘要
-    summary_prompt = f"請用繁體中文摘要以下內容：\n\n{text}"
-    summary = client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[{"role": "user", "content": summary_prompt}]
-    ).choices[0].message.content.strip()
-
-    return JSONResponse({"text": text, "summary": summary})
-
-# === Gradio 介面 ===
-def gradio_ui(audio):
-    if audio is None:
-        return "請上傳音訊檔案", ""
-    with open(audio, "rb") as f:
-        transcript = client.audio.transcriptions.create(model="whisper-1", file=f)
-    text = transcript.text.strip()
-    summary_prompt = f"請用繁體中文摘要以下內容：\n\n{text}"
-    summary = client.chat.completions.create(
+import gradio as gr
+from fastapi import FastAPI, File, UploadFile
+
+# ========================
+# 🔐 基本設定
+# ========================
+PASSWORD = os.getenv("APP_PASSWORD", "defaultpass")
+MAX_SIZE = 25 * 1024 * 1024
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# FastAPI App for捷徑 API
+app = FastAPI()
+
+# ========================
+# 🎧 音訊轉錄核心
+# ========================
+def split_audio_if_needed(path: str):
+    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 = []
+    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")
+        parts.append(fn)
+    return parts
+
+def transcribe_core(path: str, model: str = "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 轉檔失敗：{e}")
+
+    chunks = split_audio_if_needed(path)
+    txts = []
+    for f in chunks:
+        with open(f, "rb") as af:
+            t = client.audio.transcriptions.create(
+                model=model, file=af, response_format="text"
+            )
+            txts.append(t)
+    full = "\n".join(txts)
+    summ = client.chat.completions.create(
         model="gpt-4o-mini",
-        messages=[{"role": "user", "content": summary_prompt}]
+        messages=[{"role": "user", "content": f"請用繁體中文摘要以下內容：\n{full}"}],
+        temperature=0.4,
     ).choices[0].message.content.strip()
-    return text, summary
+    return full, summ
 
-demo = gr.Interface(
-    fn=gradio_ui,
-    inputs=gr.Audio(type="filepath", label="上傳音訊"),
-    outputs=[gr.Textbox(label="轉錄文字"), gr.Textbox(label="AI 摘要")],
-    title="LINE 語音轉錄與摘要 (API + UI)",
-    description="可透過捷徑上傳音訊並取得文字摘要"
-)
-
-# ✅ 關鍵修正：Spaces 預期的是 "app" 為 FastAPI，而不是混合物件
-# 我們手動掛上 Gradio，而不是使用 mount_gradio_app
-from gradio.routes import mount_gradio_app
-
-gr_app = mount_gradio_app(app, demo, path="/")
-
-# Hugging Face 需要一個變數叫 application
-application = gr_app
+# ========================
+# 🌐 FastAPI 端點（捷徑用）
+# ========================
+@app.post("/api/transcribe")
+async def api_transcribe(file: UploadFile = File(...)):
+    """供 iPhone 捷徑上傳音訊並取得 JSON"""
+    temp = file.filename
+    with open(temp, "wb") as f:
+        f.write(await file.read())
+    text, summary = transcribe_core(temp)
+    os.remove(temp)
+    return {"text": text, "summary": summary}
+
+@app.get("/health")
+def health():
+    """捷徑可先 ping 這個確認服務運作中"""
+    return {"status": "ok", "time": int(time.time())}
+
+# ========================
+# 💬 Gradio 介面
+# ========================
+def transcribe_with_pw(password, file):
+    if password.strip() != PASSWORD:
+        return "❌ 密碼錯誤", "", ""
+    if not file:
+        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）")
+    pw = gr.Textbox(label="輸入密碼", type="password")
+    f = gr.File(label="上傳音訊檔 (.m4a/.mp3/.wav/.mp4)")
+    run = gr.Button("開始轉錄 🚀")
+    s = gr.Textbox(label="狀態", interactive=False)
+    t = gr.Textbox(label="逐字稿", lines=10)
+    su = gr.Textbox(label="摘要", lines=8)
+
+    run.click(transcribe_with_pw, [pw, f], [s, t, su])
+
+# ========================
+# 🚀 啟動（單一埠）
+# ========================
+# 讓 Gradio 介面掛載到 FastAPI
+gr.mount_gradio_app(app, demo, path="/")
+
+# Hugging Face 自動綁定 port=7860，不用手動設定
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)