Update app.py

app.py

@@ -1,97 +1,13 @@
-# from fastapi import FastAPI, File, UploadFile
-# import librosa
-# import numpy as np
-# import shutil
-# import uvicorn
-# import os
-# from funasr import AutoModel
-# from starlette.middleware import Middleware
-# from starlette.middleware.cors import CORSMiddleware
-
-# import logging
-
-# logging.basicConfig(level=logging.INFO)
-# logger = logging.getLogger(__name__)
-
-# app = FastAPI(
-#     middleware=[
-#         Middleware(
-#             CORSMiddleware,
-#             allow_origins=["*"],  # Cho phép tất cả các origin
-#             allow_credentials=True,
-#             allow_methods=["*"],  # Cho phép tất cả các phương thức
-#             allow_headers=["*"],  # Cho phép tất cả các header
-#         )
-#     ]
-# )
-# # Tạo thư mục temp nếu chưa có
-# if not os.path.exists("temp"):
-#     os.makedirs("temp")
-
-# # Load mô hình SenseVoiceSmall từ Hugging Face
-# model_dir = "FunAudioLLM/SenseVoiceSmall"
-# model = AutoModel(
-#     model=model_dir,
-#     vad_model="fsmn-vad",
-#     vad_kwargs={"max_single_segment_time": 30000},
-#     device="cuda:0",
-#     hub="hf",
-# )
-
-# # Hàm tính RMS energy
-# def calculate_rms_energy(audio_path):
-#     y, sr = librosa.load(audio_path)
-#     rms = librosa.feature.rms(y=y)[0]
-#     return np.mean(rms)
-
-# # Hàm phát hiện tiếng ồn
-# def detect_noise(audio_path):
-#     rms_energy = calculate_rms_energy(audio_path)
-#     res = model.generate(input=audio_path, language="auto", audio_event_detection=True)
-#     audio_events = res[0].get("audio_event_detection", {})
-
-#     if rms_energy > 0.02:
-#         return "ồn ào"
-#     elif rms_energy > 0.01:
-#         for event_label, event_score in audio_events.items():
-#             if event_score > 0.7 and event_label in ["laughter", "applause", "crying", "coughing"]:
-#                 return f"ồn ào ({event_label})"
-#     return "yên tĩnh"
-
-# @app.get("/")
-# def read_root():
-#     return {"message": "Hello, World!"}
-
-# print(app.routes)
-
-# # API nhận file âm thanh từ Flutter
-# @app.post("/detect-noise/")
-# async def detect_noise_api(file: UploadFile = File(...)):
-#     try:
-#         logger.info("Tên file: %s", file.filename)
-#         logger.info("Loại file: %s", file.content_type)
-#         file_size = len(await file.read())
-#         logger.info("Kích thước file: %s bytes", file_size)
-#         await file.seek(0)  # Reset lại vị trí đọc file
-
-#         file_path = f"temp/{file.filename}"
-#         with open(file_path, "wb") as buffer:
-#             shutil.copyfileobj(file.file, buffer)
-
-#         result = detect_noise(file_path)
-#         return {"noise_level": result}
-#     except Exception as e:
-#         logger.exception("Lỗi trong API: %s", e)
-#         return {"error": str(e)}
-
-# # Chạy FastAPI trên Hugging Face Spaces
-# if __name__ == "__main__":
-#     uvicorn.run(app, host="0.0.0.0", port=7860)
-
-
-from fastapi import FastAPI, UploadFile, File
+from fastapi import FastAPI, File, UploadFile
+import librosa
+import numpy as np
+import shutil
+import uvicorn
+import os
+from funasr import AutoModel
 from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
+
 import logging
 
 logging.basicConfig(level=logging.INFO)
@@ -101,17 +17,101 @@ app = FastAPI(
     middleware=[
         Middleware(
             CORSMiddleware,
-            allow_origins=["*"],
+            allow_origins=["*"],  # Cho phép tất cả các origin
             allow_credentials=True,
-            allow_methods=["*"],
-            allow_headers=["*"],
+            allow_methods=["*"],  # Cho phép tất cả các phương thức
+            allow_headers=["*"],  # Cho phép tất cả các header
         )
     ]
 )
+# Tạo thư mục temp nếu chưa có
+if not os.path.exists("temp"):
+    os.makedirs("temp")
+
+# Load mô hình SenseVoiceSmall từ Hugging Face
+model_dir = "FunAudioLLM/SenseVoiceSmall"
+model = AutoModel(
+    model=model_dir,
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device="cuda:0",
+    hub="hf",
+)
 
+# Hàm tính RMS energy
+def calculate_rms_energy(audio_path):
+    y, sr = librosa.load(audio_path)
+    rms = librosa.feature.rms(y=y)[0]
+    return np.mean(rms)
+
+# Hàm phát hiện tiếng ồn
+def detect_noise(audio_path):
+    rms_energy = calculate_rms_energy(audio_path)
+    res = model.generate(input=audio_path, language="auto", audio_event_detection=True)
+    audio_events = res[0].get("audio_event_detection", {})
+
+    if rms_energy > 0.02:
+        return "ồn ào"
+    elif rms_energy > 0.01:
+        for event_label, event_score in audio_events.items():
+            if event_score > 0.7 and event_label in ["laughter", "applause", "crying", "coughing"]:
+                return f"ồn ào ({event_label})"
+    return "yên tĩnh"
+
+@app.get("/")
+def read_root():
+    return {"message": "Hello, World!"}
+
+print(app.routes)
+
+# API nhận file âm thanh từ Flutter
 @app.post("/detect-noise/")
 async def detect_noise_api(file: UploadFile = File(...)):
-    logger.info("Đã nhận được yêu cầu!")
-    return {"message": "OK"}
+    try:
+        logger.info("Tên file: %s", file.filename)
+        logger.info("Loại file: %s", file.content_type)
+        file_size = len(await file.read())
+        logger.info("Kích thước file: %s bytes", file_size)
+        await file.seek(0)  # Reset lại vị trí đọc file
+
+        file_path = f"temp/{file.filename}"
+        with open(file_path, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+
+        result = detect_noise(file_path)
+        return {"noise_level": result}
+    except Exception as e:
+        logger.exception("Lỗi trong API: %s", e)
+        return {"error": str(e)}
+
+# Chạy FastAPI trên Hugging Face Spaces
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+
+# from fastapi import FastAPI, UploadFile, File
+# from starlette.middleware import Middleware
+# from starlette.middleware.cors import CORSMiddleware
+# import logging
+
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+
+# app = FastAPI(
+#     middleware=[
+#         Middleware(
+#             CORSMiddleware,
+#             allow_origins=["*"],
+#             allow_credentials=True,
+#             allow_methods=["*"],
+#             allow_headers=["*"],
+#         )
+#     ]
+# )
+
+# @app.post("/detect-noise/")
+# async def detect_noise_api(file: UploadFile = File(...)):
+#     logger.info("Đã nhận được yêu cầu!")
+#     return {"message": "OK"}