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| title: PAM-UmiNur | |
| emoji: 🤖 | |
| colorFrom: pink | |
| colorTo: purple | |
| sdk: docker | |
| sdk_version: "1.0" | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # 🤖 PAM - Privacy-First AI Assistant | |
| **PAM** is your dual-personality AI assistant built for UmiNur's women's health ecosystem. She operates as both a warm, caring front-desk receptionist and a knowledgeable technical analyst. | |
| --- | |
| ## 💕 Meet the PAM Family | |
| ### Frontend PAM - Sweet Southern Receptionist | |
| - **Personality**: Warm, comforting, encouraging | |
| - **Voice**: Sweet southern charm with words of endearment (honey, boo, sugar, dear) | |
| - **Role**: Patient-facing conversational agent | |
| - **Handles**: Appointments, health inquiries, resource recommendations, general support | |
| ### Backend PAM - Nerdy Lab Assistant | |
| - **Personality**: Knowledgeable, enthusiastic, proactive | |
| - **Voice**: Encouraging tech colleague who loves finding patterns | |
| - **Role**: Technical infrastructure analyst | |
| - **Handles**: SIEM alerts, PHI detection, log analysis, compliance monitoring | |
| --- | |
| ## 🚀 Features | |
| ### Frontend Capabilities | |
| - ✅ **Appointment Management** - Schedule and manage patient appointments | |
| - ✅ **Health Resource Matching** - Provide relevant resources based on symptoms | |
| - ✅ **Emotional Support** - Detect distress and respond with empathy | |
| - ✅ **Emergency Detection** - Flag urgent situations and provide appropriate guidance | |
| - ✅ **Permission-Based Responses** - Respect content boundaries and escalate when needed | |
| ### Backend Capabilities | |
| - ✅ **PHI Detection** - Scan text for Protected Health Information | |
| - ✅ **Log Analysis** - Parse and classify system logs by severity | |
| - ✅ **Compliance Monitoring** - Track regulatory compliance status | |
| - ✅ **SIEM Integration** - Process security alerts and anomalies | |
| - ✅ **Proactive Insights** - Flag issues before they escalate | |
| --- | |
| ## 🏗️ Architecture | |
| ``` | |
| ┌─────────────────────────────────────────┐ | |
| │ FastAPI Service Layer │ | |
| │ (api_service.py - Port 7860) │ | |
| └───────────┬─────────────┬───────────────┘ | |
| │ │ | |
| ┌───────▼─────┐ ┌───▼──────────┐ | |
| │ Frontend PAM │ │ Backend PAM │ | |
| │ (Chat UI) │ │ (Technical) │ | |
| └──────────────┘ └──────────────┘ | |
| │ │ | |
| ┌──────▼─────────────────▼────────┐ | |
| │ HuggingFace Inference API │ | |
| │ (Mistral, BART, BERT models) │ | |
| └─────────────────────────────────┘ | |
| ``` | |
| --- | |
| ## 📡 API Endpoints | |
| ### Core Endpoints | |
| - **`GET /`** - Service information and navigation | |
| - **`GET /health`** - Health check for both agents | |
| - **`POST /ai/chat/`** - Frontend PAM (conversational) | |
| - **`POST /ai/technical/`** - Backend PAM (technical analysis) | |
| - **`POST /ai/unified/`** - Auto-routes based on intent | |
| ### Monitoring | |
| - **`GET /metrics`** - Service metrics | |
| - **`GET /docs`** - Interactive API documentation | |
| - **`GET /debug/test-agents`** - Agent testing (dev only) | |
| --- | |
| ## 🔧 Setup & Deployment | |
| ### Prerequisites | |
| - Python 3.10+ | |
| - HuggingFace account and API token | |
| - Docker (for containerized deployment) | |
| ### Environment Variables | |
| ```bash | |
| # Required | |
| HF_READ_TOKEN=your_huggingface_token_here | |
| # Optional | |
| PAM_HOST=0.0.0.0 | |
| PAM_PORT=7860 | |
| PAM_LOG_LEVEL=info | |
| ``` | |
| ### Local Development | |
| ```bash | |
| # Install dependencies | |
| pip install -r requirements.txt | |
| # Set your HF token | |
| export HF_READ_TOKEN="your_token_here" | |
| # Run the service | |
| python app.py | |
| ``` | |
| ### Docker Deployment | |
| ```bash | |
| # Build image | |
| docker build -t pam-assistant . | |
| # Run container | |
| docker run -p 7860:7860 \ | |
| -e HF_READ_TOKEN="your_token_here" \ | |
| pam-assistant | |
| ``` | |
| ### Hugging Face Spaces | |
| 1. Fork or create a new Space | |
| 2. Select "Docker" as SDK | |
| 3. Add `HF_READ_TOKEN` in Space settings (Settings → Repository secrets) | |
| 4. Push your code - auto-deployment will handle the rest! | |
| --- | |
| ## 📊 Data Files | |
| PAM requires JSON data files in the `data/` directory: | |
| - **`appointments.json`** - User appointment records | |
| - **`resources.json`** - Health resource library | |
| - **`follow_up.json`** - Follow-up tracking | |
| - **`permissions.json`** - Content permission rules | |
| - **`logs.json`** - System log entries | |
| - **`compliance.json`** - Compliance checklist | |
| --- | |
| ## 🎯 Usage Examples | |
| ### Frontend PAM (Chat) | |
| ```python | |
| # Request | |
| POST /ai/chat/ | |
| { | |
| "user_input": "Hey PAM, I'm having some cramping", | |
| "user_id": "user_001" | |
| } | |
| # Response | |
| { | |
| "reply": "Hey honey, I hear you. I've pulled together some helpful resources about what you're experiencing. Would you like me to also connect you with a nurse for a quick chat?", | |
| "intent": "health_symptoms_inquiry", | |
| "sentiment": {"label": "NEGATIVE", "score": 0.72}, | |
| "agent_type": "frontend", | |
| "personality": "sweet_southern_receptionist" | |
| } | |
| ``` | |
| ### Backend PAM (Technical) | |
| ```python | |
| # Request | |
| POST /ai/technical/ | |
| { | |
| "user_input": "check compliance" | |
| } | |
| # Response | |
| { | |
| "message": "🛡️ Great catch asking about this! Here's the compliance status:\n\n**Overall:** 4/5 checks passed (80.0%)\n\n**Action needed:** We have 1 items out of compliance:\n • Data Encryption\n\nQuick side note - I can help you prioritize these if you want to tackle them systematically!", | |
| "compliance_report": ["✅ Hipaa Compliant", "✅ Gdpr Ready", ...], | |
| "compliance_rate": 80.0, | |
| "agent_type": "backend", | |
| "personality": "nerdy_lab_assistant" | |
| } | |
| ``` | |
| --- | |
| ## 🛡️ Privacy & Security | |
| - **No persistent storage** of user conversations | |
| - **PHI detection** before logging or storage | |
| - **Permission-based content filtering** | |
| - **Encryption-ready** for production deployment | |
| - **HIPAA-aware** architecture | |
| --- | |
| ## 🤝 Contributing | |
| PAM is part of the UmiNur ecosystem. For contributions or questions: | |
| - Open an issue on GitHub | |
| - Review the code structure before proposing changes | |
| - Respect PAM's personality and voice guidelines | |
| --- | |
| ## 📝 License | |
| MIT License - See LICENSE file for details | |
| --- | |
| ## 🙏 Acknowledgments | |
| Built with: | |
| - **FastAPI** - Modern Python web framework | |
| - **HuggingFace** - Inference API and model hosting | |
| - **Transformers** - NLP model library | |
| - **Uvicorn** - ASGI server | |
| --- | |
| ## 📞 Support | |
| For technical support or questions about PAM: | |
| - 📧 Email: support@uminur.app | |
| - 🌐 Website: https://www.uminur.app | |
| - 📚 Docs: https://docs.uminur.app | |
| --- | |
| **Made with 💕 for women's health by the UmiNur team** |