Update app.py

app.py

@@ -4,7 +4,7 @@ import tempfile
 import torch
 import spaces
 import gradio as gr
-from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+from fastapi import UploadFile, File, Form, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, PlainTextResponse
 from transformers import pipeline
@@ -26,18 +26,29 @@ def run_whisper(audio_path: str, target_language: str = None, is_translate: bool
     generate_kwargs = {}
     if target_language:
         generate_kwargs["language"] = target_language
+
     if is_translate:
         generate_kwargs["language"] = "english"
         generate_kwargs["task"] = "translate"
+
     result = pipe(audio_path, generate_kwargs=generate_kwargs)
     return result["text"]
 
-# 3. 先创建主 FastAPI 应用
-from fastapi import FastAPI, UploadFile, File, Form, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse, PlainTextResponse
+# 3. Gradio 界面定义
+def gradio_predict(audio_path):
+    if audio_path is None:
+        return "请上传音频文件！"
+    return run_whisper(audio_path)
+
+demo = gr.Interface(
+    fn=gradio_predict,
+    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="输入音频"),
+    outputs=gr.Textbox(label="识别结果"),
+    title="Whisper API Node"
+)
 
-app = FastAPI()
+# 4. 获取 FastAPI 实例并添加 CORS 跨域支持
+app = demo.app
 
 app.add_middleware(
     CORSMiddleware,
@@ -47,12 +58,12 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# 4. 通用音频处理函数
 async def process_audio(file: UploadFile, response_format: str, language: str, is_translate: bool):
     suffix = os.path.splitext(file.filename)[1] or ".wav"
     with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_file:
         shutil.copyfileobj(file.file, temp_file)
         temp_path = temp_file.name
+
     try:
         text = run_whisper(temp_path, target_language=language, is_translate=is_translate)
     except Exception as e:
@@ -60,11 +71,11 @@ async def process_audio(file: UploadFile, response_format: str, language: str, i
     finally:
         if os.path.exists(temp_path):
             os.remove(temp_path)
+
     if response_format in ["text", "vtt", "srt"]:
         return PlainTextResponse(text)
     return JSONResponse(content={"text": text})
 
-# 5. API 路由（先于 Gradio 挂载）
 @app.post("/v1/audio/transcriptions")
 async def transcribe_api(
     file: UploadFile = File(...),
@@ -82,23 +93,5 @@ async def translate_api(
 ):
     return await process_audio(file, response_format, language="english", is_translate=True)
 
-# 6. Gradio 界面
-def gradio_predict(audio_path):
-    if audio_path is None:
-        return "请上传音频文件！"
-    return run_whisper(audio_path)
-
-demo = gr.Interface(
-    fn=gradio_predict,
-    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="输入音频"),
-    outputs=gr.Textbox(label="识别结果"),
-    title="Whisper API Node"
-)
-
-# 7. 将 Gradio 挂载到主应用（作为子应用）
-app.mount("/", demo.app)
-#注意： 去掉 demo.launch()，HF Spaces 会自动检测 app 对象并用 uvicorn 启动。如果 Spaces 没有自动识别，在文件末尾加：
-# 仅本地调试用，Spaces 上会自动忽略
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+# 5. 启动服务
+demo.launch()