| | |
| | """ |
| | Lightweight Aphasia Classification App |
| | Optimized for Hugging Face Spaces with lazy loading and fallbacks |
| | """ |
| |
|
| | import os |
| |
|
| | |
| | os.environ['CUDA_VISIBLE_DEVICES'] = '' |
| | os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:128' |
| | 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) |
