title	InnerVoice
emoji	🗣️
colorFrom	indigo
colorTo	purple
sdk	docker
app_port	7860

InnerVoice

AI-driven Emotional Wellness Tracker & Proactive Support Network

---

🚀 The Problem

Problem Statement inspired by the Smart India Hackathon framework:

Mental health decline is often a silent trajectory. When individuals experience prolonged periods of stress or depression, their primary support networks—therapists, partners, and close friends—are completely disconnected from their day-to-day emotional state, forcing interventions to be reactive (during a crisis) rather than proactive.

Furthermore, traditional written journaling causes high cognitive friction resulting in low adherence, and standard mood-tracking apps rely on subjective 1-to-10 sliders that fail to capture subconscious emotional exhaustion.

💡 The Solution

InnerVoice is a full-stack platform that completely removes the friction of self-reflection. Users simply speak their mind for 60 seconds into their device.

Instead of just parsing text, InnerVoice listens. It utilizes advanced, localized ML models (wav2vec2) to extract acoustic features (pitch, speech rate, energetic variance, conversational pauses) to uncover latent emotions.

Additionally, InnerVoice breaks the isolation of mental health struggles through its Trusted Circle architecture. The platform automatically broadcasts Weekly Emotional Trend Reports (synthesized by an LLM) to a pre-authenticated support system to facilitate early human intervention.

---

🔥 Key Features

1. Zero-Friction Voice Journaling

   • Client-side recording with real-time waveform visualization.
   • Immediate audio-to-text transcription powered by OpenAI Whisper.

2. Acoustic Acoustic Analysis

   • librosa extracts physiological distress markers: pitch deviation, energy (RMS), speech rate, and zero-crossing rate (pauses/filler words).
   • Emotion classification via localized HuggingFace Models (wav2vec2-lg-xlsr-en-speech-emotion-recognition).

3. Trusted Circle Architecture (Brevo Integration)

   • Allow users to invite therapists or partners to their secure network.
   • Proactive broadcasting of LLM-synthesized Weekly Trend Reports directly to via automatic Transactional Emails using the Brevo API.
   • Granular CRUD management of trusted members.

4. Multi-Factor Correlation & Contextual Engagement

   • Sleep/Mood Correlation: Visual and statistical mapping linking sleep deprivation securely to mood degradation.
   • Contextual Prompts: AI reads your previous 5 entries to generate highly personalized daily journaling prompts specifically tailored to your emotional state.
   • Advanced Theming: Fully responsive Dark/Light mode architecture.
   • Crisis Safety Net: Local and international resources triggered automatically.

---

🛠️ Tech Stack

• Frontend: Next.js 14, React context, TailwindCSS, Recharts, Framer Motion
• Backend: FastAPI, SQLAlchemy, SQLite/PostgreSQL
• Communications: Brevo Transactional Email REST API via httpx
• AI/ML: wav2vec2, librosa, OpenRouter / OpenAI for generation

---

⚙️ Quick Start (Local Deployment)

1. Backend Setup

The backend requires Python 3.10+ and downloads ~1.5GB of ML models on first run.

cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Configuration: Rename .env.example to .env and configure your API keys:

OPENROUTER_API_KEY="your_ai_key"
BREVO_API_KEY="your_brevo_api_key"
BREVO_SENDER_EMAIL="your_brevo_verified_sender@email.com"

Run Server:

python -m uvicorn main:app --reload

API runs locally at http://localhost:8000

2. Frontend Setup

cd frontend
npm install

# Start the dev server 
npm run dev

Visit http://localhost:3000 to interact with InnerVoice!

3. Database Seeding (Testing Mode)

If you want to test the multi-week timeline and Trusted Circle functionality without recording 30 days of real audio, you can seed the SQLite database with rich mock entries:

cd backend
source venv/bin/activate
python seed_data.py

This script generates a user ID. Add that ID to your frontend/.env.local as NEXT_PUBLIC_DEMO_USER_ID or simply browse in the generic UI Demo Mode.

---

🐳 Deployment (Hugging Face Spaces)

This project includes a unified Dockerfile mapped to a single port specifically designed to be highly compatible with Hugging Face Spaces.

To Deploy on Hugging Face:

1. Create a new Docker Space on Hugging Face.
2. Link it to your GitHub repository OR upload these files directly.
3. In the Space Settings, map your Secrets (Environment Variables):
   • OPENROUTER_API_KEY
   • BREVO_API_KEY
   • BREVO_SENDER_EMAIL
4. The Space will automatically build the React static files, install the Python ML stack, and boot both servers behind a unified endpoint!

---

🔒 Privacy & Architecture

Audio files are converted in-memory within the backend ML pipeline and are deleted securely and immediately after the acoustic feature-extraction completes. We only permanently store quantified acoustic metrics (floats), the speech transcription string, and the categorical detected emotion.