Update app.py

app.py

@@ -124,442 +124,6 @@ For severe distress:
 """
 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
-
-def decrypt_data(encrypted_data):
-    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):
-    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 // 16))  # Increased step for faster processing
-        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=256)[0]  # Increased hop_length
-        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[::8], sr=sr, n_mfcc=4), axis=1)  # Further reduced sampling
-        spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=voiced_audio[::8], sr=sr, n_fft=512, hop_length=256))
-        
-        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
-        }
-    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=20)  # Further reduced tokens
-        transcription = whisper_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-        logger.info(f"Transcription (language: {language}): {transcription}")
-        return transcription
-    except Exception as e:
-        logger.error(f"Transcription failed: {str(e)}")
-        return None
-
-async def get_chatbot_response(message, language="en", retries=2, timeout=10):
-    if not message:
-        return "No input provided. Please describe your symptoms or concerns.", None
-    if not chat:
-        logger.warning("Gemini chat object is None, attempting to reinitialize")
-        global chat
-        chat = initialize_gemini()
-        if not chat:
-            return "Error: Unable to connect to Gemini API. Please check API key.", None
-    
-    language_code = {"English": "en", "Hindi": "hi", "Spanish": "es", "Mandarin": "zh"}.get(language, "en")
-    full_context = "\n".join(context) + f"\nUser: {message}\nMindCare: Provide response in 6-8 simple bullet points, tailored to the user's input, in a clear and empathetic tone."
-    
-    for attempt in range(retries + 1):
-        try:
-            async with asyncio.timeout(timeout):
-                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
-        except asyncio.TimeoutError:
-            logger.error(f"Chatbot response timed out on attempt {attempt + 1}")
-            if attempt == retries:
-                return "Error: Response generation timed out.", None
-        except Exception as e:
-            logger.error(f"Chatbot response failed on attempt {attempt + 1}: {str(e)}")
-            if attempt == retries:
-                return f"Error generating response: {str(e)}", None
-        await asyncio.sleep(1)  # Brief delay between retries
-    return "Error: Unable to generate response after retries.", None
-
-async def translate_text(text, target_language, retries=2, timeout=10):
-    if not chat or not text:
-        return text
-    if target_language == "English":
-        return text
-    language_code = {"Hindi": "hi", "Spanish": "es", "Mandarin": "zh"}.get(target_language, "en")
-    prompt = f"Translate the following text into {target_language} while preserving formatting (e.g., bullet points, newlines):\n\n{text}"
-    
-    for attempt in range(retries + 1):
-        try:
-            async with asyncio.timeout(timeout):
-                response = await asyncio.get_event_loop().run_in_executor(None, lambda: chat.send_message(prompt).text)
-                logger.info(f"Translated text to {target_language}: {response[:100]}...")
-                return response
-        except asyncio.TimeoutError:
-            logger.error(f"Translation to {target_language} timed out on attempt {attempt + 1}")
-            if attempt == retries:
-                return text
-        except Exception as e:
-            logger.error(f"Translation to {target_language} failed on attempt {attempt + 1}: {str(e)}")
-            if attempt == retries:
-                return text
-        await asyncio.sleep(1)
-    return text
-
-async def analyze_symptoms(text, features, language="English"):
-    feedback = []
-    suggestions = []
-    text = text.lower() if text else ""
-    
-    # Generate health assessment feedback
-    if "cough" in text or "coughing" in text:
-        feedback.append("You mentioned a cough, which may suggest a cold or respiratory issue.")
-        suggestions.extend([
-            "• Drink warm fluids like herbal tea or water to soothe your throat.",
-            "• Rest to help your body recover from possible infection.",
-            "• Use a humidifier to ease throat irritation.",
-            "• Consider over-the-counter cough remedies, but consult a doctor first.",
-            "• Monitor symptoms; see a doctor if the cough lasts over a week."
-        ])
-    elif "fever" in text or "temperature" in text:
-        feedback.append("You mentioned a fever, which could indicate an infection.")
-        suggestions.extend([
-            "• Stay hydrated with water or electrolyte drinks.",
-            "• Rest to support your immune system.",
-            "• Monitor your temperature regularly.",
-            "• Use paracetamol to reduce fever, but follow dosage instructions.",
-            "• Seek medical advice if fever exceeds 100.4°F (38°C) for over 2 days."
-        ])
-    elif "headache" in text:
-        feedback.append("You mentioned a headache, possibly due to stress or dehydration.")
-        suggestions.extend([
-            "• Drink plenty of water to stay hydrated.",
-            "• Take short breaks to relax your mind.",
-            "• Try a mild pain reliever like ibuprofen, but consult a doctor.",
-            "• Practice deep breathing to reduce tension.",
-            "• Ensure you're getting enough sleep (7-8 hours)."
-        ])
-    elif "stress" in text or "anxious" in text or "mental stress" in text:
-        feedback.append("You mentioned stress or anxiety, which can affect well-being.")
-        suggestions.extend([
-            "• Try 5 minutes of deep breathing to calm your mind.",
-            "• Write in a journal to process your thoughts.",
-            "• Take a short walk in nature to relax.",
-            "• Practice mindfulness or meditation daily.",
-            "• Talk to a trusted friend or professional for support.",
-            "• Prioritize sleep and avoid excessive caffeine."
-        ])
-    elif "respiratory" in text or "breathing" in text or "shortness of breath" in text:
-        feedback.append("You mentioned breathing issues, which may indicate asthma or infection.")
-        suggestions.extend([
-            "• Avoid triggers like smoke or allergens.",
-            "• Practice slow, deep breathing exercises.",
-            "• Stay in a well-ventilated area.",
-            "• Monitor symptoms and seek medical help if severe.",
-            "• Rest to reduce strain on your respiratory system."
-        ])
-    elif "cold" in text:
-        feedback.append("You mentioned a cold, likely a viral infection.")
-        suggestions.extend([
-            "• Drink warm fluids like soup or tea.",
-            "• Rest to help your body fight the virus.",
-            "• Use saline nasal spray to relieve congestion.",
-            "• Take over-the-counter cold remedies, but consult a doctor.",
-            "• Stay hydrated and avoid strenuous activity."
-        ])
-    
-    # Voice feature-based feedback and suggestions
-    if features["jitter"] > 6.5:
-        feedback.append(f"High jitter ({features['jitter']:.2f}%) suggests vocal strain or respiratory issues.")
-        suggestions.append("• Rest your voice and avoid shouting.")
-    elif features["jitter"] > 4.0:
-        feedback.append(f"Moderate jitter ({features['jitter']:.2f}%) indicates possible vocal instability.")
-        suggestions.append("• Sip warm water to soothe your vocal cords.")
-    
-    if features["shimmer"] > 7.5:
-        feedback.append(f"High shimmer ({features['shimmer']:.2f}%) may indicate emotional stress.")
-        suggestions.append("• Try relaxation techniques like yoga or meditation.")
-    elif features["shimmer"] > 5.0:
-        feedback.append(f"Moderate shimmer ({features['shimmer']:.2f}%) suggests mild vocal strain.")
-        suggestions.append("• Stay hydrated to support vocal health.")
-    
-    if features["energy"] < 0.003:
-        feedback.append(f"Low vocal energy ({features['energy']:.4f}) may indicate fatigue.")
-        suggestions.append("• Ensure 7-8 hours of sleep nightly.")
-    elif features["energy"] < 0.007:
-        feedback.append(f"Low vocal energy ({features['energy']:.4f}) suggests possible tiredness.")
-        suggestions.append("• Take short naps to boost energy.")
-    
-    if features["pitch"] < 70 or features["pitch"] > 290:
-        feedback.append(f"Unusual pitch ({features['pitch']:.2f} Hz) may indicate vocal issues.")
-        suggestions.append("• Consult a doctor for a vocal health check.")
-    elif 70 <= features["pitch"] <= 90 or 270 <= features["pitch"] <= 290:
-        feedback.append(f"Pitch ({features['pitch']:.2f} Hz) is slightly outside typical range.")
-        suggestions.append("• Avoid straining your voice during conversations.")
-    
-    if features["spectral_centroid"] > 2700:
-        feedback.append(f"High spectral centroid ({features['spectral_centroid']:.2f} Hz) suggests tense speech.")
-        suggestions.append("• Practice slow, calm speaking to reduce tension.")
-    elif features["spectral_centroid"] > 2200:
-        feedback.append(f"Elevated spectral centroid ({features['spectral_centroid']:.2f} Hz) may indicate mild tension.")
-        suggestions.append("• Relax your jaw and shoulders while speaking.")
-    
-    if not feedback:
-        feedback.append("No significant health concerns detected from voice or text analysis.")
-        suggestions.extend([
-            "• Maintain a balanced diet with fruits and vegetables.",
-            "• Exercise regularly for overall health.",
-            "• Stay hydrated with 8 glasses of water daily.",
-            "• Get 7-8 hours of sleep each night.",
-            "• Practice stress-relief techniques like meditation.",
-            "• Schedule regular health check-ups."
-        ])
-    
-    # Ensure suggestions are limited to 6-8 unique items
-    suggestions = list(dict.fromkeys(suggestions))[:8]
-    if len(suggestions) < 6:
-        suggestions.extend([
-            "• Stay active with light exercise like walking.",
-            "• Practice gratitude to boost mental well-being."
-        ][:6 - len(suggestions)])
-    
-    # Translate feedback and suggestions to the selected language
-    feedback_text = "\n".join(feedback)
-    suggestions_text = "\n".join(suggestions)
-    try:
-        translated_feedback = await translate_text(feedback_text, language)
-        translated_suggestions = await translate_text(suggestions_text, language)
-    except Exception as e:
-        logger.error(f"Translation failed: {str(e)}")
-        translated_feedback = feedback_text
-        translated_suggestions = suggestions_text
-    
-    logger.debug(f"Generated feedback: {translated_feedback}, Suggestions: {translated_suggestions}")
-    return translated_feedback, translated_suggestions
-
-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:
-
-System: **Updated Code with Fixes for Error and Performance**
-
-<xaiArtifact artifact_id="15a4c230-0090-4372-9c79-3b68c1f53acc" artifact_version_id="b21f972a-9b0b-4e87-bc53-ef7ca9e86329" title="main.py" contentType="text/python">
-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
-import base64
-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", "[email protected]")
-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", "[email protected]")
-
-# 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 with retry logic
-def initialize_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")
-        return chat
-    except Exception as e:
-        logger.error(f"Google Gemini initialization failed: {str(e)}")
-        return None
-
-chat = initialize_gemini()
-
-# 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')
@@ -595,18 +159,18 @@ def extract_health_features(audio, sr):
             raise ValueError("No voiced segments detected")
         voiced_audio = np.concatenate(voiced_frames)
         
-        frame_step = max(1, len(voiced_audio) // (sr // 16))  # Increased step for faster processing
+        frame_step = max(1, len(voiced_audio) // (sr // 16))
         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=256)[0]  # Increased hop_length
+        amplitudes = librosa.feature.rms(y=voiced_audio, frame_length=512, hop_length=256)[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[::8], sr=sr, n_mfcc=4), axis=1)  # Further reduced sampling
+        mfcc = np.mean(librosa.feature.mfcc(y=voiced_audio[::8], sr=sr, n_mfcc=4), axis=1)
         spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=voiced_audio[::8], sr=sr, n_fft=512, hop_length=256))
         
         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}")
@@ -627,7 +191,7 @@ def extract_health_features(audio, sr):
             "energy": 0,
             "mfcc_mean": 0,
             "spectral_centroid": 0
-        }  # Return default values to prevent failure
+        }
 
 def transcribe_audio(audio, language="en"):
     try:
@@ -636,7 +200,7 @@ def transcribe_audio(audio, language="en"):
         )
         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=20)  # Further reduced tokens
+            generated_ids = whisper_model.generate(inputs["input_features"], max_new_tokens=20)
         transcription = whisper_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
         logger.info(f"Transcription (language: {language}): {transcription}")
         return transcription
@@ -675,7 +239,7 @@ async def get_chatbot_response(message, language="en", retries=2, timeout=10):
             logger.error(f"Chatbot response failed on attempt {attempt + 1}: {str(e)}")
             if attempt == retries:
                 return f"Error generating response: {str(e)}", None
-        await asyncio.sleep(1)  # Brief delay between retries
+        await asyncio.sleep(1)
     return "Error: Unable to generate response after retries.", None
 
 async def translate_text(text, target_language, retries=2, timeout=10):
@@ -708,7 +272,6 @@ async def analyze_symptoms(text, features, language="English"):
     suggestions = []
     text = text.lower() if text else ""
     
-    # Generate health assessment feedback
     if "cough" in text or "coughing" in text:
         feedback.append("You mentioned a cough, which may suggest a cold or respiratory issue.")
         suggestions.extend([
@@ -765,7 +328,6 @@ async def analyze_symptoms(text, features, language="English"):
             "• Stay hydrated and avoid strenuous activity."
         ])
     
-    # Voice feature-based feedback and suggestions
     if features["jitter"] > 6.5:
         feedback.append(f"High jitter ({features['jitter']:.2f}%) suggests vocal strain or respiratory issues.")
         suggestions.append("• Rest your voice and avoid shouting.")
@@ -812,7 +374,6 @@ async def analyze_symptoms(text, features, language="English"):
             "• Schedule regular health check-ups."
         ])
     
-    # Ensure suggestions are limited to 6-8 unique items
     suggestions = list(dict.fromkeys(suggestions))[:8]
     if len(suggestions) < 6:
         suggestions.extend([
@@ -820,7 +381,6 @@ async def analyze_symptoms(text, features, language="English"):
             "• Practice gratitude to boost mental well-being."
         ][:6 - len(suggestions)])
     
-    # Translate feedback and suggestions to the selected language
     feedback_text = "\n".join(feedback)
     suggestions_text = "\n".join(suggestions)
     try:
@@ -876,11 +436,11 @@ def store_user_consent(email, language):
 
 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"
+        feedback = feedback.replace('<', '&lt;').replace('>', '&gt;').replace('&', '&amp;')
+        transcription = transcription.replace('<', '&lt;').replace('>', '&gt;').replace('&', '&amp;') if transcription else "None"
+        suggestions = suggestions.replace('<', '&lt;').replace('>', '&gt;').replace('&', '&amp;') if suggestions else "None"
         email_to_use = email.strip() if email and email.strip() else DEFAULT_EMAIL
-        email = email_to_use.replace('<', '<').replace('>', '>').replace('&', '&')
+        email = email_to_use.replace('<', '&lt;').replace('>', '&gt;').replace('&', '&amp;')
         language_display = SALESFORCE_LANGUAGE_MAP.get(language, "English")
         
         ist = pytz.timezone('Asia/Kolkata')
@@ -935,7 +495,6 @@ def generate_pdf_report(feedback, transcription, features, language, email, sugg
             fontName='Times-Roman'
         )
         
-        # Translate PDF section titles to the selected language
         section_titles = {
             "English": {
                 "title": "MindCare Health Assistant Report",
@@ -1167,7 +726,6 @@ async def analyze_voice(audio_file=None, language="English", email=None):
         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."
 
-        # Translate the additional feedback details to the selected language
         details_to_translate = (
             f"Voice Analysis Details:\n"
             f"- Pitch: {features['pitch']:.2f} Hz\n"