Update app.py

app.py

@@ -5,16 +5,13 @@ import numpy as np
 import torch
 import librosa
 import uvicorn
-
 from fastapi import FastAPI, HTTPException, Security, Depends
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.security.api_key import APIKeyHeader
 from pydantic import BaseModel
 from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
 
-# ======================================================
-# CONFIG
-# ======================================================
+# Config
 API_KEY_NAME = "access_token"
 API_KEY_VALUE = "HCL_SECURE_KEY_2026"
 MODEL_ID = "melba-t/wav2vec2-fake-speech-detection" 
@@ -24,15 +21,13 @@ LABEL_MAP = {0: "HUMAN", 1: "AI_GENERATED"}
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger("hcl-api")
 
+# Initialize Model
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Load Model
 feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
 model = AutoModelForAudioClassification.from_pretrained(MODEL_ID).to(DEVICE)
 model.eval()
 
 app = FastAPI(title="HCL AI Voice Detection API")
-api_key_header = APIKeyHeader(name=API_KEY_NAME, auto_error=False)
 
 app.add_middleware(
     CORSMiddleware,
@@ -44,6 +39,8 @@ app.add_middleware(
 class AudioRequest(BaseModel):
     audio_base64: str
 
+api_key_header = APIKeyHeader(name=API_KEY_NAME, auto_error=False)
+
 async def verify_api_key(api_key: str = Security(api_key_header)):
     if api_key != API_KEY_VALUE:
         raise HTTPException(status_code=403, detail="Invalid API Key")
@@ -51,18 +48,17 @@ async def verify_api_key(api_key: str = Security(api_key_header)):
 
 def preprocess_audio(b64_string: str):
     try:
-        # Clean Base64 header and fix padding
         if "," in b64_string:
             b64_string = b64_string.split(",")[1]
         
+        # Correct padding
         missing_padding = len(b64_string) % 4
         if missing_padding:
             b64_string += "=" * (4 - missing_padding)
 
         audio_bytes = base64.b64decode(b64_string)
         
-        # Wrap bytes in BytesIO and load with librosa
-        # librosa handles MP3 decoding better than soundfile in many Linux envs
+        # Load via librosa for better MP3 compatibility
         with io.BytesIO(audio_bytes) as bio:
             audio, sr = librosa.load(bio, sr=TARGET_SR)
 
@@ -74,16 +70,15 @@ def preprocess_audio(b64_string: str):
         logger.error(f"Preprocessing error: {e}")
         raise ValueError(f"Decoding failed: {str(e)}")
 
+@app.get("/")
+def home():
+    return {"message": "API is running. Visit /docs for Swagger UI"}
+
 @app.post("/predict")
 async def predict(request: AudioRequest, _: str = Depends(verify_api_key)):
     try:
-        waveform = preprocess_audio(request.audio_base64)
-        
-        inputs = feature_extractor(
-            waveform, 
-            sampling_rate=TARGET_SR, 
-            return_tensors="pt"
-        ).to(DEVICE)
+        waveform = preprocess_audio(request.audio_base_64)
+        inputs = feature_extractor(waveform, sampling_rate=TARGET_SR, return_tensors="pt").to(DEVICE)
 
         with torch.inference_mode():
             logits = model(**inputs).logits
@@ -98,7 +93,8 @@ async def predict(request: AudioRequest, _: str = Depends(verify_api_key)):
     except ValueError as ve:
         raise HTTPException(status_code=400, detail=str(ve))
     except Exception as e:
+        logger.error(f"Prediction error: {e}")
         raise HTTPException(status_code=500, detail="Internal Server Error")
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=False)