Update app.py

app.py

@@ -1,17 +1,15 @@
 import os
 import time
 import shutil
-import threading
 from pydub import AudioSegment
 from openai import OpenAI
 import gradio as gr
-from fastapi import FastAPI, UploadFile, File, Form
-import uvicorn
+from fastapi import HTTPException
 
 # ======================================================
 # 🔐 設定區
 # ======================================================
-PASSWORD = os.getenv("APP_PASSWORD", "chou")
+PASSWORD = os.getenv("APP_PASSWORD", "defaultpass")
 MAX_SIZE = 25 * 1024 * 1024
 client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
@@ -27,72 +25,77 @@ def split_audio_if_needed(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):
+    num = int(size / MAX_SIZE) + 1
+    chunk_ms = len(audio) / num
+    files = []
+    for i in range(num):
+        start, end = int(i * chunk_ms), int((i + 1) * chunk_ms)
+        chunk = audio[start:end]
         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
+        chunk.export(fn, format="wav")
+        files.append(fn)
+    return files
 
 def transcribe_core(path, model="whisper-1"):
-    if path.lower().endswith(".mp4"):
-        fixed = path[:-4] + ".m4a"
+    if path and path.lower().endswith(".mp4"):
+        fixed_path = path[:-4] + ".m4a"
         try:
-            shutil.copy(path, fixed)
-            path = fixed
+            shutil.copy(path, fixed_path)
+            path = fixed_path
             print("🔧 已自動修正 mp4 → m4a")
         except Exception as e:
             print(f"⚠️ mp4→m4a 轉檔失敗：{e}")
 
-    # Whisper 轉錄
     chunks = split_audio_if_needed(path)
-    txts = []
+    raw_parts = []
     for f in chunks:
         with open(f, "rb") as af:
             res = client.audio.transcriptions.create(
-                model=model, file=af, response_format="text"
+                model=model,
+                file=af,
+                response_format="text"
             )
-            txts.append(res)
-    raw = "\n".join(txts)
+            raw_parts.append(res)
+    full_raw = "\n".join(raw_parts)
 
-    # 簡→繁
+    # 簡轉繁
     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" + raw
+        "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
     )
-    trad = client.chat.completions.create(
+    trad_resp = client.chat.completions.create(
         model="gpt-4o-mini",
         messages=[
-            {"role": "system", "content": "你是嚴格的繁體中文轉換器。"},
+            {"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()
+    )
+    full_trad = trad_resp.choices[0].message.content.strip()
 
     # 摘要
     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" + trad
+        "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
     )
-    summ = client.chat.completions.create(
+    sum_resp = 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()
-
-    return trad, summ
+    )
+    summ = sum_resp.choices[0].message.content.strip()
+    return full_trad, summ
 
 # ======================================================
 # 💬 Gradio 介面
 # ======================================================
 def transcribe_with_password(password, file):
     if password.strip() != PASSWORD:
-        return "❌ 密碼錯誤", "", ""
+        raise HTTPException(status_code=403, detail="密碼錯誤 ❌")
     if not file:
         return "⚠️ 未選擇檔案", "", ""
     text, summary = transcribe_core(file.name)
@@ -109,33 +112,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     run.click(transcribe_with_password, [pw, f], [s, t, su])
 
 # ======================================================
-# 🌐 FastAPI for 捷徑
-# ======================================================
-api_app = FastAPI(title="LINE Transcription API")
-
-@api_app.post("/api/transcribe")
-async def api_transcribe(file: UploadFile = File(...), token: str = Form(...)):
-    if token != PASSWORD:
-        return {"error": "Invalid token"}
-    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}
-
-@api_app.get("/api/health")
-def health():
-    return {"status": "ok", "time": int(time.time())}
-
-# ======================================================
-# 🚀 啟動設定（Gradio 為主入口，FastAPI 背景執行）
+# 🚀 啟動
 # ======================================================
-def run_api():
-    uvicorn.run(api_app, host="0.0.0.0", port=7861, log_level="info")
-
-threading.Thread(target=run_api, daemon=True).start()
-app = demo  # 讓 Hugging Face 正確掛載主頁
-
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)
+else:
+    demo.launch()