Update app.py

app.py

@@ -4,12 +4,14 @@ import shutil
 from pydub import AudioSegment
 from openai import OpenAI
 import gradio as gr
-from fastapi import HTTPException
+from fastapi import FastAPI, UploadFile, File, Form
+from threading import Thread
+import uvicorn
 
 # ======================================================
 # 🔐 設定區
 # ======================================================
-PASSWORD = os.getenv("APP_PASSWORD", "defaultpass")
+PASSWORD = os.getenv("APP_PASSWORD", "chou")
 MAX_SIZE = 25 * 1024 * 1024
 client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
@@ -25,77 +27,82 @@ def split_audio_if_needed(path):
     if size <= MAX_SIZE:
         return [path]
     audio = AudioSegment.from_file(path)
-    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]
+    n = int(size / MAX_SIZE) + 1
+    chunk_ms = len(audio) / n
+    parts = []
+    for i in range(n):
         fn = f"chunk_{i+1}.wav"
-        chunk.export(fn, format="wav")
-        files.append(fn)
-    return files
+        audio[int(i * chunk_ms):int((i + 1) * chunk_ms)].export(fn, format="wav")
+        parts.append(fn)
+    return parts
 
 def transcribe_core(path, model="whisper-1"):
-    if path and path.lower().endswith(".mp4"):
-        fixed_path = path[:-4] + ".m4a"
+    if path.lower().endswith(".mp4"):
+        fixed = path[:-4] + ".m4a"
         try:
-            shutil.copy(path, fixed_path)
-            path = fixed_path
-            print("🔧 已自動修正 mp4 → m4a")
+            shutil.copy(path, fixed)
+            path = fixed
         except Exception as e:
             print(f"⚠️ mp4→m4a 轉檔失敗：{e}")
 
     chunks = split_audio_if_needed(path)
-    raw_parts = []
+    txts = []
     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)
+            res = client.audio.transcriptions.create(model=model, file=af, response_format="text")
+            txts.append(res)
+    full_raw = "\n".join(txts)
 
-    # 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" + full_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_resp = client.chat.completions.create(
+    trad = 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": "system", "content": "你是嚴格的繁體中文轉換器。"},
             {"role": "user", "content": conv_prompt}
         ],
         temperature=0.0,
-    )
-    full_trad = trad_resp.choices[0].message.content.strip()
+    ).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"
-        + full_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" + trad
     )
-    sum_resp = 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,
-    )
-    summ = sum_resp.choices[0].message.content.strip()
-    return full_trad, summ
+    ).choices[0].message.content.strip()
+
+    return trad, summ
+
+# ======================================================
+# 🌐 FastAPI for 捷徑
+# ======================================================
+api_app = FastAPI()
+
+@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}
 
 # ======================================================
 # 💬 Gradio 介面
 # ======================================================
 def transcribe_with_password(password, file):
     if password.strip() != PASSWORD:
-        raise HTTPException(status_code=403, detail="密碼錯誤 ❌")
+        return "❌ 密碼錯誤", "", ""
     if not file:
         return "⚠️ 未選擇檔案", "", ""
     text, summary = transcribe_core(file.name)
@@ -114,7 +121,11 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 # ======================================================
 # 🚀 啟動
 # ======================================================
+def run_api():
+    uvicorn.run(api_app, host="0.0.0.0", port=7861)
+
+Thread(target=run_api, daemon=True).start()
+app = demo  # ✅ Hugging Face 主入口使用 Gradio
+
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)
-else:
-    demo.launch()