dev check working

app.py

@@ -296,3 +296,95 @@ def reencode_audio(input_path, output_path):
     ]
     subprocess.run(command, check=True)
 
+#--------------------------------------------------------------------------------------------------------------------
+from collections import Counter
+from datetime import datetime
+
+@app.post("/upload")
+async def upload_file(file: UploadFile = File(...)):
+    print(f"Received file: {file.filename}")
+
+    original_filename = file.filename.rsplit('.', 1)[0]
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    wav_filename = os.path.join(SAVE_DIR, f"{timestamp}.wav")
+    reencoded_filename = os.path.join(SAVE_DIR, f"{timestamp}_reencoded.wav")
+
+    # os.makedirs(SAVE_DIR, exist_ok=True)
+    with open(wav_filename, "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
+
+    reencode_audio(wav_filename, reencoded_filename)
+    os.remove(wav_filename)
+    print(f"File successfully re-encoded as: {reencoded_filename}")
+
+    try:
+        audio, sr = librosa.load(reencoded_filename, sr=None)  
+        print("Loaded successfully with librosa")
+    except Exception as e:
+        print(f"Error loading re-encoded file: {e}")
+    new_features = extract_features(reencoded_filename)
+    prediction, entropy = classify_audio(new_features)
+    with open(reencoded_filename, "rb") as audio_file:
+        audio_data = audio_file.read()
+
+    # audio_base64 = base64.b64encode(audio_data).decode('utf-8')
+    os.remove(reencoded_filename)
+    return JSONResponse(content={
+        "prediction": bool(prediction),
+        "entropy": float(entropy),
+    })
+    
+
+@app.post("/upload_audio")
+async def upload_file(file: UploadFile = File(...)):
+    print(f"Received file: {file.filename}")
+
+    original_filename = file.filename.rsplit('.', 1)[0]
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    wav_filename = os.path.join(SAVE_DIR, f"{timestamp}.wav")
+    reencoded_filename = os.path.join(SAVE_DIR, f"{timestamp}_reencoded.wav")
+
+    # os.makedirs(SAVE_DIR, exist_ok=True)
+    with open(wav_filename, "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
+
+    reencode_audio(wav_filename, reencoded_filename)
+    
+    os.remove(wav_filename)
+    print(f"File successfully re-encoded as: {reencoded_filename}")
+
+    try:
+        audio, sr = librosa.load(reencoded_filename, sr=None)  
+        print("Loaded successfully with librosa")
+    except Exception as e:
+        print(f"Error loading re-encoded file: {e}")
+    new_features = extract_features(reencoded_filename)
+    detector = UnifiedDeepfakeDetector()
+    print(reencoded_filename)
+    result = detector.analyze_audio_rf(reencoded_filename, model_choice="all")
+    prediction, entropy = classify_audio(new_features)
+    with open(reencoded_filename, "rb") as audio_file:
+        audio_data = audio_file.read()
+    result = list(result)
+    result.append("FAKE" if float(entropy) < 150 else "REAL")
+    print(result)
+    r_normalized = [x.upper() for x in result]
+    counter = Counter(r_normalized)
+
+    most_common_element, _ = counter.most_common(1)[0]
+
+    print(f"The most frequent element is: {most_common_element}") 
+    
+
+    audio_base64 = base64.b64encode(audio_data).decode('utf-8')
+    print(f"Audio Data Length: {len(audio_data)}")
+
+    os.remove(reencoded_filename)
+    return JSONResponse(content={
+        "filename": file.filename,
+        "prediction": most_common_element.upper(),
+        "entropy": float(entropy),
+        "audio": audio_base64,
+        "content_type": "audio/wav"
+    })
+