Update app.py

app.py

@@ -1,14 +1,10 @@
-# ======================================================
-# HCL AI VOICE DETECTION API – HACKATHON SUBMISSION
-# ======================================================
-
 import base64
 import io
 import logging
 import numpy as np
 import torch
-import soundfile as sf
 import librosa
+import uvicorn
 
 from fastapi import FastAPI, HTTPException, Security, Depends
 from fastapi.middleware.cors import CORSMiddleware
@@ -17,42 +13,26 @@ from pydantic import BaseModel
 from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
 
 # ======================================================
-# CONFIG & REQUIREMENTS MAPPING
+# CONFIG
 # ======================================================
-# The hackathon requires specific classification results
-LABEL_MAP = {
-    0: "HUMAN",
-    1: "AI_GENERATED"
-}
-
 API_KEY_NAME = "access_token"
-API_KEY_VALUE = "HCL_SECURE_KEY_2026"  # Ensure this matches your submission docs
-
-# Using a model fine-tuned for Deepfake/Synthetic Voice Detection
+API_KEY_VALUE = "HCL_SECURE_KEY_2026"
 MODEL_ID = "melba-t/wav2vec2-fake-speech-detection" 
 TARGET_SR = 16000
+LABEL_MAP = {0: "HUMAN", 1: "AI_GENERATED"}
 
-# ======================================================
-# INITIALIZATION
-# ======================================================
 logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("hcl-voice-safety")
+logger = logging.getLogger("hcl-api")
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-logger.info(f"Loading model to {DEVICE}...")
 
-try:
-    feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
-    model = AutoModelForAudioClassification.from_pretrained(MODEL_ID).to(DEVICE)
-    model.eval()
-    logger.info("Model loaded successfully.")
-except Exception as e:
-    logger.error(f"Failed to load model: {e}")
+# Load Model
+feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
+model = AutoModelForAudioClassification.from_pretrained(MODEL_ID).to(DEVICE)
+model.eval()
 
-# ======================================================
-# FASTAPI SETUP
-# ======================================================
 app = FastAPI(title="HCL AI Voice Detection API")
+api_key_header = APIKeyHeader(name=API_KEY_NAME, auto_error=False)
 
 app.add_middleware(
     CORSMiddleware,
@@ -64,87 +44,61 @@ app.add_middleware(
 class AudioRequest(BaseModel):
     audio_base64: str
 
-api_key_header = APIKeyHeader(name=API_KEY_NAME, auto_error=False)
-
-# ======================================================
-# UTILITIES
-# ======================================================
 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")
     return api_key
 
 def preprocess_audio(b64_string: str):
-    """Decodes base64 MP3/WAV and converts to 16kHz Mono."""
     try:
-        # Strip header if present (e.g., data:audio/mp3;base64,...)
+        # Clean Base64 header and fix padding
         if "," in b64_string:
             b64_string = b64_string.split(",")[1]
         
+        missing_padding = len(b64_string) % 4
+        if missing_padding:
+            b64_string += "=" * (4 - missing_padding)
+
         audio_bytes = base64.b64decode(b64_string)
         
-        # Use soundfile for reading. Note: For MP3, ensure 'audioread' or 'ffmpeg' is in the environment
+        # Wrap bytes in BytesIO and load with librosa
+        # librosa handles MP3 decoding better than soundfile in many Linux envs
         with io.BytesIO(audio_bytes) as bio:
-            audio, sr = sf.read(bio)
+            audio, sr = librosa.load(bio, sr=TARGET_SR)
 
-        # Convert to Mono if Stereo
-        if len(audio.shape) > 1:
-            audio = np.mean(audio, axis=1)
-
-        # Resample to 16kHz
-        if sr != TARGET_SR:
-            audio = librosa.resample(audio.astype(np.float32), orig_sr=sr, target_sr=TARGET_SR)
-
-        # Normalization & Padding for stability
-        audio = np.nan_to_num(audio)
         if len(audio) < TARGET_SR:
             audio = np.pad(audio, (0, TARGET_SR - len(audio)))
 
         return audio.astype(np.float32)
     except Exception as e:
-        logger.error(f"Audio processing error: {e}")
-        raise ValueError("Could not decode audio. Ensure it is a valid Base64 MP3/WAV.")
-
-# ======================================================
-# ENDPOINTS
-# ======================================================
-@app.get("/health")
-def health():
-    return {"status": "active", "device": DEVICE}
+        logger.error(f"Preprocessing error: {e}")
+        raise ValueError(f"Decoding failed: {str(e)}")
 
 @app.post("/predict")
 async def predict(request: AudioRequest, _: str = Depends(verify_api_key)):
-    """
-    Analyzes voice sample and classifies as AI_GENERATED or HUMAN.
-    """
     try:
-        # 1. Preprocess
         waveform = preprocess_audio(request.audio_base64)
         
-        # 2. Inference
         inputs = feature_extractor(
             waveform, 
             sampling_rate=TARGET_SR, 
-            return_tensors="pt", 
-            padding=True
+            return_tensors="pt"
         ).to(DEVICE)
 
         with torch.inference_mode():
             logits = model(**inputs).logits
             probs = torch.softmax(logits, dim=-1)
 
-        # 3. Get results
         confidence, pred_idx = torch.max(probs, dim=-1)
-        label = LABEL_MAP.get(int(pred_idx.item()), "UNKNOWN")
-
-        # 4. Return structured JSON
+        
         return {
-            "classification": label,
+            "classification": LABEL_MAP.get(int(pred_idx.item()), "UNKNOWN"),
             "confidence_score": round(float(confidence.item()), 4)
         }
-
     except ValueError as ve:
         raise HTTPException(status_code=400, detail=str(ve))
     except Exception as e:
-        logger.exception("Prediction failed")
-        raise HTTPException(status_code=500, detail="Internal server error during analysis")
+        raise HTTPException(status_code=500, detail="Internal Server Error")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)