| |
| """ |
| Lightweight Aphasia Classification App |
| Optimized for Hugging Face Spaces with lazy loading and fallbacks |
| """ |
|
|
| import os |
|
|
| |
| os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:512' |
| os.environ['OMP_NUM_THREADS'] = '2' |
| os.environ['MKL_NUM_THREADS'] = '2' |
| os.environ['NUMEXPR_NUM_THREADS'] = '2' |
| os.environ['TOKENIZERS_PARALLELISM'] = 'false' |
| |
| os.environ['BATCHALIGN_CACHE'] = '/tmp/batchalign_cache' |
| os.environ['HF_HUB_CACHE'] = '/tmp/hf_cache' |
| os.environ['TRANSFORMERS_CACHE'] = '/tmp/transformers_cache' |
|
|
| |
| os.environ['WHISPER_CACHE'] = '/tmp/whisper_cache' |
|
|
| print("🔧 Environment configured for CPU-only processing") |
| print("💾 Model caches set to /tmp/ to save space") |
|
|
|
|
| from flask import Flask, request, render_template_string, jsonify |
| import os |
| import tempfile |
| import logging |
| import json |
| import threading |
| import time |
| from pathlib import Path |
|
|
| |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') |
| logger = logging.getLogger(__name__) |
|
|
| app = Flask(__name__) |
| app.config['MAX_CONTENT_LENGTH'] = 50 * 1024 * 1024 |
|
|
| print("🚀 Starting Lightweight Aphasia Classification System") |
|
|
| |
| MODULES = {} |
| MODELS_LOADED = False |
| LOADING_STATUS = "Starting up..." |
|
|
| def lazy_import_modules(): |
| """Import modules only when needed""" |
| global MODULES, MODELS_LOADED, LOADING_STATUS |
| |
| if MODELS_LOADED: |
| return True |
| |
| try: |
| LOADING_STATUS = "Loading audio processing..." |
| logger.info("Importing utils_audio...") |
| from utils_audio import convert_to_wav |
| MODULES['convert_to_wav'] = convert_to_wav |
| logger.info("✓ Audio processing loaded") |
| |
| LOADING_STATUS = "Loading speech analysis..." |
| logger.info("Importing to_cha...") |
| from to_cha import to_cha_from_wav |
| MODULES['to_cha_from_wav'] = to_cha_from_wav |
| logger.info("✓ Speech analysis loaded") |
| |
| LOADING_STATUS = "Loading data conversion..." |
| logger.info("Importing cha_json...") |
| from cha_json import cha_to_json_file |
| MODULES['cha_to_json_file'] = cha_to_json_file |
| logger.info("✓ Data conversion loaded") |
| |
| LOADING_STATUS = "Loading AI model..." |
| logger.info("Importing output...") |
| from output import predict_from_chajson |
| MODULES['predict_from_chajson'] = predict_from_chajson |
| logger.info("✓ AI model loaded") |
| |
| MODELS_LOADED = True |
| LOADING_STATUS = "Ready!" |
| logger.info("🎉 All modules loaded successfully!") |
| return True |
| |
| except Exception as e: |
| logger.error(f"Failed to load modules: {e}") |
| LOADING_STATUS = f"Error: {str(e)}" |
| return False |
|
|
| def background_loader(): |
| """Load modules in background thread""" |
| logger.info("Starting background module loading...") |
| lazy_import_modules() |
|
|
| |
| loading_thread = threading.Thread(target=background_loader, daemon=True) |
| loading_thread.start() |
|
|
| |
| HTML_TEMPLATE = """ |
| <!DOCTYPE html> |
| <html lang="en"> |
| <head> |
| <meta charset="UTF-8"> |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> |
| <title>🧠 Aphasia Classification</title> |
| <style> |
| body { |
| font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); |
| min-height: 100vh; |
| padding: 20px; |
| margin: 0; |
| } |
| |
| .container { |
| max-width: 800px; |
| margin: 0 auto; |
| background: white; |
| border-radius: 20px; |
| box-shadow: 0 20px 60px rgba(0,0,0,0.1); |
| overflow: hidden; |
| } |
| |
| .header { |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); |
| color: white; |
| padding: 40px 30px; |
| text-align: center; |
| } |
| |
| .content { |
| padding: 40px 30px; |
| } |
| |
| .status { |
| background: #f8f9fa; |
| border-radius: 10px; |
| padding: 20px; |
| margin-bottom: 30px; |
| border-left: 4px solid #28a745; |
| } |
| |
| .status.loading { |
| border-left-color: #ffc107; |
| } |
| |
| .status.error { |
| border-left-color: #dc3545; |
| } |
| |
| .upload-section { |
| background: #f8f9fa; |
| border-radius: 15px; |
| padding: 30px; |
| text-align: center; |
| margin-bottom: 30px; |
| } |
| |
| .file-input { |
| display: none; |
| } |
| |
| .file-label { |
| display: inline-block; |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); |
| color: white; |
| padding: 15px 30px; |
| border-radius: 50px; |
| cursor: pointer; |
| font-weight: 600; |
| transition: transform 0.2s ease; |
| } |
| |
| .file-label:hover { |
| transform: translateY(-2px); |
| } |
| |
| .analyze-btn { |
| background: #28a745; |
| color: white; |
| border: none; |
| padding: 15px 40px; |
| border-radius: 50px; |
| font-weight: 600; |
| cursor: pointer; |
| margin-top: 20px; |
| transition: all 0.2s ease; |
| } |
| |
| .analyze-btn:disabled { |
| background: #6c757d; |
| cursor: not-allowed; |
| } |
| |
| .results { |
| background: #f8f9fa; |
| border-radius: 15px; |
| padding: 30px; |
| margin-top: 30px; |
| display: none; |
| white-space: pre-wrap; |
| font-family: monospace; |
| } |
| |
| .loading { |
| text-align: center; |
| padding: 40px; |
| display: none; |
| } |
| |
| .spinner { |
| border: 4px solid #f3f3f3; |
| border-top: 4px solid #667eea; |
| border-radius: 50%; |
| width: 50px; |
| height: 50px; |
| animation: spin 1s linear infinite; |
| margin: 0 auto 20px; |
| } |
| |
| @keyframes spin { |
| 0% { transform: rotate(0deg); } |
| 100% { transform: rotate(360deg); } |
| } |
| |
| .refresh-btn { |
| background: #17a2b8; |
| color: white; |
| border: none; |
| padding: 10px 20px; |
| border-radius: 25px; |
| cursor: pointer; |
| margin-left: 10px; |
| } |
| </style> |
| </head> |
| <body> |
| <div class="container"> |
| <div class="header"> |
| <h1>🧠 Aphasia Classification</h1> |
| <p>AI-powered speech analysis for aphasia identification</p> |
| </div> |
| |
| <div class="content"> |
| <div class="status" id="statusBox"> |
| <h3 id="statusTitle">🔄 System Status</h3> |
| <div id="statusText">{{ status_message }}</div> |
| <button class="refresh-btn" onclick="checkStatus()">Refresh Status</button> |
| </div> |
| |
| <div class="upload-section"> |
| <h3>📁 Upload Audio File</h3> |
| <p>Upload speech audio for aphasia classification</p> |
| |
| <form id="uploadForm" enctype="multipart/form-data"> |
| <input type="file" id="audioFile" name="audio" class="file-input" accept="audio/*" required> |
| <label for="audioFile" class="file-label"> |
| 🎵 Choose Audio File |
| </label> |
| <br> |
| <button type="submit" class="analyze-btn" id="analyzeBtn"> |
| 🔍 Analyze Speech |
| </button> |
| </form> |
| |
| <p style="color: #666; margin-top: 15px; font-size: 0.9em;"> |
| Supported: MP3, WAV, M4A (max 50MB) |
| </p> |
| </div> |
| |
| <div class="loading" id="loading"> |
| <div class="spinner"></div> |
| <h3>🔄 Processing Audio...</h3> |
| <p>This may take 2-5 minutes. Please be patient.</p> |
| </div> |
| |
| <div class="results" id="results"></div> |
| </div> |
| </div> |
| |
| <script> |
| // Check status periodically |
| function checkStatus() { |
| fetch('/status') |
| .then(response => response.json()) |
| .then(data => { |
| const statusBox = document.getElementById('statusBox'); |
| const statusTitle = document.getElementById('statusTitle'); |
| const statusText = document.getElementById('statusText'); |
| |
| if (data.ready) { |
| statusBox.className = 'status'; |
| statusTitle.textContent = '🟢 System Ready'; |
| statusText.textContent = 'All components loaded. Ready to process audio files.'; |
| } else { |
| statusBox.className = 'status loading'; |
| statusTitle.textContent = '🟡 Loading...'; |
| statusText.textContent = data.status; |
| } |
| }) |
| .catch(error => { |
| const statusBox = document.getElementById('statusBox'); |
| statusBox.className = 'status error'; |
| document.getElementById('statusTitle').textContent = '🔴 Error'; |
| document.getElementById('statusText').textContent = 'Failed to check status'; |
| }); |
| } |
| |
| // Check status every 5 seconds |
| setInterval(checkStatus, 5000); |
| |
| // Form submission |
| document.getElementById('uploadForm').addEventListener('submit', async function(e) { |
| e.preventDefault(); |
| |
| const fileInput = document.getElementById('audioFile'); |
| const loading = document.getElementById('loading'); |
| const results = document.getElementById('results'); |
| const analyzeBtn = document.getElementById('analyzeBtn'); |
| |
| if (!fileInput.files[0]) { |
| alert('Please select an audio file'); |
| return; |
| } |
| |
| // Check if system is ready |
| const statusCheck = await fetch('/status'); |
| const status = await statusCheck.json(); |
| |
| if (!status.ready) { |
| alert('System is still loading. Please wait and try again.'); |
| return; |
| } |
| |
| // Show loading |
| loading.style.display = 'block'; |
| results.style.display = 'none'; |
| analyzeBtn.disabled = true; |
| analyzeBtn.textContent = 'Processing...'; |
| |
| try { |
| const formData = new FormData(); |
| formData.append('audio', fileInput.files[0]); |
| |
| const response = await fetch('/analyze', { |
| method: 'POST', |
| body: formData |
| }); |
| |
| const data = await response.json(); |
| |
| loading.style.display = 'none'; |
| |
| if (data.success) { |
| results.textContent = data.result; |
| results.style.borderLeft = '4px solid #28a745'; |
| } else { |
| results.textContent = 'Error: ' + data.error; |
| results.style.borderLeft = '4px solid #dc3545'; |
| } |
| |
| results.style.display = 'block'; |
| |
| } catch (error) { |
| loading.style.display = 'none'; |
| results.textContent = 'Network error: ' + error.message; |
| results.style.borderLeft = '4px solid #dc3545'; |
| results.style.display = 'block'; |
| } |
| |
| analyzeBtn.disabled = false; |
| analyzeBtn.textContent = '🔍 Analyze Speech'; |
| }); |
| |
| // File selection feedback |
| document.getElementById('audioFile').addEventListener('change', function(e) { |
| const label = document.querySelector('.file-label'); |
| if (e.target.files[0]) { |
| label.textContent = '✓ ' + e.target.files[0].name; |
| } else { |
| label.textContent = '🎵 Choose Audio File'; |
| } |
| }); |
| </script> |
| </body> |
| </html> |
| """ |
|
|
| @app.route('/') |
| def index(): |
| """Main page""" |
| return render_template_string(HTML_TEMPLATE, status_message=LOADING_STATUS) |
|
|
| @app.route('/status') |
| def status(): |
| """Status check endpoint""" |
| return jsonify({ |
| 'ready': MODELS_LOADED, |
| 'status': LOADING_STATUS, |
| 'modules_loaded': len(MODULES) |
| }) |
|
|
| @app.route('/analyze', methods=['POST']) |
| def analyze_audio(): |
| """Process uploaded audio - only if models are loaded""" |
| try: |
| |
| if not MODELS_LOADED: |
| return jsonify({ |
| 'success': False, |
| 'error': f'System still loading: {LOADING_STATUS}' |
| }) |
| |
| |
| if 'audio' not in request.files: |
| return jsonify({'success': False, 'error': 'No audio file uploaded'}) |
| |
| audio_file = request.files['audio'] |
| if audio_file.filename == '': |
| return jsonify({'success': False, 'error': 'No file selected'}) |
| |
| |
| with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(audio_file.filename)[1]) as tmp_file: |
| audio_file.save(tmp_file.name) |
| temp_path = tmp_file.name |
| |
| try: |
| logger.info("🎵 Starting audio processing...") |
| |
| |
| logger.info("Converting to WAV...") |
| wav_path = MODULES['convert_to_wav'](temp_path, sr=16000, mono=True) |
| |
| |
| logger.info("Generating CHA file...") |
| cha_path = MODULES['to_cha_from_wav'](wav_path, lang="eng") |
| |
| |
| logger.info("Converting to JSON...") |
| json_path, _ = MODULES['cha_to_json_file'](cha_path) |
| |
| |
| logger.info("Running classification...") |
| results = MODULES['predict_from_chajson'](".", json_path, output_file=None) |
| |
| |
| for temp_file in [temp_path, wav_path, cha_path, json_path]: |
| try: |
| os.unlink(temp_file) |
| except: |
| pass |
| |
| |
| if "predictions" in results and results["predictions"]: |
| pred = results["predictions"][0] |
| |
| classification = pred["prediction"]["predicted_class"] |
| confidence = pred["prediction"]["confidence_percentage"] |
| description = pred["class_description"]["name"] |
| severity = pred["additional_predictions"]["predicted_severity_level"] |
| fluency = pred["additional_predictions"]["fluency_rating"] |
| |
| result_text = f"""🧠 APHASIA CLASSIFICATION RESULTS |
| |
| 🎯 Classification: {classification} |
| 📊 Confidence: {confidence} |
| 📋 Type: {description} |
| 📈 Severity: {severity}/3 |
| 🗣️ Fluency: {fluency} |
| |
| 📊 Top 3 Probabilities:""" |
|
|
| prob_dist = pred["probability_distribution"] |
| for i, (atype, info) in enumerate(list(prob_dist.items())[:3], 1): |
| result_text += f"\n{i}. {atype}: {info['percentage']}" |
| |
| result_text += f""" |
| |
| 📝 Description: |
| {pred["class_description"]["description"]} |
| |
| ✅ Processing completed successfully! |
| """ |
| |
| return jsonify({'success': True, 'result': result_text}) |
| else: |
| return jsonify({'success': False, 'error': 'No predictions generated'}) |
| |
| except Exception as e: |
| |
| try: |
| os.unlink(temp_path) |
| except: |
| pass |
| raise e |
| |
| except Exception as e: |
| logger.error(f"Processing error: {e}") |
| return jsonify({'success': False, 'error': str(e)}) |
|
|
| if __name__ == '__main__': |
| port = int(os.environ.get('PORT', 7860)) |
| print(f"🚀 Starting on port {port}") |
| print("🔄 Models loading in background...") |
| |
| app.run(host='0.0.0.0', port=port, debug=False, threaded=True) |
