Spaces:

martinoyovo
/

voice-sentiment-analysis

Running

App Files Files Community

martinoyovo commited on Jul 14

Commit

626e802

1 Parent(s): 33715d3

Removed unused files

Browse files

Files changed (4) hide show

api.py +0 -563
main.py +0 -60
render.yaml +0 -9
requirements.txt +1 -4

api.py DELETED Viewed

@@ -1,563 +0,0 @@
-#!/usr/bin/env python3
-"""
-REST API for Voice Sentiment Analysis System
-Provides endpoints for integrating the pipeline into other applications
-"""
-from flask import Flask, request, jsonify, render_template_string
-from flask_cors import CORS
-import os
-import tempfile
-import uuid
-from voice_sentiment import VoiceSentimentAnalyzer
-import logging
-# Initialize Flask app
-app = Flask(__name__)
-CORS(app)  # Enable CORS for cross-origin requests
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-# Initialize the analyzer (singleton)
-analyzer = None
-def get_analyzer():
-    """Get or create analyzer instance"""
-    global analyzer
-    if analyzer is None:
-        logger.info("Initializing Voice Sentiment Analyzer...")
-        analyzer = VoiceSentimentAnalyzer()
-        logger.info("Analyzer ready!")
-    return analyzer
-# API Documentation HTML Template
-API_DOCS_HTML = """
-<!DOCTYPE html>
-<html>
-<head>
-    <title>Voice Sentiment Analysis API Documentation</title>
-    <style>
-        body { font-family: Arial, sans-serif; margin: 40px; line-height: 1.6; }
-        .header { background: #f4f4f4; padding: 20px; border-radius: 5px; margin-bottom: 30px; }
-        .endpoint { background: #f9f9f9; padding: 15px; margin: 20px 0; border-left: 4px solid #007cba; }
-        .method { background: #007cba; color: white; padding: 3px 8px; border-radius: 3px; font-size: 12px; }
-        .method.get { background: #28a745; }
-        .method.post { background: #007cba; }
-        pre { background: #f4f4f4; padding: 15px; border-radius: 5px; overflow-x: auto; }
-        code { background: #f4f4f4; padding: 2px 4px; border-radius: 3px; }
-        .example { margin: 10px 0; }
-        h1 { color: #333; }
-        h2 { color: #007cba; border-bottom: 2px solid #007cba; padding-bottom: 5px; }
-        h3 { color: #555; }
-    </style>
-</head>
-<body>
-    <div class="header">
-        <h1>Voice Sentiment Analysis API</h1>
-        <p><strong>Version:</strong> 1.0.0</p>
-        <p><strong>Base URL:</strong> <code>{{ base_url }}</code></p>
-        <p>Analyze customer call sentiment using Wav2Vec 2.0 + BERT pipeline</p>
-    </div>
-    <h2>Authentication</h2>
-    <p>No authentication required for this API.</p>
-    <h2>Supported Audio Formats</h2>
-    <ul>
-        <li><strong>WAV</strong> (.wav) - Recommended</li>
-        <li><strong>MP3</strong> (.mp3)</li>
-        <li><strong>M4A</strong> (.m4a)</li>
-    </ul>
-    <h2>API Endpoints</h2>
-    <div class="endpoint">
-        <h3><span class="method get">GET</span> /docs</h3>
-        <p><strong>Description:</strong> This documentation page</p>
-        <p><strong>Response:</strong> HTML documentation</p>
-    </div>
-    <div class="endpoint">
-        <h3><span class="method get">GET</span> /health</h3>
-        <p><strong>Description:</strong> Health check endpoint</p>
-        <p><strong>Response:</strong></p>
-        <pre><code>{
-  "status": "healthy",
-  "service": "Voice Sentiment Analysis API",
-  "version": "1.0.0"
-}</code></pre>
-    </div>
-    <div class="endpoint">
-        <h3><span class="method post">POST</span> /analyze</h3>
-        <p><strong>Description:</strong> Analyze a single audio file for sentiment</p>
-        <p><strong>Content-Type:</strong> multipart/form-data</p>
-        <p><strong>Parameters:</strong></p>
-        <ul>
-            <li><code>audio</code> (file, required): Audio file to analyze</li>
-        </ul>
-        <div class="example">
-            <p><strong>Example Request (cURL):</strong></p>
-            <pre><code>curl -X POST \\
-  -F "audio=@call1.wav" \\
-  {{ base_url }}/analyze</code></pre>
-        </div>
-        <div class="example">
-            <p><strong>Example Response:</strong></p>
-            <pre><code>{
-  "success": true,
-  "data": {
-    "filename": "call1.wav",
-    "transcription": "Hello I am very satisfied with your service",
-    "sentiment": "POSITIVE",
-    "confidence_score": 0.89,
-    "satisfaction": "Satisfied"
-  },
-  "processing_id": "uuid-string"
-}</code></pre>
-        </div>
-        <div class="example">
-            <p><strong>Error Response:</strong></p>
-            <pre><code>{
-  "error": "Unsupported file format",
-  "message": "Supported formats: .wav, .mp3, .m4a, .flac",
-  "received": ".txt"
-}</code></pre>
-        </div>
-    </div>
-    <div class="endpoint">
-        <h3><span class="method post">POST</span> /analyze/batch</h3>
-        <p><strong>Description:</strong> Analyze multiple audio files</p>
-        <p><strong>Content-Type:</strong> multipart/form-data</p>
-        <p><strong>Parameters:</strong></p>
-        <ul>
-            <li><code>audio</code> (files, required): Multiple audio files to analyze</li>
-        </ul>
-        <div class="example">
-            <p><strong>Example Request (cURL):</strong></p>
-            <pre><code>curl -X POST \\
-  -F "audio=@call1.wav" \\
-  -F "audio=@call2.mp3" \\
-  {{ base_url }}/analyze/batch</code></pre>
-        </div>
-        <div class="example">
-            <p><strong>Example Response:</strong></p>
-            <pre><code>{
-  "success": true,
-  "batch_id": "uuid-string",
-  "statistics": {
-    "total_files": 2,
-    "sentiment_distribution": {
-      "POSITIVE": {"count": 1, "percentage": 50.0},
-      "NEGATIVE": {"count": 1, "percentage": 50.0}
-    },
-    "satisfaction_distribution": {
-      "Satisfied": {"count": 1, "percentage": 50.0},
-      "Dissatisfied": {"count": 1, "percentage": 50.0}
-    }
-  },
-  "results": [
-    {
-      "filename": "call1.wav",
-      "transcription": "Hello I am satisfied",
-      "sentiment": "POSITIVE",
-      "confidence_score": 0.89,
-      "satisfaction": "Satisfied",
-      "success": true
-    },
-    {
-      "filename": "call2.mp3",
-      "transcription": "This is terrible service",
-      "sentiment": "NEGATIVE",
-      "confidence_score": 0.92,
-      "satisfaction": "Dissatisfied",
-      "success": true
-    }
-  ],
-  "processed_files": 2,
-  "total_uploaded": 2
-}</code></pre>
-        </div>
-    </div>
-    <div class="endpoint">
-        <h3><span class="method get">GET</span> /models/info</h3>
-        <p><strong>Description:</strong> Get information about loaded models</p>
-        <p><strong>Response:</strong></p>
-        <pre><code>{
-  "speech_recognition": {
-    "model": "facebook/wav2vec2-large-960h-lv60-self",
-    "type": "Wav2Vec 2.0",
-    "language": "English",
-    "description": "Large Wav2Vec 2.0 model for English speech recognition"
-  },
-  "sentiment_analysis": {
-    "model": "nlptown/bert-base-multilingual-uncased-sentiment",
-    "type": "BERT",
-    "language": "Multilingual",
-    "description": "Multilingual BERT for sentiment analysis"
-  },
-  "supported_formats": [".wav", ".mp3", ".m4a", ".flac"],
-  "classifications": {
-    "sentiments": ["POSITIVE", "NEGATIVE", "NEUTRAL"],
-    "satisfaction": ["Satisfied", "Dissatisfied", "Neutral"]
-  }
-}</code></pre>
-    </div>
-    <h2>Response Codes</h2>
-    <ul>
-        <li><strong>200</strong> - Success</li>
-        <li><strong>400</strong> - Bad Request (invalid file, missing parameters)</li>
-        <li><strong>404</strong> - Endpoint Not Found</li>
-        <li><strong>413</strong> - File Too Large (>16MB)</li>
-        <li><strong>500</strong> - Internal Server Error</li>
-    </ul>
-    <h2>Integration Examples</h2>
-    <h3>Python</h3>
-    <pre><code>import requests
-# Single file analysis
-with open('audio.wav', 'rb') as f:
-    response = requests.post(
-        '{{ base_url }}/analyze',
-        files={'audio': f}
-    )
-    result = response.json()
-    print(f"Sentiment: {result['data']['sentiment']}")
-# Batch analysis
-files = [
-    ('audio', open('call1.wav', 'rb')),
-    ('audio', open('call2.mp3', 'rb'))
-]
-response = requests.post('{{ base_url }}/analyze/batch', files=files)
-result = response.json()
-print(f"Processed {result['processed_files']} files")</code></pre>
-    <h3>JavaScript</h3>
-    <pre><code>// Single file upload
-const formData = new FormData();
-formData.append('audio', fileInput.files[0]);
-fetch('{{ base_url }}/analyze', {
-    method: 'POST',
-    body: formData
-})
-.then(response => response.json())
-.then(data => {
-    console.log('Sentiment:', data.data.sentiment);
-});</code></pre>
-    <h3>Node.js</h3>
-    <pre><code>const fs = require('fs');
-const FormData = require('form-data');
-const form = new FormData();
-form.append('audio', fs.createReadStream('call.wav'));
-fetch('{{ base_url }}/analyze', {
-    method: 'POST',
-    body: form
-})
-.then(response => response.json())
-.then(data => console.log(data));</code></pre>
-    <h2>Rate Limits</h2>
-    <p>Currently no rate limits are enforced. For production use, consider implementing rate limiting.</p>
-    <h2>File Size Limits</h2>
-    <ul>
-        <li><strong>Maximum file size:</strong> 16MB per file</li>
-        <li><strong>Recommended:</strong> Keep files under 5MB for faster processing</li>
-        <li><strong>Optimal duration:</strong> 30 seconds to 2 minutes</li>
-    </ul>
-    <footer style="margin-top: 50px; padding-top: 20px; border-top: 1px solid #eee; color: #666;">
-        <p>Voice Sentiment Analysis API - Powered by Wav2Vec 2.0 + BERT</p>
-    </footer>
-</body>
-</html>
-"""
-@app.route('/docs', methods=['GET'])
-@app.route('/documentation', methods=['GET'])
-@app.route('/', methods=['GET'])
-def api_documentation():
-    """API Documentation page"""
-    base_url = request.url_root.rstrip('/')
-    return render_template_string(API_DOCS_HTML, base_url=base_url)
-@app.route('/health', methods=['GET'])
-def health_check():
-    """Health check endpoint"""
-    return jsonify({
-        "status": "healthy",
-        "service": "Voice Sentiment Analysis API",
-        "version": "1.0.0"
-    })
-@app.route('/analyze', methods=['POST'])
-def analyze_audio():
-    """
-    Analyze a single audio file
-    Expected: multipart/form-data with 'audio' file
-    Returns: JSON with analysis results
-    """
-    try:
-        # Check if file is present
-        if 'audio' not in request.files:
-            return jsonify({
-                "error": "No audio file provided",
-                "message": "Please upload an audio file using the 'audio' field"
-            }), 400
-        audio_file = request.files['audio']
-        # Check if file is selected
-        if audio_file.filename == '':
-            return jsonify({
-                "error": "No file selected",
-                "message": "Please select an audio file"
-            }), 400
-        # Validate file extension
-        allowed_extensions = ['.wav', '.mp3', '.m4a', '.flac']
-        file_ext = os.path.splitext(audio_file.filename)[1].lower()
-        if file_ext not in allowed_extensions:
-            return jsonify({
-                "error": "Unsupported file format",
-                "message": f"Supported formats: {', '.join(allowed_extensions)}",
-                "received": file_ext
-            }), 400
-        # Save file temporarily
-        temp_id = str(uuid.uuid4())
-        temp_filename = f"temp_audio_{temp_id}{file_ext}"
-        temp_path = os.path.join(tempfile.gettempdir(), temp_filename)
-        audio_file.save(temp_path)
-        try:
-            # Analyze the audio
-            analyzer = get_analyzer()
-            result = analyzer.analyze_call(temp_path)
-            # Clean up temporary file
-            os.remove(temp_path)
-            # Return results
-            return jsonify({
-                "success": True,
-                "data": {
-                    "filename": audio_file.filename,
-                    "transcription": result['transcription'],
-                    "sentiment": result['sentiment'],
-                    "confidence_score": round(result['score'], 3),
-                    "satisfaction": result['satisfaction']
-                },
-                "processing_id": temp_id
-            })
-        except Exception as e:
-            # Clean up on error
-            if os.path.exists(temp_path):
-                os.remove(temp_path)
-            raise e
-    except Exception as e:
-        logger.error(f"Error processing audio: {str(e)}")
-        return jsonify({
-            "error": "Processing failed",
-            "message": str(e)
-        }), 500
-@app.route('/analyze/batch', methods=['POST'])
-def analyze_batch():
-    """
-    Analyze multiple audio files
-    Expected: multipart/form-data with multiple 'audio' files
-    Returns: JSON with batch analysis results
-    """
-    try:
-        # Check if files are present
-        if 'audio' not in request.files:
-            return jsonify({
-                "error": "No audio files provided",
-                "message": "Please upload audio files using the 'audio' field"
-            }), 400
-        audio_files = request.files.getlist('audio')
-        if not audio_files or all(f.filename == '' for f in audio_files):
-            return jsonify({
-                "error": "No files selected",
-                "message": "Please select audio files"
-            }), 400
-        results = []
-        temp_files = []
-        batch_id = str(uuid.uuid4())
-        try:
-            # Process each file
-            for i, audio_file in enumerate(audio_files):
-                if audio_file.filename == '':
-                    continue
-                # Validate file extension
-                allowed_extensions = ['.wav', '.mp3', '.m4a', '.flac']
-                file_ext = os.path.splitext(audio_file.filename)[1].lower()
-                if file_ext not in allowed_extensions:
-                    results.append({
-                        "filename": audio_file.filename,
-                        "error": f"Unsupported format: {file_ext}",
-                        "success": False
-                    })
-                    continue
-                # Save file temporarily
-                temp_filename = f"batch_{batch_id}_{i}{file_ext}"
-                temp_path = os.path.join(tempfile.gettempdir(), temp_filename)
-                temp_files.append(temp_path)
-                audio_file.save(temp_path)
-                # Analyze the audio
-                analyzer = get_analyzer()
-                result = analyzer.analyze_call(temp_path)
-                results.append({
-                    "filename": audio_file.filename,
-                    "transcription": result['transcription'],
-                    "sentiment": result['sentiment'],
-                    "confidence_score": round(result['score'], 3),
-                    "satisfaction": result['satisfaction'],
-                    "success": True
-                })
-            # Calculate statistics
-            successful_results = [r for r in results if r.get('success', False)]
-            total_files = len(successful_results)
-            if total_files > 0:
-                sentiment_counts = {}
-                satisfaction_counts = {}
-                for result in successful_results:
-                    sentiment = result['sentiment']
-                    satisfaction = result['satisfaction']
-                    sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1
-                    satisfaction_counts[satisfaction] = satisfaction_counts.get(satisfaction, 0) + 1
-                statistics = {
-                    "total_files": total_files,
-                    "sentiment_distribution": {
-                        k: {"count": v, "percentage": round(v/total_files*100, 1)}
-                        for k, v in sentiment_counts.items()
-                    },
-                    "satisfaction_distribution": {
-                        k: {"count": v, "percentage": round(v/total_files*100, 1)}
-                        for k, v in satisfaction_counts.items()
-                    }
-                }
-            else:
-                statistics = {"total_files": 0, "message": "No files processed successfully"}
-            return jsonify({
-                "success": True,
-                "batch_id": batch_id,
-                "statistics": statistics,
-                "results": results,
-                "processed_files": len(successful_results),
-                "total_uploaded": len([f for f in audio_files if f.filename != ''])
-            })
-        finally:
-            # Clean up temporary files
-            for temp_path in temp_files:
-                if os.path.exists(temp_path):
-                    os.remove(temp_path)
-    except Exception as e:
-        logger.error(f"Error processing batch: {str(e)}")
-        return jsonify({
-            "error": "Batch processing failed",
-            "message": str(e)
-        }), 500
-@app.route('/models/info', methods=['GET'])
-def model_info():
-    """Get information about loaded models"""
-    return jsonify({
-        "speech_recognition": {
-            "model": "facebook/wav2vec2-large-960h-lv60-self",
-            "type": "Wav2Vec 2.0",
-            "language": "English",
-            "description": "Large Wav2Vec 2.0 model for English speech recognition"
-        },
-        "sentiment_analysis": {
-            "model": "nlptown/bert-base-multilingual-uncased-sentiment",
-            "type": "BERT",
-            "language": "Multilingual",
-            "description": "Multilingual BERT for sentiment analysis (1-5 stars)"
-        },
-        "supported_formats": [".wav", ".mp3", ".m4a", ".flac"],
-        "classifications": {
-            "sentiments": ["POSITIVE", "NEGATIVE", "NEUTRAL"],
-            "satisfaction": ["Satisfied", "Dissatisfied", "Neutral"]
-        }
-    })
-@app.errorhandler(413)
-def file_too_large(error):
-    """Handle file too large error"""
-    return jsonify({
-        "error": "File too large",
-        "message": "Audio file exceeds maximum size limit"
-    }), 413
-@app.errorhandler(404)
-def not_found(error):
-    """Handle 404 errors"""
-    return jsonify({
-        "error": "Endpoint not found",
-        "message": "The requested endpoint does not exist",
-        "available_endpoints": [
-            "GET /health - Health check",
-            "POST /analyze - Analyze single audio file",
-            "POST /analyze/batch - Analyze multiple audio files",
-            "GET /models/info - Get model information"
-        ]
-    }), 404
-if __name__ == '__main__':
-    # Configuration
-    HOST = os.getenv('API_HOST', '0.0.0.0')
-    PORT = int(os.getenv('API_PORT', 8000))
-    DEBUG = os.getenv('API_DEBUG', 'False').lower() == 'true'
-    # Set maximum file size (16MB)
-    app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024
-    print(f"Starting Voice Sentiment Analysis API...")
-    print(f"Server: http://{HOST}:{PORT}")
-    print(f"Health check: http://{HOST}:{PORT}/health")
-    print(f"Documentation: See README for API usage examples")
-    app.run(host=HOST, port=PORT, debug=DEBUG)

main.py DELETED Viewed

@@ -1,60 +0,0 @@
-#!/usr/bin/env python3
-"""
-Main script for voice sentiment analysis
-"""
-from voice_sentiment import VoiceSentimentAnalyzer
-import os
-def main():
-    """Main function"""
-    print("VOICE SENTIMENT ANALYSIS SYSTEM")
-    print("="*50)
-    # Initialize the system
-    analyzer = VoiceSentimentAnalyzer()
-    # Simple menu
-    while True:
-        print("\nOptions:")
-        print("1. Analyze an audio file")
-        print("2. Analyze a folder of calls")
-        print("3. Exit")
-        choice = input("\nYour choice (1-3): ").strip()
-        if choice == "1":
-            # Single file analysis
-            file_path = input("Audio file path: ").strip()
-            if os.path.exists(file_path):
-                try:
-                    result = analyzer.analyze_call(file_path)
-                    print("\nAnalysis completed!")
-                except Exception as e:
-                    print(f"Error: {e}")
-            else:
-                print("File not found!")
-        elif choice == "2":
-            # Folder analysis
-            folder_path = input("Folder path: ").strip()
-            if os.path.exists(folder_path):
-                try:
-                    results = analyzer.analyze_batch(folder_path)
-                    print(f"\n{len(results)} files analyzed!")
-                except Exception as e:
-                    print(f"Error: {e}")
-            else:
-                print("Folder not found!")
-        elif choice == "3":
-            print("Goodbye!")
-            break
-        else:
-            print("Invalid choice!")
-if __name__ == "__main__":
-    main()

render.yaml DELETED Viewed

@@ -1,9 +0,0 @@
-services:
-  - type: web
-    name: voice-sentiment-api
-    env: python
-    buildCommand: pip install -r requirements.txt
-    startCommand: gunicorn --bind 0.0.0.0:$PORT --timeout 300 api:app
-    envVars:
-      - key: PYTHON_VERSION
-        value: 3.9.18

requirements.txt CHANGED Viewed

@@ -6,7 +6,4 @@ numpy>=1.21.0
 scipy>=1.7.0
 torchaudio>=0.9.0
 soundfile>=0.10.0
-gradio>=4.0.0
-flask>=2.0.0
-flask-cors>=3.0.0
-gunicorn>=20.0.0

 scipy>=1.7.0
 torchaudio>=0.9.0
 soundfile>=0.10.0
+gradio>=4.0.0