Update app.py

app.py

@@ -1,5 +1,5 @@
 # ======================================================
-# HCL AI VOICE DETECTION API – HF SPACES SAFE
+# HCL AI VOICE DETECTION API – HF SPACES (STABLE)
 # ======================================================
 
 import base64
@@ -13,7 +13,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.security.api_key import APIKeyHeader
 from pydantic import BaseModel
 
-from transformers import AutoProcessor, AutoModelForAudioClassification
+from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
 
 # ======================================================
 # CONFIG
@@ -21,7 +21,8 @@ from transformers import AutoProcessor, AutoModelForAudioClassification
 API_KEY_NAME = "access_token"
 API_KEY_VALUE = "HCL_SECURE_KEY_2026"
 
-MODEL_ID = "superb/wav2vec2-base-superb-ks"  # ✅ VERIFIED, EXISTS
+# ✅ VERIFIED audio-classification model
+MODEL_ID = "superb/wav2vec2-base-superb-ks"
 TARGET_SR = 16000
 
 # ======================================================
@@ -36,7 +37,7 @@ logger = logging.getLogger("voice-detection")
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 logger.info(f"Using device: {DEVICE}")
 
-processor = AutoProcessor.from_pretrained(MODEL_ID)
+feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
 model = AutoModelForAudioClassification.from_pretrained(MODEL_ID).to(DEVICE)
 model.eval()
 
@@ -57,7 +58,7 @@ app.add_middleware(
 )
 
 # ======================================================
-# SCHEMAS
+# SCHEMA
 # ======================================================
 class AudioRequest(BaseModel):
     audio_base64: str
@@ -87,7 +88,7 @@ def decode_audio(b64_audio: str):
 
 
 def analyze_voice(audio):
-    inputs = processor(
+    inputs = feature_extractor(
         audio,
         sampling_rate=TARGET_SR,
         return_tensors="pt"
@@ -100,8 +101,8 @@ def analyze_voice(audio):
         probs = torch.softmax(logits, dim=-1)
 
     confidence, pred = torch.max(probs, dim=-1)
-
     label = "AI_GENERATED" if pred.item() == 1 else "HUMAN"
+
     return label, round(confidence.item(), 4)