| --- |
| title: Tell Me |
| emoji: "💬🌿" |
| colorFrom: indigo |
| colorTo: green |
| sdk: docker |
| pinned: false |
| tags: |
| - streamlit |
| short_description: Mental wellbeing chat (research) |
| --- |
| |
| **🌿 Tell Me — A Mental Well-Being Space** |
|
|
| Tell Me is a safe space for individuals seeking some well-being advice or a self-reflection space. It also provides the research community to simulate some LLM generated client-therapist synthetic data. This is a research prototype, not a medical device. |
|
|
| ## Key Components of Tell Me:*** |
| |
| - **Tell Me Assistant** |
| |
| Tell Me Assistant is a Mental Well-being Chatbot designed to help individuals process their thoughts and emotions in a supportive way. |
| It is not a substitute for professional care, but it offers a safe space for conversation and self-reflection. |
| The Assistant is created with care, recognizing that people may turn to it during moments of initial support. Its goal is to make such therapeutic-style interactions more accessible and approachable for everyone. |
|
|
| `fresh_app_v2.py` interconnected with `rag.py` and `llm_models.py` to power the Assistant with context using RAG |
| |
| - **Simulate a Conversation** |
| This generates a synthetic client–therapist conversation from a short client profile. It helps create sample data for research and lets professionals inspect the dialogue quality. Outputs are created by an LLM and can guide future fine-tuning or evaluation. |
| Multi‑turn, role‑locked *Therapist ↔ Client* dialogue from a brief persona (see `llm_models.py`). |
|
|
| - **Well‑being Planner (CrewAI)** |
| 1) Transcript analysis (themes, emotions, triggers) |
| 2) **7‑day plan** (CBT/behavioral steps, routines, sleep hygiene, social micro‑actions) |
| 3) **Guided meditation** script + **MP3** (gTTS/Edge/Coqui/ElevenLabs) |
| Implemented in `crew_ai.py`, surfaced in the **Planner** tab in `fresh_app_v2.py`. |
|
|
|
|
| - **Evaluation** |
| Use `prep_responses.py` and `judge.py` to prepare and score generations using LLM as a judge and also the results of conducted Human Evaluation; see `Results/` for artifacts (e.g., *gpt4o/5 eval*). |
|
|
| --- |
|
|
| ## Repository Structure |
|
|
| ``` |
| . |
| ├─ Results/ # Evaluation outputs / artifacts (e.g., gpt4o eval) |
| ├─ index_storage/ # Vector index built by rag.py |
| ├─ rag_data/ # Source docs for RAG |
| ├─ src/ # Streamlit template seed |
| ├─ bg.jpg # App background |
| ├─ config.toml # Streamlit config (dark mode default, etc.) |
| ├─ crew_ai.py # CrewAI pipeline (planner + meditation TTS) |
| ├─ fresh_app_v2.py # Main Streamlit app |
| ├─ judge.py # Evaluation judge |
| ├─ llm_models.py # Prompt builders + simulate‑conversation helpers |
| ├─ prep_responses.py # Prep helper for evaluation |
| ├─ rag.py # Simple RAG indexing/query helpers |
| ├─ requirements.txt # Python dependencies |
| ├─ Dockerfile # Optional container build |
| ├─ .gitattributes |
| └─ README.md # You are here :) |
| ``` |
|
|
| --- |
|
|
| ## Quickstart |
|
|
| ### 1) Python setup |
|
|
| ```bash |
| # Python 3.10+ recommended |
| python -m venv .venv |
| source .venv/bin/activate # Windows: .venv\Scripts\activate |
| pip install -r requirements.txt |
| ``` |
|
|
| ### 2) Environment variables |
|
|
| Create a `.env` in the project root (same folder as `fresh_app_v2.py`). Minimal example: |
|
|
| ```dotenv |
| |
| # Optional TTS configuration for the guided meditation |
| # TTS_PROVIDER=gtts # or: edge | coqui | elevenlabs |
| # ELEVEN_API_KEY=... # if using ElevenLabs |
| # EDGE_VOICE=en-US-JennyNeural # if using edge-tts |
| # COQUI_MODEL=tts_models/multilingual/multi-dataset/xtts_v2 |
| ``` |
|
|
| > Some tabs may allow choosing models/keys in the UI. |
| > The **Planner** currently works with the key above (and/or an in‑tab field if present in your build). |
|
|
| ### 3) Run the app |
|
|
| ```bash |
| streamlit run fresh_app_v2.py |
| ``` |
|
|
| Open the URL Streamlit prints (usually http://localhost:8501). |
|
|
| --- |
|
|
| ## Using the App |
|
|
| ### UI View Recommendation |
|
|
| Note: On the streamlit app please ensure that you have selected the Dark Mode in settings to get the best UI Experience of the App |
|
|
| ### Simulate a Conversation 🧪🤖 |
| 1. In that tab, paste a **Client Profile** (e.g., `Age 24 student; recently moved... sleep irregular...`). |
| 2. Click **Generate Synthetic Dialogue** to produce a multi‑turn conversation. |
| 3. Optionally **Download Transcript**. |
|
|
| ### Well‑being Planner 📅🧘 |
| 1. Ensure you provide open ai api key for running this module (i.e paste a key in the tab if the field is available). |
| 2. Upload one **.txt** transcript (client–therapist chat). |
| 3. Click **Create Plan & Meditation**. |
| 4. The app displays: |
| - **Transcript Summary** |
| - **7‑Day Well‑being Plan** (Markdown, Day 1 … Day 7) |
| - **Meditation Script** and an **MP3** player |
| (audio saved locally as `guided_meditation.mp3`) |
| |
| ### RAG (optional) |
| - Place your files into `rag_data/`. |
| - Build/update the index (if needed): |
|
|
| ```bash |
| python rag.py |
| ``` |
|
|
| - Use the app’s RAG controls to query your docs (index artifacts stored in `index_storage/`). |
|
|
| ### Evaluation (optional) |
| - Use `prep_responses.py` to format generations and `judge.py` to score them. |
| - Outputs/examples are kept under `Results/`. |
|
|
| --- |
|
|
| ## Streamlit Configuration |
|
|
| - `config.toml` sets app defaults (e.g., dark mode). Example: |
|
|
| ```toml |
| [theme] |
| base = "dark" |
| ``` |
|
|
| Adjust as needed per Streamlit docs. |
|
|
| --- |
|
|
| ## Docker (optional) |
|
|
| ```bash |
| # Build |
| docker build -t tellme-assistant . |
| |
| # Run (exposes Streamlit on 8501) |
| docker run --rm -p 8501:8501 --env-file .env tellme-assistant |
| ``` |
|
|
| --- |
|
|
| ## Troubleshooting |
|
|
| - **AuthenticationError / “You didn’t provide an API key.”** |
| Ensure `.env` includes `open_ai_key_for_crew_ai=sk-...` (or provide the key in‑tab if available) and **restart** Streamlit so the new env is loaded. |
|
|
| - **Only meditation shows but not the plan** |
| Update to the latest `crew_ai.py` that collects and returns **summary**, **plan**, and **meditation**, and ensure the tab renders all three fields. |
|
|
| - **TTS provider errors** |
| Install the provider’s dependency (`pip install edge-tts`, `pip install TTS`, `pip install elevenlabs`) and set the related env vars. |
|
|
| - **Ollama (if used in other tabs)** |
| Start the daemon and pull a model: |
| ```bash |
| ollama serve |
| ollama pull llama3.1:8b-instruct |
| ``` |
|
|
| --- |
|
|
| ## Tech Stack |
|
|
| - **UI:** Streamlit |
| - **LLMs:** OpenAI (planner), plus optional Anthropic/Ollama in other tabs |
| - **Agents:** CrewAI (via LiteLLM under the hood) |
| - **RAG:** Simple local index (`rag.py`, `index_storage/`) |
| - **TTS:** gTTS / Edge‑TTS / Coqui TTS / ElevenLabs (configurable) |
| # |
| --- |
|
|
| ## Roadmap |
|
|
| - In‑tab API key entry for the CrewAI planner (UI‑first flow) |
| - Configurable model/provider for planner |
| - Save generated plans/MP3s into `Results/` with timestamped filenames |
|
|
| --- |
|
|
| ## License |
|
|
| MIT |
|
|
| --- |
|
|
| ## Acknowledgments |
|
|
| - Streamlit template seed |
| - CrewAI & LiteLLM ecosystem |
| - TTS libraries: gTTS, Edge‑TTS, Coqui TTS, ElevenLabs |
|
|
| ## Acknowledgment of AI Assistance |
| Some parts of this project code was generated or refined with the assistance of GPT-5. |
| All outputs were reviewed, tested, and integrated by the authors. |
|
|
|
|
