How vishwaksen21/cure-blend works
CureBlend is an educational health assistant with web and CLI entry points. Its main workflow takes symptom descriptions, produces disease predictions, then presents herbal and pharmaceutical options alongside safety guidance, severity information, and optional AI insights. The graph also includes dataset and medical-knowledge components and the documented graph-embedding capability. Several source excerpts are partial, so uncertain internal calls and data wiring are omitted; feature coverage beyond the sampled implementations follows the README.
Main entry points: ai_assistant.py Health assistant · main.py CLI interface · personalized_recommender.py Personalized recommendations · streamlit_app.py Web interface
Components 20
User experience
- Web interface
streamlit_app.py - CLI interface
main.py
Analysis and guidance
- Health assistant
src/ai_assistant.py - Symptom prediction
src/symptom_predictor.py - Multi-disease analysis
src/multi_disease_detector.py - Severity assessment
src/severity_classifier.py - Safety checks
src/safety_checks.py - Herbal safety
src/herbal_safety_checker.py - Personalized recommendations
src/personalized_recommender.py - Model explanations
src/explainability.py - AI insights
src/ai_assistant.py - User feedback
src/feedback_system.py
Medical knowledge
- Drug recommendations
src/drug_database.py - Medical knowledge store
src/database_manager.py
Data and modeling
- Data orchestration
src/data_orchestrator.py - Dataset management
src/dataset_manager.py - Medical knowledge graph
src/build_graph.py - Graph embeddings
src/embeddings.py
Other components
- Person
- AI model service
Connections 16
- Person → Web interface: enters symptoms
- Person → CLI interface: enters symptoms
- Web interface → Health assistant: requests analysis
- CLI interface → Health assistant: requests analysis
- CLI interface → Multi-disease analysis: analyzes overlap
- Health assistant → Symptom prediction: predicts disease
- Health assistant → Personalized recommendations: gets recommendations
- Health assistant → Safety checks: checks safety
- Health assistant → Severity assessment: assesses severity
- Health assistant → AI insights: generates insights
- AI insights → AI model service: requests generation
- Personalized recommendations → Drug recommendations: looks up drugs
- Personalized recommendations → Herbal safety: checks herbal safety
- Health assistant → Medical knowledge store: reads medical data
- Data orchestration → Dataset management: reads datasets
- Medical knowledge graph → Graph embeddings: generates vectors