Update app.py

app.py

@@ -1,187 +1,196 @@
-# app.py - Main application file
-
-import os
-import sys
-import logging
-import traceback
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s',
-    datefmt='%Y-%m-%d %H:%M:%S'
-)
-logger = logging.getLogger("speech_api")
-
-# Set all cache directories to locations within /tmp
-cache_dirs = {
-    "HF_HOME": "/tmp/hf_home",
-    "TRANSFORMERS_CACHE": "/tmp/transformers_cache",
-    "HUGGINGFACE_HUB_CACHE": "/tmp/huggingface_hub_cache",
-    "TORCH_HOME": "/tmp/torch_home",
-    "XDG_CACHE_HOME": "/tmp/xdg_cache"
-}
-
-# Set environment variables and create directories
-for env_var, path in cache_dirs.items():
-    os.environ[env_var] = path
-    try:
-        os.makedirs(path, exist_ok=True)
-        logger.info(f"📁 Created cache directory: {path}")
-    except Exception as e:
-        logger.error(f"❌ Failed to create directory {path}: {str(e)}")
-
-# Now import the rest of the libraries
-try:
-    import librosa
-    import glob
-    import numpy as np
-    import torch
-    from pydub import AudioSegment
-    import tempfile
-    import soundfile as sf
-    from flask import Flask, request, jsonify, send_file, g
-    from flask_cors import CORS
-    from werkzeug.utils import secure_filename
-
-    # Import functionality from other modules
-    from translator import (
-        init_models, check_model_status, handle_asr_request,
-        handle_tts_request, handle_translation_request
-    )
-    from evaluate import (
-        handle_evaluation_request, handle_upload_reference,
-        init_reference_audio, calculate_similarity
-    )
-
-    logger.info("✅ All required libraries imported successfully")
-except ImportError as e:
-    logger.critical(f"❌ Failed to import necessary libraries: {str(e)}")
-    sys.exit(1)
-
-# Check CUDA availability
-if torch.cuda.is_available():
-    logger.info(f"🚀 CUDA available: {torch.cuda.get_device_name(0)}")
-    device = "cuda"
-else:
-    logger.info("⚠️ CUDA not available, using CPU")
-    device = "cpu"
-
-# Constants
-SAMPLE_RATE = 16000
-OUTPUT_DIR = "/tmp/audio_outputs"
-REFERENCE_AUDIO_DIR = "./reference_audio"
-
-try:
-    os.makedirs(OUTPUT_DIR, exist_ok=True)
-    logger.info(f"📁 Created output directory: {OUTPUT_DIR}")
-except Exception as e:
-    logger.error(f"❌ Failed to create output directory: {str(e)}")
-
-# Initialize Flask app
-app = Flask(__name__)
-CORS(app)
-
-# Load models
-init_models(device)
-
-
-# Define routes
-@app.route("/", methods=["GET"])
-def home():
-    return jsonify({"message": "Speech API is running", "status": "active"})
-
-
-@app.route("/health", methods=["GET"])
-def health_check():
-    health_status = check_model_status()
-    health_status["api_status"] = "online"
-    health_status["device"] = device
-    return jsonify(health_status)
-
-
-@app.route("/asr", methods=["POST"])
-def transcribe_audio():
-    return handle_asr_request(request, OUTPUT_DIR, SAMPLE_RATE)
-
-
-@app.route("/tts", methods=["POST"])
-def generate_tts():
-    return handle_tts_request(request, OUTPUT_DIR)
-
-
-@app.route("/translate", methods=["POST"])
-def translate_text():
-    return handle_translation_request(request)
-
-
-@app.route("/download/<filename>", methods=["GET"])
-def download_audio(filename):
-    file_path = os.path.join(OUTPUT_DIR, filename)
-    if os.path.exists(file_path):
-        logger.info(f"📤 Serving audio file: {file_path}")
-        return send_file(file_path, mimetype="audio/wav", as_attachment=True)
-
-    logger.warning(f"⚠️ Requested file not found: {file_path}")
-    return jsonify({"error": "File not found"}), 404
-
-
-@app.route("/evaluate", methods=["POST"])
-def evaluate_pronunciation():
-    return handle_evaluation_request(request, REFERENCE_AUDIO_DIR, OUTPUT_DIR, SAMPLE_RATE)
-
-
-@app.route("/check_references", methods=["GET"])
-def check_references():
-    """Endpoint to check if reference files exist and are accessible"""
-    ref_patterns = ["mayap_a_abak", "mayap_a_ugtu", "mayap_a_gatpanapun",
-                    "mayap_a_bengi", "komusta_ka"]
-    results = {}
-
-    for pattern in ref_patterns:
-        pattern_dir = os.path.join(REFERENCE_AUDIO_DIR, pattern)
-        if os.path.exists(pattern_dir):
-            wav_files = glob.glob(os.path.join(pattern_dir, "*.wav"))
-            results[pattern] = {
-                "exists": True,
-                "path": pattern_dir,
-                "file_count": len(wav_files),
-                "files": [os.path.basename(f) for f in wav_files]
-            }
-        else:
-            results[pattern] = {
-                "exists": False,
-                "path": pattern_dir
-            }
-
-    return jsonify({
-        "reference_audio_dir": REFERENCE_AUDIO_DIR,
-        "directory_exists": os.path.exists(REFERENCE_AUDIO_DIR),
-        "patterns": results
-    })
-
-
-@app.route("/upload_reference", methods=["POST"])
-def upload_reference_audio():
-    return handle_upload_reference(request, REFERENCE_AUDIO_DIR, SAMPLE_RATE)
-
-
-# Add an initialization route that will be called before the first request
-@app.before_request
-def before_request():
-    if not hasattr(g, 'initialized'):
-        init_reference_audio(REFERENCE_AUDIO_DIR, OUTPUT_DIR)
-        g.initialized = True
-
-
-if __name__ == "__main__":
-    init_reference_audio(REFERENCE_AUDIO_DIR, OUTPUT_DIR)
-    logger.info("🚀 Starting Speech API server")
-
-    # Get the status for logging
-    status = check_model_status()
-    logger.info(f"📊 System status: ASR model: {'✅' if status['asr_model'] == 'loaded' else '❌'}")
-    for lang, model_status in status['tts_models'].items():
-        logger.info(f"📊 TTS model {lang}: {'✅' if model_status == 'loaded' else '❌'}")
-
+# app.py - Main application file
+
+import os
+import sys
+import logging
+import traceback
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S'
+)
+logger = logging.getLogger("speech_api")
+
+# Set all cache directories to locations within /tmp
+cache_dirs = {
+    "HF_HOME": "/tmp/hf_home",
+    "TRANSFORMERS_CACHE": "/tmp/transformers_cache",
+    "HUGGINGFACE_HUB_CACHE": "/tmp/huggingface_hub_cache",
+    "TORCH_HOME": "/tmp/torch_home",
+    "XDG_CACHE_HOME": "/tmp/xdg_cache"
+}
+
+# Set environment variables and create directories
+for env_var, path in cache_dirs.items():
+    os.environ[env_var] = path
+    try:
+        os.makedirs(path, exist_ok=True)
+        logger.info(f"📁 Created cache directory: {path}")
+    except Exception as e:
+        logger.error(f"❌ Failed to create directory {path}: {str(e)}")
+
+# Now import the rest of the libraries
+try:
+    import librosa
+    import glob
+    import numpy as np
+    import torch
+    from pydub import AudioSegment
+    import tempfile
+    import soundfile as sf
+    from flask import Flask, request, jsonify, send_file, g
+    from flask_cors import CORS
+    from werkzeug.utils import secure_filename
+
+    # Import functionality from other modules
+    from translator import (
+        init_models, check_model_status, handle_asr_request,
+        handle_tts_request, handle_translation_request
+    )
+    from evaluate import (
+        handle_evaluation_request, handle_upload_reference,
+        init_reference_audio, calculate_similarity
+    )
+
+    logger.info("✅ All required libraries imported successfully")
+except ImportError as e:
+    logger.critical(f"❌ Failed to import necessary libraries: {str(e)}")
+    sys.exit(1)
+
+# Check CUDA availability
+if torch.cuda.is_available():
+    logger.info(f"🚀 CUDA available: {torch.cuda.get_device_name(0)}")
+    device = "cuda"
+else:
+    logger.info("⚠️ CUDA not available, using CPU")
+    device = "cpu"
+
+# Constants
+SAMPLE_RATE = 16000
+OUTPUT_DIR = "/tmp/audio_outputs"
+REFERENCE_AUDIO_DIR = "./reference_audio"
+
+try:
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+    logger.info(f"📁 Created output directory: {OUTPUT_DIR}")
+except Exception as e:
+    logger.error(f"❌ Failed to create output directory: {str(e)}")
+
+# Initialize Flask app
+app = Flask(__name__)
+CORS(app)
+
+# Load models
+init_models(device)
+
+
+# Define routes
+@app.route("/", methods=["GET"])
+def home():
+    return jsonify({"message": "Speech API is running", "status": "active"})
+
+
+@app.route("/health", methods=["GET"])
+def health_check():
+    health_status = check_model_status()
+    health_status["api_status"] = "online"
+    health_status["device"] = device
+    return jsonify(health_status)
+
+
+@app.route("/asr", methods=["POST"])
+def transcribe_audio():
+    return handle_asr_request(request, OUTPUT_DIR, SAMPLE_RATE)
+
+
+@app.route("/tts", methods=["POST"])
+def generate_tts():
+    return handle_tts_request(request, OUTPUT_DIR)
+
+
+@app.route("/translate", methods=["POST"])
+def translate_text():
+    return handle_translation_request(request)
+
+
+@app.route("/download/<filename>", methods=["GET"])
+def download_audio(filename):
+    file_path = os.path.join(OUTPUT_DIR, filename)
+    if os.path.exists(file_path):
+        logger.info(f"📤 Serving audio file: {file_path}")
+        return send_file(file_path, mimetype="audio/wav", as_attachment=True)
+
+    logger.warning(f"⚠️ Requested file not found: {file_path}")
+    return jsonify({"error": "File not found"}), 404
+
+
+@app.route("/evaluate", methods=["POST"])
+def evaluate_pronunciation():
+    return handle_evaluation_request(request, REFERENCE_AUDIO_DIR, OUTPUT_DIR, SAMPLE_RATE)
+
+
+@app.route("/check_references", methods=["GET"])
+def check_references():
+    """Endpoint to check if reference files exist and are accessible"""
+    ref_patterns = ["mayap_a_abak", "mayap_a_ugtu", "mayap_a_gatpanapun",
+                    "mayap_a_bengi", "komusta_ka"]
+    results = {}
+
+    for pattern in ref_patterns:
+        pattern_dir = os.path.join(REFERENCE_AUDIO_DIR, pattern)
+        if os.path.exists(pattern_dir):
+            wav_files = glob.glob(os.path.join(pattern_dir, "*.wav"))
+            results[pattern] = {
+                "exists": True,
+                "path": pattern_dir,
+                "file_count": len(wav_files),
+                "files": [os.path.basename(f) for f in wav_files]
+            }
+        else:
+            results[pattern] = {
+                "exists": False,
+                "path": pattern_dir
+            }
+
+    return jsonify({
+        "reference_audio_dir": REFERENCE_AUDIO_DIR,
+        "directory_exists": os.path.exists(REFERENCE_AUDIO_DIR),
+        "patterns": results
+    })
+
+
+@app.route("/upload_reference", methods=["POST"])
+def upload_reference_audio():
+    return handle_upload_reference(request, REFERENCE_AUDIO_DIR, SAMPLE_RATE)
+
+
+@app.before_request
+def before_request():
+    global REFERENCE_AUDIO_DIR  # Add this line
+    if not hasattr(g, 'initialized'):
+        # This might return an updated path if the original fails
+        updated_ref_dir = init_reference_audio(REFERENCE_AUDIO_DIR, OUTPUT_DIR)
+        if updated_ref_dir and updated_ref_dir != REFERENCE_AUDIO_DIR:
+            REFERENCE_AUDIO_DIR = updated_ref_dir
+            logger.info(f"📁 Updated reference audio directory to: {REFERENCE_AUDIO_DIR}")
+        g.initialized = True
+
+if __name__ == "__main__":
+    global REFERENCE_AUDIO_DIR  # Add this line
+    # This might return an updated path if the original fails
+    updated_ref_dir = init_reference_audio(REFERENCE_AUDIO_DIR, OUTPUT_DIR)
+    if updated_ref_dir and updated_ref_dir != REFERENCE_AUDIO_DIR:
+        REFERENCE_AUDIO_DIR = updated_ref_dir
+        logger.info(f"📁 Updated reference audio directory to: {REFERENCE_AUDIO_DIR}")
+    
+    logger.info("🚀 Starting Speech API server")
+    
+    # Get the status for logging
+    status = check_model_status()
+    logger.info(f"📊 System status: ASR model: {'✅' if status['asr_model'] == 'loaded' else '❌'}")
+    for lang, model_status in status['tts_models'].items():
+        logger.info(f"📊 TTS model {lang}: {'✅' if model_status == 'loaded' else '❌'}")
+    
     app.run(host="0.0.0.0", port=7860, debug=True)