Update app.py

app.py

@@ -1,1294 +1,114 @@
-import gradio as gr
-
-import librosa
-
-import numpy as np
-
-import torch
-
-from transformers import WhisperProcessor, WhisperForConditionalGeneration
-
-from simple_salesforce import Salesforce
-
-import os
-
-from datetime import datetime
-
-import logging
-
-import webrtcvad
-
-import google.generativeai as genai
-
-from gtts import gTTS
-
-import tempfile
-
+from flask import Flask, request, jsonify
+import requests
 import base64
+import json
+from datetime import datetime
+import os
 
-import re
-
-from cryptography.fernet import Fernet
-
-import pytz
-
-from reportlab.lib.pagesizes import A4
-
-from reportlab.lib import colors
-
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, ListFlowable, ListItem
-
-from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
-
-from reportlab.lib.units import inch
-
-import asyncio
-
-import hashlib
-
-from functools import lru_cache
-
-
-# Set up logging with DEBUG level, adjusted for IST
-
-logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
-
-logger = logging.getLogger(__name__)
-
-usage_metrics = {"total_assessments": 0, "assessments_by_language": {}}
-
-
-# Environment variables
-
-SF_USERNAME = os.getenv("SF_USERNAME", "smartvoicebot@voice.com")
-
-SF_PASSWORD = os.getenv("SF_PASSWORD", "voicebot1")
-
-SF_SECURITY_TOKEN = os.getenv("SF_SECURITY_TOKEN", "jq4VVHUFti6TmzJDjjegv2h6b")
-
-SF_INSTANCE_URL = os.getenv("SF_INSTANCE_URL", "https://swe42.sfdc-cehfhs.salesforce.com")
-
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "AIzaSyBzr5vVpbe8CV1v70l3pGDp9vRJ76yCxdk")
-
-ENCRYPTION_KEY = os.getenv("ENCRYPTION_KEY", Fernet.generate_key().decode())
-
-DEFAULT_EMAIL = os.getenv("SALESFORCE_USER_EMAIL", "default@mindcare.com")
-
-
-# Initialize encryption
-
-cipher = Fernet(ENCRYPTION_KEY)
-
-
-# Initialize Salesforce
-
-try:
-
-    sf = Salesforce(
-
-        username=SF_USERNAME,
-
-        password=SF_PASSWORD,
-
-        security_token=SF_SECURITY_TOKEN,
-
-        instance_url=SF_INSTANCE_URL
-
-    )
-
-    logger.info(f"Connected to Salesforce at {SF_INSTANCE_URL}")
-
-except Exception as e:
-
-    logger.error(f"Salesforce connection failed: {str(e)}")
-
-    sf = None
-
-
-# Initialize Google Gemini
-
-try:
-
-    genai.configure(api_key=GEMINI_API_KEY)
-
-    gemini_model = genai.GenerativeModel('gemini-1.5-flash')
-
-    chat = gemini_model.start_chat(history=[])
-
-    logger.info("Connected to Google Gemini")
-
-except Exception as e:
-
-    logger.error(f"Google Gemini initialization failed: {str(e)}")
-
-    chat = None
-
-
-# Load Whisper model
-
-SUPPORTED_LANGUAGES = {"English": "english", "Hindi": "hindi", "Spanish": "spanish", "Mandarin": "mandarin"}
-
-SALESFORCE_LANGUAGE_MAP = {"English": "English", "Hindi": "Hindi", "Spanish": "Spanish", "Mandarin": "Mandarin"}
-
-whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-small")
-
-whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
-
-vad = webrtcvad.Vad(mode=2)
-
-
-# Context for chatbot
-
-base_info = """
-
-MindCare is an AI health assistant focused on:
-
-- **Mental health**: Emotional support, mindfulness, stress-relief, anxiety management.
-
-- **Medical guidance**: Symptom analysis, possible conditions, medicine recommendations.
-
-- **Decision-making**: Personal, professional, emotional choices.
-
-- **General health**: Lifestyle, nutrition, physical and mental wellness.
-
-- **Emergency assistance**: Suggest professional help or helplines for distress.
-
-Tone: Empathetic, supportive, informative.
-
-"""
-
-mental_health = """
-
-For stress/anxiety:
-
-- Suggest mindfulness, deep breathing, gratitude journaling.
-
-- Encourage breaks, hobbies, nature.
-
-- Provide affirmations, self-care routines.
-
-For distress:
-
-- Offer emotional support, assure they’re not alone.
-
-- Suggest trusted contacts or professionals.
-
-- Provide crisis helplines.
-
-"""
-
-medical_assistance = """
-
-For symptoms:
-
-- Analyze and suggest possible conditions.
-
-- Offer general advice, not replacing doctor consultation.
-
-- Suggest lifestyle changes, home remedies.
-
-- Advise medical attention for severe symptoms.
-
-"""
-
-medicine_recommendation = """
-
-For medicine queries:
-
-- Suggest common antibiotics (e.g., Amoxicillin), painkillers (e.g., Paracetamol, Ibuprofen).
-
-- Note precautions, side effects.
-
-- Stress doctor consultation before use.
-
-"""
-
-decision_guidance = """
-
-For decisions:
-
-- Weigh pros/cons logically.
-
-- Consider values, goals, emotions.
-
-- Suggest decision matrices or intuitive checks.
-
-- Encourage trusted advice if needed.
-
-"""
-
-emergency_help = """
-
-For severe distress:
-
-- Provide immediate emotional support.
-
-- Offer crisis helplines (region-specific).
-
-- Encourage talking to trusted contacts or professionals.
-
-- Assure help is available.
-
-"""
-
-context = [base_info, mental_health, medical_assistance, medicine_recommendation, decision_guidance, emergency_help]
-
-
-def encrypt_data(data):
-
-    try:
-
-        return cipher.encrypt(data.encode('utf-8')).decode('utf-8')
-
-    except Exception as e:
-
-        logger.error(f"Encryption failed: {str(e)}")
-
-        return data
+app = Flask(__name__)
 
+# Hugging Face API configuration
+HUGGING_FACE_TOKEN = os.getenv('HUGGING_FACE_TOKEN', 'YOUR_HUGGING_FACE_TOKEN')
+WHISPER_API_URL = 'https://api-inference.huggingface.co/models/openai/whisper-tiny.en'
+DISEASE_API_URL = 'https://api-inference.huggingface.co/models/abhirajeshbhai/symptom-2-disease-net'
+NLP_API_URL = 'https://api-inference.huggingface.co/models/bert-base-uncased'
+HEADERS = {'Authorization': f'Bearer {HUGGING_FACE_TOKEN}'}
 
-def decrypt_data(encrypted_data):
+# Supported languages
+SUPPORTED_LANGUAGES = ['en', 'es', 'hi', 'zh']
 
+def call_hugging_face_api(api_url, data, is_binary=False):
+    """Helper function to call Hugging Face API"""
     try:
-
-        return cipher.decrypt(encrypted_data.encode('utf-8')).decode('utf-8')
-
-    except Exception as e:
-
-        logger.error(f"Decryption failed: {str(e)}")
-
-        return encrypted_data
-
-
-@lru_cache(maxsize=100)
-
-def cached_transcribe(audio_file, language):
-
-    audio, sr = librosa.load(audio_file, sr=16000)
-
-    language_code = {"English": "en", "Hindi": "hi", "Spanish": "es", "Mandarin": "zh"}.get(language, "en")
-
-    return transcribe_audio(audio, language_code)
-
-
-def extract_health_features(audio, sr):
-
+        if is_binary:
+            response = requests.post(api_url, headers=HEADERS, data=data, timeout=10)
+        else:
+            response = requests.post(api_url, headers=HEADERS, json={'inputs': data}, timeout=10)
+        response.raise_for_status()
+        return response.json()
+    except requests.RequestException as e:
+        return {'error': str(e)}
+
+@app.route('/process_voice', methods=['POST'])
+def process_voice():
+    """Process voice input for health assessment"""
     try:
-
-        audio = librosa.util.normalize(audio)
-
-        frame_duration = 30
-
-        frame_samples = int(sr * frame_duration / 1000)
-
-        frames = [audio[i:i + frame_samples] for i in range(0, len(audio), frame_samples)]
-
-        voiced_frames = [frame for frame in frames if len(frame) == frame_samples and vad.is_speech((frame * 32768).astype(np.int16).tobytes(), sr)]
-
-        if not voiced_frames:
-
-            raise ValueError("No voiced segments detected")
-
-        voiced_audio = np.concatenate(voiced_frames)
-
-        
-
-        frame_step = max(1, len(voiced_audio) // (sr // 4))
-
-        pitches, magnitudes = librosa.piptrack(y=voiced_audio[::frame_step], sr=sr, fmin=75, fmax=300)
-
-        valid_pitches = [p for p in pitches[magnitudes > 0] if 75 <= p <= 300]
-
-        pitch = np.mean(valid_pitches) if valid_pitches else 0
-
-        jitter = np.std(valid_pitches) / pitch if pitch and valid_pitches else 0
-
-        jitter = min(jitter, 10)
-
-        amplitudes = librosa.feature.rms(y=voiced_audio, frame_length=512, hop_length=128)[0]
-
-        shimmer = np.std(amplitudes) / np.mean(amplitudes) if np.mean(amplitudes) else 0
-
-        shimmer = min(shimmer, 10)
-
-        energy = np.mean(amplitudes)
-
-        
-
-        mfcc = np.mean(librosa.feature.mfcc(y=voiced_audio[::2], sr=sr, n_mfcc=4), axis=1)
-
-        spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=voiced_audio[::2], sr=sr, n_fft=512, hop_length=128))
-
-        
-
-        logger.debug(f"Extracted features: pitch={pitch:.2f}, jitter={jitter*100:.2f}%, shimmer={shimmer*100:.2f}%, energy={energy:.4f}, mfcc_mean={np.mean(mfcc):.2f}, spectral_centroid={spectral_centroid:.2f}")
-
-        return {
-
-            "pitch": pitch,
-
-            "jitter": jitter * 100,
-
-            "shimmer": shimmer * 100,
-
-            "energy": energy,
-
-            "mfcc_mean": np.mean(mfcc),
-
-            "spectral_centroid": spectral_centroid
-
+        data = request.get_json()
+        base64_audio = data.get('audio')
+        language = data.get('language', 'en')
+        user_email = data.get('user_email')
+
+        if not base64_audio or not user_email:
+            return jsonify({'error': 'Missing audio or user_email'}), 400
+        
+        if language not in SUPPORTED_LANGUAGES:
+            return jsonify({'error': f'Unsupported language. Supported: {SUPPORTED_LANGUAGES}'}), 400
+
+        # Decode base64 audio
+        audio_binary = base64.b64decode(base64_audio)
+
+        # Step 1: Speech-to-text using Whisper
+        whisper_result = call_hugging_face_api(WHISPER_API_URL, audio_binary, is_binary=True)
+        if 'error' in whisper_result:
+            return jsonify({'error': 'Speech recognition failed', 'details': whisper_result['error']}), 500
+        
+        text_output = whisper_result.get('text', '')
+        if not text_output:
+            return jsonify({'error': 'No text recognized from audio'}), 400
+
+        # Step 2: Health analysis using symptom-to-disease model
+        health_result = call_hugging_face_api(DISEASE_API_URL, text_output)
+        if 'error' in health_result:
+            return jsonify({'error': 'Health analysis failed', 'details': health_result['error']}), 500
+
+        # Extract disease and confidence
+        disease = health_result[0]['label'] if health_result else 'Unknown'
+        confidence = health_result[0]['score'] * 100 if health_result else 0.0
+
+        # Step 3: Generate response with disclaimer
+        response = {
+            'assessment_result': f'Possible {disease} detected. Consult a doctor.',
+            'confidence_score': confidence,
+            'timestamp': datetime.utcnow().isoformat(),
+            'disclaimer': 'This is not a medical diagnosis. Please consult a healthcare professional.',
+            'transcribed_text': text_output
         }
 
-    except Exception as e:
-
-        logger.error(f"Feature extraction failed: {str(e)}")
-
-        raise
-
-
-def transcribe_audio(audio, language="en"):
-
-    try:
-
-        whisper_model.config.forced_decoder_ids = whisper_processor.get_decoder_prompt_ids(
-
-            language=SUPPORTED_LANGUAGES.get({"en": "English", "hi": "Hindi", "es": "Spanish", "zh": "Mandarin"}.get(language, "English"), "english"), task="transcribe"
-
-        )
-
-        inputs = whisper_processor(audio, sampling_rate=16000, return_tensors="pt")
-
-        with torch.no_grad():
-
-            generated_ids = whisper_model.generate(inputs["input_features"], max_new_tokens=50)
-
-        transcription = whisper_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
-        logger.info(f"Transcription (language: {language}): {transcription}")
-
-        return transcription
+        return jsonify(response), 200
 
     except Exception as e:
+        return jsonify({'error': str(e)}), 500
 
-        logger.error(f"Transcription failed: {str(e)}")
-
-        return None
-
-
-async def get_chatbot_response(message, language="en"):
-
-    if not chat or not message:
-
-        return "Unable to generate response.", None
-
-    language_code = {"English": "en", "Hindi": "hi", "Spanish": "es", "Mandarin": "zh"}.get(language, "en")
-
-    full_context = "\n".join(context) + f"\nUser: {message}\nMindCare:"
-
+@app.route('/health_query', methods=['POST'])
+def health_query():
+    """Handle health-related queries using BERT"""
     try:
+        data = request.get_json()
+        query = data.get('query')
+        language = data.get('language', 'en')
+
+        if not query:
+            return jsonify({'error': 'Missing query'}), 400
+
+        if language not in SUPPORTED_LANGUAGES:
+            return jsonify({'error': f'Unsupported language. Supported: {SUPPORTED_LANGUAGES}'}), 400
+
+        # Call BERT for query understanding
+        nlp_result = call_hugging_face_api(NLP_API_URL, query)
+        if 'error' in nlp_result:
+            return jsonify({'error': 'Query processing failed', 'details': nlp_result['error']}), 500
+
+        # Simplified response (mocked for demo, extend with actual logic)
+        response = {
+            'answer': f'Understood: {query}. For accurate information, consult a healthcare provider.',
+            'timestamp': datetime.utcnow().isoformat(),
+            'disclaimer': 'This is not a medical diagnosis. Please consult a healthcare professional.'
+        }
 
-        response = await asyncio.get_event_loop().run_in_executor(None, lambda: chat.send_message(full_context).text)
-
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
-
-            tts = gTTS(text=response, lang=language_code, slow=False)
-
-            tts.save(temp_audio.name)
-
-            audio_path = temp_audio.name
-
-        logger.info(f"Generated response: {response[:100]}... and audio at {audio_path}")
-
-        return response, audio_path
+        return jsonify(response), 200
 
     except Exception as e:
+        return jsonify({'error': str(e)}), 500
 
-        logger.error(f"Chatbot response failed: {str(e)}")
-
-        return "Error generating response. Please check your input or API key.", None
-
-
-def analyze_symptoms(text, features):
-
-    feedback = []
-
-    text = text.lower() if text else ""
-
-    
-
-    if "cough" in text or "coughing" in text:
-
-        feedback.append("You mentioned a cough. This could indicate a respiratory issue like a cold or bronchitis. Stay hydrated, rest, and consider consulting a doctor if it persists.")
-
-    elif "fever" in text or "temperature" in text:
-
-        feedback.append("You mentioned a fever. This could be a sign of infection or illness. Monitor your temperature, stay hydrated, and seek medical advice if it exceeds 100.4°F (38°C).")
-
-    elif "headache" in text:
-
-        feedback.append("You mentioned a headache. This could be due to stress, dehydration, or tension. Try resting, drinking water, and using a mild pain reliever like Paracetamol. Consult a doctor if severe.")
-
-    elif "stress" in text or "anxious" in text or "mental stress" in text:
-
-        feedback.append("You mentioned stress or anxiety. Try deep breathing exercises or mindfulness. If persistent, consider speaking with a mental health professional for support.")
-
-    elif "respiratory" in text or "breathing" in text or "shortness of breath" in text:
-
-        feedback.append("You mentioned respiratory issues or shortness of breath. This could indicate asthma or an infection. Seek medical attention if it worsens.")
-
-    elif "cold" in text:
-
-        feedback.append("You mentioned a cold. This could be a viral infection. Rest, stay hydrated, and consider over-the-counter remedies like decongestants or honey for cough. Consult a doctor if symptoms worsen.")
-
-    
-
-    if features["jitter"] > 6.5:
-
-        feedback.append(f"High jitter ({features['jitter']:.2f}%) detected, suggesting potential vocal cord strain or respiratory issues. Consult a doctor.")
-
-    elif features["jitter"] > 4.0:
-
-        feedback.append(f"Moderate jitter ({features['jitter']:.2f}%) detected, indicating possible vocal instability. Monitor and rest your voice.")
-
-    
-
-    if features["shimmer"] > 7.5:
-
-        feedback.append(f"High shimmer ({features['shimmer']:.2f}%) suggests possible emotional stress or vocal fatigue. Consider professional evaluation.")
-
-    elif features["shimmer"] > 5.0:
-
-        feedback.append(f"Moderate shimmer ({features['shimmer']:.2f}%) indicates mild vocal strain. Rest and hydrate recommended.")
-
-    
-
-    if features["energy"] < 0.003:
-
-        feedback.append(f"Very low vocal energy ({features['energy']:.4f}) detected, possibly indicating fatigue or low mood. Rest and consult a doctor if needed.")
-
-    elif features["energy"] < 0.007:
-
-        feedback.append(f"Low vocal energy ({features['energy']:.4f}) suggests possible fatigue. Ensure adequate rest.")
-
-    
-
-    if features["pitch"] < 70 or features["pitch"] > 290:
-
-        feedback.append(f"Unusual pitch ({features['pitch']:.2f} Hz) detected, which may indicate vocal cord issues. Consult a doctor.")
-
-    elif 70 <= features["pitch"] <= 90 or 270 <= features["pitch"] <= 290:
-
-        feedback.append(f"Pitch ({features['pitch']:.2f} Hz) is slightly outside typical range, possibly due to tension. Monitor your voice.")
-
-    
-
-    if features["spectral_centroid"] > 2700:
-
-        feedback.append(f"High spectral centroid ({features['spectral_centroid']:.2f} Hz) suggests tense speech, potentially linked to stress or anxiety.")
-
-    elif features["spectral_centroid"] > 2200:
-
-        feedback.append(f"Elevated spectral centroid ({features['spectral_centroid']:.2f} Hz) may indicate mild tension in speech.")
-
-    
-
-    if not feedback:
-
-        feedback.append("No significant health concerns detected from voice or text analysis. Maintain a healthy lifestyle and consult a doctor if symptoms arise.")
-
-    
-
-    logger.debug(f"Generated feedback: {feedback}")
-
-    return "\n".join(feedback)
-
-
-def store_user_consent(email, language):
-
-    if not sf:
-
-        logger.warning("Salesforce not connected; skipping consent storage")
-
-        return None
-
-    try:
-
-        email_to_use = email.strip() if email and email.strip() else DEFAULT_EMAIL
-
-        sanitized_email = email_to_use.replace("'", "\\'").replace('"', '\\"')
-
-        query = f"SELECT Id FROM HealthUser__c WHERE Email__c = '{sanitized_email}'"
-
-        logger.debug(f"Executing SOQL query: {query}")
-
-        user = sf.query(query)
-
-        user_id = None
-
-        if user["totalSize"] == 0:
-
-            logger.info(f"No user found for email: {sanitized_email}, creating new user")
-
-            user = sf.HealthUser__c.create({
-
-                "Email__c": sanitized_email,
-
-                "Language__c": SALESFORCE_LANGUAGE_MAP.get(language, "English"),
-
-                "ConsentGiven__c": True
-
-            })
-
-            user_id = user["id"]
-
-            logger.info(f"Created new user with email: {sanitized_email}, ID: {user_id}")
-
-        else:
-
-            user_id = user["records"][0]["Id"]
-
-            logger.info(f"Found existing user with email: {sanitized_email}, ID: {user_id}")
-
-            sf.HealthUser__c.update(user_id, {
-
-                "Language__c": SALESFORCE_LANGUAGE_MAP.get(language, "English"),
-
-                "ConsentGiven__c": True
-
-            })
-
-            logger.info(f"Updated user with email: {sanitized_email}")
-
-        sf.ConsentLog__c.create({
-
-            "HealthUser__c": user_id,
-
-            "ConsentType__c": "Voice Analysis",
-
-            "ConsentDate__c": datetime.utcnow().isoformat()
-
-        })
-
-        logger.info(f"Stored consent log for user ID: {user_id}")
-
-        return user_id
-
-    except Exception as e:
-
-        logger.error(f"Consent storage failed: {str(e)}")
-
-        logger.exception("Stack trace for consent storage failure:")
-
-        return None
-
-
-def generate_pdf_report(feedback, transcription, features, language, email, suggestions):
-
-    try:
-
-        feedback = feedback.replace('<', '<').replace('>', '>').replace('&', '&')
-
-        transcription = transcription.replace('<', '<').replace('>', '>').replace('&', '&') if transcription else "None"
-
-        suggestions = suggestions.replace('<', '<').replace('>', '>').replace('&', '&') if suggestions else "None"
-
-        email_to_use = email.strip() if email and email.strip() else DEFAULT_EMAIL
-
-        email = email_to_use.replace('<', '<').replace('>', '>').replace('&', '&')
-
-        language_display = SALESFORCE_LANGUAGE_MAP.get(language, "English")
-
-        
-
-        ist = pytz.timezone('Asia/Kolkata')
-
-        ist_time = datetime.now(ist).strftime("%I:%M %p IST on %B %d, %Y")
-
-        logger.debug(f"Generating PDF with IST time: {ist_time}, feedback: {feedback[:100]}..., transcription: {transcription[:100]}..., suggestions: {suggestions[:100]}..., language: {language_display}, email: {email}")
-
-        
-
-        debug_dir = "/tmp/mindcare_logs"
-
-        os.makedirs(debug_dir, exist_ok=True)
-
-        timestamp = datetime.now(ist).strftime("%Y%m%d_%H%M%S")
-
-        pdf_path = os.path.join(debug_dir, f"report_{timestamp}.pdf")
-
-        
-
-        doc = SimpleDocTemplate(pdf_path, pagesize=A4, rightMargin=inch, leftMargin=inch, topMargin=inch, bottomMargin=inch)
-
-        styles = getSampleStyleSheet()
-
-        title_style = ParagraphStyle(
-
-            name='Title',
-
-            fontSize=16,
-
-            leading=20,
-
-            alignment=1,
-
-            spaceAfter=12,
-
-            fontName='Times-Bold'
-
-        )
-
-        heading_style = ParagraphStyle(
-
-            name='Heading1',
-
-            fontSize=14,
-
-            leading=16,
-
-            spaceBefore=12,
-
-            spaceAfter=8,
-
-            fontName='Times-Bold'
-
-        )
-
-        subheading_style = ParagraphStyle(
-
-            name='Heading2',
-
-            fontSize=12,
-
-            leading=14,
-
-            spaceBefore=10,
-
-            spaceAfter=6,
-
-            fontName='Times-Bold'
-
-        )
-
-        normal_style = ParagraphStyle(
-
-            name='Normal',
-
-            fontSize=12,
-
-            leading=14,
-
-            spaceAfter=6,
-
-            fontName='Times-Roman'
-
-        )
-
-        bullet_style = ParagraphStyle(
-
-            name='Bullet',
-
-            fontSize=12,
-
-            leading=14,
-
-            leftIndent=20,
-
-            firstLineIndent=-10,
-
-            spaceAfter=4,
-
-            fontName='Times-Roman'
-
-        )
-
-        
-
-        story = []
-
-        story.append(Paragraph("MindCare Health Assistant Report", title_style))
-
-        story.append(Paragraph(f"Generated on {ist_time}", normal_style))
-
-        story.append(Spacer(1, 0.5 * inch))
-
-        
-
-        story.append(Paragraph("User Information", heading_style))
-
-        user_info = [
-
-            ListItem(Paragraph(f"<b>Email</b>: {email}", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"<b>Language</b>: {language_display}", bullet_style), bulletText="•")
-
-        ]
-
-        story.append(ListFlowable(user_info, bulletType='bullet'))
-
-        story.append(Spacer(1, 0.25 * inch))
-
-        
-
-        story.append(Paragraph("Voice Analysis Results", heading_style))
-
-        story.append(Paragraph("Health Assessment", subheading_style))
-
-        for line in feedback.split('\n'):
-
-            if line.strip():
-
-                story.append(Paragraph(line, normal_style))
-
-        story.append(Spacer(1, 0.1 * inch))
-
-        
-
-        story.append(Paragraph("Health Suggestions", subheading_style))
-
-        for line in suggestions.split('\n'):
-
-            if line.strip():
-
-                story.append(Paragraph(line, normal_style))
-
-        story.append(Spacer(1, 0.1 * inch))
-
-        
-
-        story.append(Paragraph("Voice Analysis Details", subheading_style))
-
-        details = [
-
-            ListItem(Paragraph(f"Pitch: {features['pitch']:.2f} Hz", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"Jitter: {features['jitter']:.2f}% (voice stability)", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"Shimmer: {features['shimmer']:.2f}% (amplitude variation)", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"Energy: {features['energy']:.4f} (vocal intensity)", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"MFCC Mean: {features['mfcc_mean']:.2f} (timbre quality)", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"Spectral Centroid: {features['spectral_centroid']:.2f} Hz (voice brightness)", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"Transcription: {transcription}", bullet_style), bulletText="•")
-
-        ]
-
-        story.append(ListFlowable(details, bulletType='bullet'))
-
-        story.append(Spacer(1, 0.1 * inch))
-
-        
-
-        story.append(Paragraph("Transcription", subheading_style))
-
-        story.append(Paragraph(transcription, normal_style))
-
-        story.append(Spacer(1, 0.1 * inch))
-
-        
-
-        story.append(Paragraph("Voice Metrics", subheading_style))
-
-        metrics = [
-
-            ListItem(Paragraph(f"<b>Pitch</b>: {features['pitch']:.2f} Hz", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"<b>Jitter</b>: {features['jitter']:.2f}%", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"<b>Shimmer</b>: {features['shimmer']:.2f}%", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"<b>Energy</b>: {features['energy']:.4f}", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"<b>MFCC Mean</b>: {features['mfcc_mean']:.2f}", bullet_style), bulletText="•"),
-
-            ListItem(Paragraph(f"<b>Spectral Centroid</b>: {features['spectral_centroid']:.2f} Hz", bullet_style), bulletText="•")
-
-        ]
-
-        story.append(ListFlowable(metrics, bulletType='bullet'))
-
-        story.append(Spacer(1, 0.1 * inch))
-
-        
-
-        story.append(Paragraph("Disclaimer", heading_style))
-
-        story.append(Paragraph("This report is a preliminary analysis and not a medical diagnosis. Always consult a healthcare provider.", normal_style))
-
-        
-
-        doc.build(story)
-
-        logger.info(f"Generated PDF report: {pdf_path}")
-
-        
-
-        try:
-
-            with open(pdf_path, 'rb') as f:
-
-                pdf_content = f.read()
-
-            if len(pdf_content) > 0 and pdf_content.startswith(b'%PDF'):
-
-                return pdf_path, None
-
-            else:
-
-                logger.error(f"PDF file {pdf_path} is corrupt or empty")
-
-                return None, f"PDF generation failed: Generated PDF is corrupt or empty."
-
-        except Exception as e:
-
-            logger.error(f"Failed to verify PDF {pdf_path}: {str(e)}")
-
-            return None, f"PDF generation failed: Unable to verify PDF. Error: {str(e)}."
-
-    except Exception as e:
-
-        logger.error(f"PDF generation failed: {str(e)}")
-
-        logger.exception("Stack trace for PDF generation failure:")
-
-        return None, f"PDF generation failed: {str(e)}."
-
-
-def store_in_salesforce(user_id, audio_file, feedback, respiratory_score, mental_health_score, features, transcription, language):
-
-    if not sf:
-
-        logger.warning("Salesforce not connected; skipping storage")
-
-        return
-
-    try:
-
-        with open(audio_file, "rb") as f:
-
-            audio_content = base64.b64encode(f.read()).decode()
-
-        content_version = sf.ContentVersion.create({
-
-            "Title": f"Voice_Assessment_{datetime.utcnow().isoformat()}",
-
-            "PathOnClient": os.path.basename(audio_file),
-
-            "VersionData": audio_content,
-
-            "IsMajorVersion": True
-
-        })
-
-        content_document_id = sf.query(f"SELECT ContentDocumentId FROM ContentVersion WHERE Id = '{content_version['id']}'")["records"][0]["ContentDocumentId"]
-
-        file_url = f"{SF_INSTANCE_URL}/lightning/r/ContentDocument/{content_document_id}/view"
-
-
-        feedback_str = feedback[:32767]
-
-        assessment = sf.VoiceAssessment__c.create({
-
-            "HealthUser__c": user_id,
-
-            "VoiceRecording__c": file_url,
-
-            "AssessmentResult__c": feedback_str,
-
-            "AssessmentDate__c": datetime.utcnow().isoformat(),
-
-            "ConfidenceScore__c": 95.0,
-
-            "RespiratoryScore__c": float(respiratory_score),
-
-            "MentalHealthScore__c": float(mental_health_score),
-
-            "Pitch__c": float(features["pitch"]),
-
-            "Jitter__c": float(features["jitter"]),
-
-            "Shimmer__c": float(features["shimmer"]),
-
-            "Energy__c": float(features["energy"]),
-
-            "Transcription__c": transcription or "None",
-
-            "Language__c": SALESFORCE_LANGUAGE_MAP.get(language, "English")
-
-        })
-
-        sf.ContentDocumentLink.create({
-
-            "ContentDocumentId": content_document_id,
-
-            "LinkedEntityId": assessment["id"],
-
-            "ShareType": "V"
-
-        })
-
-        logger.info(f"Stored assessment in Salesforce: {assessment['id']}")
-
-    except Exception as e:
-
-        logger.error(f"Salesforce storage failed: {str(e)}")
-
-        logger.exception("Stack trace for Salesforce storage failure:")
-
-        raise
-
-
-async def analyze_voice(audio_file=None, language="English", email=None):
-
-    global usage_metrics
-
-    usage_metrics["total_assessments"] += 1
-
-    usage_metrics["assessments_by_language"][language] = usage_metrics["assessments_by_language"].get(language, 0) + 1
-
-
-    try:
-
-        if not audio_file or not os.path.exists(audio_file):
-
-            raise ValueError("No valid audio file provided")
-
-        
-
-        audio, sr = librosa.load(audio_file, sr=16000)
-
-        max_duration = 10
-
-        if len(audio) > max_duration * sr:
-
-            audio = audio[:max_duration * sr]
-
-            logger.info(f"Truncated audio to first {max_duration} seconds for faster processing")
-
-        if len(audio) < sr:
-
-            raise ValueError("Audio too short (minimum 1 second)")
-
-
-        language_code = {"English": "en", "Hindi": "hi", "Spanish": "es", "Mandarin": "zh"}.get(language, "en")
-
-        user_id = store_user_consent(email, language)
-
-        if not user_id:
-
-            logger.warning("Proceeding with analysis despite consent storage failure")
-
-            feedback_message = "Warning: User consent could not be stored in Salesforce, but analysis will proceed.\n"
-
-        else:
-
-            feedback_message = ""
-
-
-        features = extract_health_features(audio, sr)
-
-        transcription = cached_transcribe(audio_file, language)
-
-        feedback = analyze_symptoms(transcription, features)
-
-
-        respiratory_score = features["jitter"]
-
-        mental_health_score = features["shimmer"]
-
-
-        feedback = feedback_message + feedback + "\n\n**Voice Analysis Details**:\n"
-
-        feedback += f"- Pitch: {features['pitch']:.2f} Hz\n"
-
-        feedback += f"- Jitter: {features['jitter']:.2f}% (voice stability)\n"
-
-        feedback += f"- Shimmer: {features['shimmer']:.2f}% (amplitude variation)\n"
-
-        feedback += f"- Energy: {features['energy']:.4f} (vocal intensity)\n"
-
-        feedback += f"- MFCC Mean: {features['mfcc_mean']:.2f} (timbre quality)\n"
-
-        feedback += f"- Spectral Centroid: {features['spectral_centroid']:.2f} Hz (voice brightness)\n"
-
-        feedback += f"- Transcription: {transcription if transcription else 'None'}\n"
-
-        feedback += f"- Email: {email if email and email.strip() else DEFAULT_EMAIL}\n"
-
-        feedback += "\n**Disclaimer**: This is a preliminary analysis. Consult a healthcare provider for professional evaluation."
-
-
-        suggestions, suggestion_audio = await get_chatbot_response(feedback, language)
-
-
-        if user_id and sf:
-
-            store_in_salesforce(user_id, audio_file, feedback, respiratory_score, mental_health_score, features, transcription, language)
-
-        else:
-
-            logger.warning("Skipping Salesforce storage due to missing user_id or Salesforce connection")
-
-
-        file_path, pdf_error = generate_pdf_report(feedback, transcription, features, language, email, suggestions)
-
-        if pdf_error:
-
-            feedback += f"\n\n**Error**: {pdf_error}"
-
-            return feedback, file_path, suggestions, suggestion_audio
-
-
-        try:
-
-            os.remove(audio_file)
-
-            logger.info(f"Deleted audio file: {audio_file}")
-
-        except Exception as e:
-
-            logger.error(f"Failed to delete audio file: {str(e)}")
-
-
-        return feedback, file_path, suggestions, suggestion_audio
-
-    except Exception as e:
-
-        logger.error(f"Audio processing failed: {str(e)}")
-
-        return f"Error: {str(e)}", None, "Error: Could not generate suggestions due to audio processing failure.", None
-
-
-def launch():
-
-    custom_css = """
-
-    .gradio-container {
-
-        max-width: 1200px;
-
-        margin: auto;
-
-        font-family: 'Roboto', sans-serif;
-
-        background-color: #f5f7fa;
-
-        color: #333;
-
-    }
-
-    @import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap');
-
-    h1, h3 {
-
-        background: linear-gradient(to right, #007bff, #0056b3);
-
-        color: white;
-
-        padding: 15px;
-
-        border-radius: 8px;
-
-        text-align: center;
-
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-
-    }
-
-    .gr-column {
-
-        background: white;
-
-        border-radius: 8px;
-
-        padding: 20px;
-
-        box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
-
-        margin: 10px;
-
-    }
-
-    .gr-button {
-
-        background: #007bff;
-
-        color: white;
-
-        border: none;
-
-        border-radius: 6px;
-
-        padding: 10px 20px;
-
-        font-weight: bold;
-
-        transition: background 0.3s;
-
-    }
-
-    .gr-button:hover {
-
-        background: #0056b3;
-
-    }
-
-    .gr-textbox, .gr-dropdown, .gr-checkbox, .gr-file, .gr-audio {
-
-        border-radius: 6px;
-
-        border: 1px solid #ced4da;
-
-    }
-
-    .gr-textbox textarea {
-
-        font-size: 14px;
-
-    }
-
-    #health-results {
-
-        background: #e9f7ef;
-
-        border: 1px solid #28a745;
-
-        border-radius: 6px;
-
-    }
-
-    .no-microphone-warning {
-
-        color: red;
-
-        font-weight: bold;
-
-    }
-
-    """
-
-
-    def check_microphone_access():
-
-        try:
-
-            from navigator import mediaDevices
-
-            devices = mediaDevices.enumerateDevices()
-
-            for device in devices:
-
-                if device.kind == "audioinput":
-
-                    return None
-
-            return "Microphone access is not available. Please upload an audio file or check browser permissions."
-
-        except Exception as e:
-
-            logger.error(f"Microphone access check failed: {str(e)}")
-
-            return "Microphone access is not available. Please upload an audio file or check browser permissions."
-
-
-    with gr.Blocks(title="MindCare Health Assistant", css=custom_css) as demo:
-
-        gr.Markdown("Record your voice or type a message for health assessments and suggestions.")
-
-
-        with gr.Row():
-
-            with gr.Column():
-
-                gr.Markdown("### Voice Analysis")
-
-                mic_warning = gr.Markdown()
-
-                mic_warning.value = check_microphone_access() or ""
-
-                gr.Markdown("Upload voice (1+ sec) describing symptoms (e.g., 'I have a cough' or 'I feel stressed'). Note: Microphone recording may not be supported in all contexts; use file upload instead.")
-
-                email_input = gr.Textbox(label="Enter Your Email", placeholder="e.g., user@example.com", value="")
-
-                language_input = gr.Dropdown(choices=list(SUPPORTED_LANGUAGES.keys()), label="Select Language", value="English")
-
-                consent_input = gr.Checkbox(label="I consent to data storage and voice analysis", value=True, interactive=False)
-
-                audio_input = gr.Audio(type="filepath", label="Upload Voice (WAV, MP3, FLAC)", format="wav", interactive=True)
-
-                voice_output = gr.Textbox(label="Health Assessment Results", elem_id="health-results")
-
-                file_output = gr.File(label="Download Assessment Report (PDF)", file_types=[".pdf"])
-
-                submit_btn = gr.Button("Submit")
-
-                clear_btn = gr.Button("Clear")
-
-
-            with gr.Column():
-
-                gr.Markdown("### Health Suggestions")
-
-                gr.Markdown("Enter a message for personalized health advice or get suggestions based on voice analysis.")
-
-                text_input = gr.Textbox(label="Enter your message (optional)")
-
-                text_output = gr.Textbox(label="Response")
-
-                audio_output = gr.Audio(label="Response Audio")
-
-                suggest_submit_btn = gr.Button("Submit")
-
-                suggest_clear_btn = gr.Button("Clear")
-
-
-        submit_btn.click(
-
-            fn=analyze_voice,
-
-            inputs=[audio_input, language_input, email_input],
-
-            outputs=[voice_output, file_output, text_output, audio_output]
-
-        )
-
-        clear_btn.click(
-
-            fn=lambda: (gr.update(value=None), gr.update(value="English"), gr.update(value=""), gr.update(value=""), gr.update(value=None), gr.update(value=""), gr.update(value=None)),
-
-            inputs=None,
-
-            outputs=[audio_input, language_input, email_input, voice_output, file_output, text_output, audio_output]
-
-        )
-
-        suggest_submit_btn.click(
-
-            fn=get_chatbot_response,
-
-            inputs=[text_input, language_input],
-
-            outputs=[text_output, audio_output]
-
-        )
-
-        suggest_clear_btn.click(
-
-            fn=lambda: (gr.update(value=""), gr.update(value=""), gr.update(value=None)),
-
-            inputs=None,
-
-            outputs=[text_input, text_output, audio_output]
-
-        )
-
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-
-
-if __name__ == "__main__":
-
-    launch()
-
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=False)