Update app.py

app.py

@@ -1,137 +1,127 @@
-import logging
-from contextlib import contextmanager
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
-import tempfile
-import os
-import librosa
-import numpy as np
-import keras
-
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from io import BytesIO
-from models import User, Course, connection
-from forms import UserRegistration, UserLoginForm
-from fastapi.responses import JSONResponse
-from utils import (
-    create_cnn_model,
-    get_features,
-    extract_features,
-    pad_or_trim,
-    noise,
-    stretch,
-    pitch,
-)
-import numpy as np
-import tensorflow as tf
-import os
-from fastapi.middleware.cors import CORSMiddleware
-import logging
-
-app = FastAPI(port=8000)
-
-# origins = [
-#     "http://localhost:3000",
-#     "http://127.0.0.1:3000",
-#     # Add more origins if needed
-# ]
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],#origins,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-filepath = os.path.abspath("cnn_1_v6_final_model.h5")
-if not os.path.exists(filepath):
-    raise FileNotFoundError(f"Model file not found at {filepath}")
-
-model = keras.models.load_model(filepath, compile=False)
-target_shape = (32, 200)
-
-
-@app.post("/save-audio")
-async def save_audio(file: UploadFile = File(...)):
-    if not file.content_type.startswith("audio/"):
-        raise HTTPException(status_code=400, detail="Invalid file type")
-
-    file_path = os.path.join("audio", file.filename)
-    os.makedirs("audio", exist_ok=True)
-    try:
-        with open(file_path, "wb") as f:
-            content = await file.read()
-            f.write(content)
-        return JSONResponse(
-            content={"message": "File saved successfully", "filePath": file_path},
-            status_code=200,
-        )
-    except Exception as e:
-        return JSONResponse(content={"error": str(e)}, status_code=500)
-
-
-logging.basicConfig(
-    level=logging.INFO,
-    filename="server.log",
-    filemode="w",
-    format="%(asctime)s - %(levelname)s - %(message)s",
-)
-
-
-@contextmanager
-def temporary_audio_file(audio_bytes):
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
-        tmp_file.write(audio_bytes)
-        tmp_file.flush()  # Make sure data is written to disk
-        tmp_filename = tmp_file.name
-    try:
-        yield tmp_filename
-    finally:
-        if os.path.exists(tmp_filename):
-            os.remove(tmp_filename)
-
-
-@app.post("/process-audio")
-async def process_audio(audio: UploadFile = File(...)):
-    if audio.content_type != "audio/mpeg":
-        raise HTTPException(
-            status_code=400, detail="Invalid file type. Only MP3 files are supported."
-        )
-
-    try:
-        audio_bytes = await audio.read()
-        logging.info(
-            f"Received audio bytes: {len(audio_bytes)} bytes"
-        )  # Log size of audio bytes
-        with temporary_audio_file(audio_bytes) as tmp_filename:
-            logging.info(f"Temporary file created: {tmp_filename}")
-            audio_data, sample_rate = librosa.load(tmp_filename, sr=None)
-            logging.info(
-                f"Audio loaded: sample rate = {sample_rate}, data shape = {audio_data.shape}"
-            )
-            if not audio_data.any() or sample_rate == 0:
-                raise ValueError("Empty or invalid audio data.")
-
-            features = extract_features(audio_data, sample_rate)
-            logging.info(f"Features extracted: shape = {features.shape}")
-            target_shape = (1, model.input_shape[1])
-            features = pad_or_trim(features, target_shape[1])
-            features = np.expand_dims(features, axis=0)
-
-            prediction = model.predict(features)
-            # Add interpretation of prediction here (e.g., class labels)
-            logging.info(f"Prediction: {prediction}")
-            return {"prediction": prediction.tolist()}
-
-    except librosa.util.exceptions.ParameterError as e:
-        logging.error(f"Librosa error: {e}")
-        raise HTTPException(status_code=400, detail=f"Invalid audio file: {e}")
-    except ValueError as e:
-        logging.error(f"Value error: {e}")
-        raise HTTPException(status_code=400, detail=f"Invalid audio data: {e}")
-    except Exception as e:
-        logging.exception(f"Error processing audio: {e}")  # Log the full traceback
-        raise HTTPException(status_code=500, detail="Internal server error")
-
+import logging
+from contextlib import contextmanager
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+import tempfile
+import os
+import librosa
+import numpy as np
+import keras
+from utils import (
+    create_cnn_model,
+    get_features,
+    extract_features,
+    pad_or_trim,
+    noise,
+    stretch,
+    pitch,
+)
+import numpy as np
+
+app = FastAPI(port=8000)
+
+# origins = [
+#     "http://localhost:3000",
+#     "http://127.0.0.1:3000",
+#     # Add more origins if needed
+# ]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],#origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+filepath = os.path.abspath("cnn_1_v6_final_model.h5")
+if not os.path.exists(filepath):
+    raise FileNotFoundError(f"Model file not found at {filepath}")
+
+model = keras.models.load_model(filepath, compile=False)
+target_shape = (32, 200)
+
+
+@app.post("/save-audio")
+async def save_audio(file: UploadFile = File(...)):
+    if not file.content_type.startswith("audio/"):
+        raise HTTPException(status_code=400, detail="Invalid file type")
+
+    file_path = os.path.join("audio", file.filename)
+    os.makedirs("audio", exist_ok=True)
+    try:
+        with open(file_path, "wb") as f:
+            content = await file.read()
+            f.write(content)
+        return JSONResponse(
+            content={"message": "File saved successfully", "filePath": file_path},
+            status_code=200,
+        )
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)
+
+
+logging.basicConfig(
+    level=logging.INFO,
+    filename="server.log",
+    filemode="w",
+    format="%(asctime)s - %(levelname)s - %(message)s",
+)
+
+
+@contextmanager
+def temporary_audio_file(audio_bytes):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
+        tmp_file.write(audio_bytes)
+        tmp_file.flush()  # Make sure data is written to disk
+        tmp_filename = tmp_file.name
+    try:
+        yield tmp_filename
+    finally:
+        if os.path.exists(tmp_filename):
+            os.remove(tmp_filename)
+
+
+@app.post("/process-audio")
+async def process_audio(audio: UploadFile = File(...)):
+    if audio.content_type != "audio/mpeg":
+        raise HTTPException(
+            status_code=400, detail="Invalid file type. Only MP3 files are supported."
+        )
+
+    try:
+        audio_bytes = await audio.read()
+        logging.info(
+            f"Received audio bytes: {len(audio_bytes)} bytes"
+        )  # Log size of audio bytes
+        with temporary_audio_file(audio_bytes) as tmp_filename:
+            logging.info(f"Temporary file created: {tmp_filename}")
+            audio_data, sample_rate = librosa.load(tmp_filename, sr=None)
+            logging.info(
+                f"Audio loaded: sample rate = {sample_rate}, data shape = {audio_data.shape}"
+            )
+            if not audio_data.any() or sample_rate == 0:
+                raise ValueError("Empty or invalid audio data.")
+
+            features = extract_features(audio_data, sample_rate)
+            logging.info(f"Features extracted: shape = {features.shape}")
+            target_shape = (1, model.input_shape[1])
+            features = pad_or_trim(features, target_shape[1])
+            features = np.expand_dims(features, axis=0)
+
+            prediction = model.predict(features)
+            # Add interpretation of prediction here (e.g., class labels)
+            logging.info(f"Prediction: {prediction}")
+            return {"prediction": prediction.tolist()}
+
+    except librosa.util.exceptions.ParameterError as e:
+        logging.error(f"Librosa error: {e}")
+        raise HTTPException(status_code=400, detail=f"Invalid audio file: {e}")
+    except ValueError as e:
+        logging.error(f"Value error: {e}")
+        raise HTTPException(status_code=400, detail=f"Invalid audio data: {e}")
+    except Exception as e:
+        logging.exception(f"Error processing audio: {e}")  # Log the full traceback
+        raise HTTPException(status_code=500, detail="Internal server error")
+