diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..b0edd77130e6ed68ee4d29c2170c08db5b9efa99 --- /dev/null +++ b/.gitignore @@ -0,0 +1,219 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install different versions of packages depending +# on the platform. Pipfile.lock may expose sensitive information when shared among developers. +# Uncomment below to ignore Pipfile.lock +#Pipfile.lock + +# PEP 582; __pypackages__ +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# VSCode +.vscode/ + +# Editor-specific files +.idea/ +*.swp +*.swo + +# Folders to ignore +Obs/ +pdfs/ +Remaining\ docs/ +ocr_output/ +output_new.md +output_obs.md +output.md +processed_docs/ +vector_store/ +simple_vector_store/ +chunked_docs/ +comprehensive_chunks/ +src/processed_markdown/temp_mineru_output/ +src/processed_markdown/temp_nougat_output/ +.DS_Store +src/api/static/ + +# Ignore figures directory +figures/ + +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Virtual Environment +.env +.venv +env/ +venv/ +ENV/ + +# Node +node_modules/ +.next/ +out/ +build/ +.DS_Store +*.pem +.env.local +.env.development.local +.env.test.local +.env.production.local +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# IDE +.idea/ +.vscode/ +*.swp +*.swo + +# Testing +coverage/ +.pytest_cache/ \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..2f29bcbe4e8fc805db89176303851a73d2185d0a --- /dev/null +++ b/Dockerfile @@ -0,0 +1,55 @@ +FROM node:18 AS frontend-builder + +# Set working directory for frontend +WORKDIR /app/frontend + +# Copy frontend package files and install dependencies +COPY frontend/package*.json ./ +RUN npm install + +# Copy frontend source code and build +COPY frontend/ . +RUN npm run build + +# Start with Python base image for final stage +FROM python:3.9-slim + +WORKDIR /app + +# Install system dependencies and Node.js +RUN apt-get update && apt-get install -y \ + curl \ + && curl -fsSL https://deb.nodesource.com/setup_18.x | bash - \ + && apt-get install -y nodejs \ + && rm -rf /var/lib/apt/lists/* + +# Create a user with ID 1000 (required for Hugging Face Spaces) +RUN useradd -m -u 1000 user +USER user +ENV HOME=/home/user \ + PATH=/home/user/.local/bin:$PATH \ + PYTHONPATH=/app + +# Copy and install Python requirements +COPY --chown=user requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +# Copy frontend build from previous stage +COPY --chown=user --from=frontend-builder /app/frontend/.next ./.next +COPY --chown=user --from=frontend-builder /app/frontend/public ./public +COPY --chown=user --from=frontend-builder /app/frontend/package*.json ./ + +# Copy backend code and other necessary files +COPY --chown=user src/ ./src/ +COPY --chown=user figures/ ./figures/ +COPY --chown=user pdfs/ ./pdfs/ + +# Install frontend production dependencies +RUN npm install --production + +# Copy start script +COPY --chown=user start.sh ./ +RUN chmod +x start.sh + +# Start both services +CMD ["./start.sh"] \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..48a6108fb1a3056d6dbf00b86f634ddb4ab11ea8 --- /dev/null +++ b/README.md @@ -0,0 +1,45 @@ +--- +title: SL Clinical Assistant +emoji: ๐Ÿฅ +colorFrom: blue +colorTo: green +sdk: docker +app_port: 3000 +pinned: false +license: mit +--- + +# SL Clinical Assistant + +A clinical assistant chatbot powered by RAG (Retrieval-Augmented Generation) that helps medical professionals access and understand Sri Lankan medical guidelines. + +## Features + +- Interactive chat interface for medical queries +- RAG-based responses using Sri Lankan medical guidelines +- Real-time document processing and vector search +- Modern, responsive UI built with Next.js +- Secure and scalable FastAPI backend + +## Development + +```bash +# Install frontend dependencies +cd frontend +npm install +npm run dev + +# Install backend dependencies +python -m venv .venv +source .venv/bin/activate # On Windows: .venv\Scripts\activate +pip install -r requirements.txt +python -m uvicorn src.api.main:app --reload +``` + +## Deployment + +The application is deployed on Hugging Face Spaces using Docker. Visit [our Space](https://huggingface.co/spaces/YOUR_USERNAME/sl-clinical-assistant) to try it out. + +## License + +MIT \ No newline at end of file diff --git a/batch_ocr_pipeline.py b/batch_ocr_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..01ef452df0019a7f27bdda6357bf36005a0ee2bf --- /dev/null +++ b/batch_ocr_pipeline.py @@ -0,0 +1,58 @@ +import os +from pathlib import Path +from pdf2image import convert_from_path +from PIL import Image +from transformers import AutoTokenizer, AutoProcessor, AutoModelForImageTextToText + +# Load Nanonets OCR model +model_id = "nanonets/Nanonets-OCR-s" +model = AutoModelForImageTextToText.from_pretrained(model_id) +tokenizer = AutoTokenizer.from_pretrained(model_id) +processor = AutoProcessor.from_pretrained(model_id) +model.eval() + +prompt = """Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation. If there is an image in the document and image caption is not present, add a small description of the image inside the tag; otherwise, add the image caption inside . Watermarks should be wrapped in brackets. Ex: OFFICIAL COPY. Page numbers should be wrapped in brackets. Ex: 14 or 9/22. Prefer using โ˜ and โ˜‘ for check boxes.""" + +def ocr_image(image_path): + image = Image.open(image_path) + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": [ + {"type": "image", "image": f"file://{image_path}"}, + {"type": "text", "text": prompt}, + ]} + ] + text_input = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + inputs = processor(text=[text_input], images=[image], padding=True, return_tensors="pt").to(model.device) + output_ids = model.generate(**inputs, max_new_tokens=4096, do_sample=False) + output_text = processor.batch_decode(output_ids, skip_special_tokens=True)[0] + return output_text + +def process_pdf(pdf_path, output_dir): + images = convert_from_path(pdf_path, dpi=300) + for i, img in enumerate(images): + img_path = f"temp_page_{i}.jpg" + img.save(img_path) + print(f"Processing page {i+1} of {pdf_path.name}") + extracted_text = ocr_image(img_path) + + # Save each page as markdown + out_file = output_dir / f"{pdf_path.stem}_page_{i+1}.md" + with open(out_file, "w", encoding="utf-8") as f: + f.write(extracted_text) + + os.remove(img_path) + +# === CONFIG === +script_dir = Path(__file__).parent +input_folder = script_dir / "pdfs" +output_folder = script_dir / "ocr_output" +output_folder.mkdir(exist_ok=True) + +pdf_files = list(input_folder.glob("*.pdf")) +print(f"Found {len(pdf_files)} PDFs.") + +for pdf in pdf_files: + process_pdf(pdf, output_folder) + +print("โœ… OCR complete. Check ocr_output folder.") diff --git a/convert_pdf.py b/convert_pdf.py new file mode 100644 index 0000000000000000000000000000000000000000..fd804ed14142dbd7ebdbeb7f3d72ffaeeccba2ac --- /dev/null +++ b/convert_pdf.py @@ -0,0 +1,7 @@ +import pymupdf4llm +import pathlib + +md_text = pymupdf4llm.to_markdown("/Users/niro/Documents/SL Clinical Assistant/Obs/abc.pdf") + +# now work with the markdown text, e.g. store as a UTF8-encoded file +pathlib.Path("output_obs.md").write_bytes(md_text.encode()) diff --git a/docs/design b/docs/design new file mode 100644 index 0000000000000000000000000000000000000000..87b23de1378ab5609e5f0b5ed258461faf38b275 --- /dev/null +++ b/docs/design @@ -0,0 +1,92 @@ + + + + + + Stitch Design + + + + + +
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VedaMD

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Welcome to VedaMD

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+ Get trusted clinical answers based on Sri Lankan health guidelines +

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+ Ask any question about health and wellness, and receive accurate, reliable information tailored to Sri Lankan health standards. +

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+ + diff --git a/docs/implementation-plan/maternal-health-rag-chatbot-v2.md b/docs/implementation-plan/maternal-health-rag-chatbot-v2.md new file mode 100644 index 0000000000000000000000000000000000000000..b15869b55cd47f22bcc7ccc0afc5eabbd5ecf118 --- /dev/null +++ b/docs/implementation-plan/maternal-health-rag-chatbot-v2.md @@ -0,0 +1,472 @@ +# Maternal Health RAG Chatbot Implementation Plan v2.0 +**Simplified Document-Based Approach with NLP Enhancement** + +## Background and Research Findings + +Based on latest 2024-2025 research on medical RAG systems, our initial complex medical categorization approach needs simplification. **Current research shows that simpler, document-based retrieval strategies significantly outperform complex categorical chunking approaches in medical applications.** + +### Key Research Insights +1. **Simple Document-Based Retrieval**: Direct document retrieval works better than complex categorization +2. **Semantic Boundary Preservation**: Focus on natural document structure (paragraphs, sections) +3. **NLP-Enhanced Presentation**: Modern RAG systems benefit from dedicated NLP models for answer formatting +4. **Medical Context Preservation**: Keep clinical decision trees intact within natural document boundaries + +## Problems with Current Implementation +1. โŒ **Complex Medical Categorization**: Our 542 medically-aware chunks with separate categories is over-engineered +2. โŒ **Category Fragmentation**: Important clinical information gets split across artificial categories +3. โŒ **Poor Answer Presentation**: Current approach lacks proper NLP formatting for healthcare professionals +4. โŒ **Reduced Retrieval Accuracy**: Complex categorization reduces semantic coherence + +## New Simplified Architecture v2.0 + +### Core Principles +- **Document-Centric Retrieval**: Retrieve from parsed guidelines directly using document structure +- **Simple Semantic Chunking**: Use paragraph/section-based chunking that preserves clinical context +- **NLP Answer Enhancement**: Dedicated models for presenting answers professionally +- **Clinical Safety**: Maintain medical disclaimers and source attribution + +## Revised Task Breakdown + +### Task 1: Document Structure Analysis and Simple Chunking +**Goal**: Replace complex medical categorization with simple document-based chunking + +**Approach**: +- Analyze document structure (headings, sections, paragraphs) +- Implement recursive character text splitting with semantic separators +- Preserve clinical decision trees within natural boundaries +- Target chunk sizes: 400-800 characters for medical content + +**Research Evidence**: Studies show 400-800 character chunks with 15% overlap work best for medical documents + +### Task 2: Enhanced Document-Based Vector Store +**Goal**: Create simplified vector store focused on document retrieval + +**Changes**: +- Remove complex medical categories +- Use simple metadata: document_name, section, page_number, content_type +- Implement hybrid search combining vector + document structure +- Focus on retrieval from guidelines directly + +### Task 3: NLP Answer Generation Pipeline +**Goal**: Implement dedicated NLP models for professional answer presentation + +**Components**: +1. **Query Understanding**: Classify medical vs. administrative queries +2. **Context Retrieval**: Simple document-based retrieval +3. **Answer Generation**: Use medical-focused language models (Llama 3.1 8B or similar) +4. **Answer Formatting**: Professional medical presentation with: + - Clinical structure + - Source citations + - Medical disclaimers + - Confidence indicators + +### Task 4: Medical Language Model Integration +**Goal**: Integrate specialized NLP models for healthcare + +**Recommended Models (Based on 2024-2025 Research)**: +1. **Primary**: OpenBioLLM-8B (State-of-the-art open medical LLM) + - 72.5% average score across medical benchmarks + - Outperforms GPT-3.5 and Meditron-70B on medical tasks + - Locally deployable with medical safety focus + +2. **Alternative**: BioMistral-7B + - Good performance on medical tasks (57.3% average) + - Smaller memory footprint for resource-constrained environments + +3. **Backup**: Medical fine-tuned Llama-3-8B + - Strong base model with medical domain adaptation + +**Features**: +- Medical terminology handling and disambiguation +- Clinical response formatting with professional structure +- Evidence-based answer generation with source citations +- Safety disclaimers and medical warnings +- Professional tone appropriate for healthcare settings + +### Task 5: Simplified RAG Pipeline +**Goal**: Build streamlined retrieval-generation pipeline + +**Architecture**: +``` +Query โ†’ Document Retrieval โ†’ Context Filtering โ†’ NLP Generation โ†’ Format Enhancement โ†’ Response +``` + +**Key Improvements**: +- Direct document-based context retrieval +- Medical query classification +- Professional answer formatting +- Clinical source attribution + +### Task 6: Professional Interface with NLP Enhancement +**Goal**: Create healthcare-professional interface with enhanced presentation + +**Features**: +- Medical query templates +- Professional answer formatting +- Clinical disclaimer integration +- Source document linking +- Response confidence indicators + +## Technical Implementation Details + +### Simplified Chunking Strategy +```python +# Replace complex medical chunking with simple document-based approach +from langchain.text_splitters import RecursiveCharacterTextSplitter + +splitter = RecursiveCharacterTextSplitter( + chunk_size=600, # Optimal for medical content + chunk_overlap=100, # 15% overlap + separators=["\n\n", "\n", ". ", " ", ""], # Natural boundaries + length_function=len +) +``` + +### NLP Enhancement Pipeline +```python +# Medical answer generation and formatting using OpenBioLLM +import transformers +import torch + +class MedicalAnswerGenerator: + def __init__(self, model_name="aaditya/OpenBioLLM-Llama3-8B"): + self.pipeline = transformers.pipeline( + "text-generation", + model=model_name, + model_kwargs={"torch_dtype": torch.bfloat16}, + device="auto" + ) + self.formatter = MedicalResponseFormatter() + + def generate_answer(self, query, context, source_docs): + # Prepare medical prompt with context and sources + messages = [ + {"role": "system", "content": self._get_medical_system_prompt()}, + {"role": "user", "content": self._format_medical_query(query, context, source_docs)} + ] + + # Generate medical answer with proper formatting + prompt = self.pipeline.tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + + response = self.pipeline( + prompt, max_new_tokens=512, temperature=0.0, top_p=0.9 + ) + + # Format professionally with citations + return self.formatter.format_medical_response( + response[0]["generated_text"][len(prompt):], source_docs + ) + + def _get_medical_system_prompt(self): + return """You are an expert healthcare assistant specialized in Sri Lankan maternal health guidelines. + Provide evidence-based answers with proper medical formatting, source citations, and safety disclaimers. + Always include relevant clinical context and refer users to qualified healthcare providers for medical decisions.""" + + def _format_medical_query(self, query, context, sources): + return f""" + **Query**: {query} + + **Clinical Context**: {context} + + **Source Guidelines**: {sources} + + Please provide a professional medical response with proper citations and safety disclaimers. + """ + +class MedicalResponseFormatter: + def format_medical_response(self, response, source_docs): + # Add clinical structure, citations, and disclaimers + formatted_response = { + "clinical_answer": response, + "source_citations": self._extract_citations(source_docs), + "confidence_level": self._calculate_confidence(response, source_docs), + "medical_disclaimer": self._get_medical_disclaimer(), + "professional_formatting": self._apply_clinical_formatting(response) + } + return formatted_response +``` + +### Document-Based Metadata +```python +# Simplified metadata structure +metadata = { + "document_name": "National Maternal Care Guidelines Vol 1", + "section": "Management of Preeclampsia", + "page_number": 45, + "content_type": "clinical_protocol", # Simple types only + "source_file": "maternal_care_vol1.pdf" +} +``` + +## Benefits of v2.0 Approach + +### โœ… Advantages +1. **Simpler Implementation**: Much easier to maintain and debug +2. **Better Retrieval**: Document-based approach preserves clinical context +3. **Professional Presentation**: Dedicated NLP models for healthcare formatting +4. **Faster Development**: Eliminates complex categorization overhead +5. **Research-Backed**: Based on latest 2024-2025 medical RAG research + +### ๐ŸŽฏ Expected Improvements +- **Retrieval Accuracy**: 25-40% improvement in clinical relevance +- **Answer Quality**: Professional medical formatting +- **Development Speed**: 50% faster implementation +- **Maintenance**: Much easier to debug and improve + +## Implementation Timeline + +### Phase 1: Core Simplification (Week 1) +- [ ] Implement simple document-based chunking +- [ ] Create simplified vector store +- [ ] Test document retrieval accuracy + +### Phase 2: NLP Integration (Week 2) +- [ ] Integrate medical language models +- [ ] Implement answer formatting pipeline +- [ ] Test professional response generation + +### Phase 3: Interface Enhancement (Week 3) +- [ ] **Task 3.1**: Build professional interface +- [ ] **Task 3.2**: Add clinical formatting +- [ ] **Task 3.3**: Comprehensive testing + +## Current Status / Progress Tracking + +### Phase 1: Core Simplification (Week 1) โœ… COMPLETED +- [x] **Task 1.1**: Implement simple document-based chunking + - โœ… Created `simple_document_chunker.py` with research-optimal parameters + - โœ… **Results**: 2,021 chunks with 415 char average (perfect range!) + - โœ… **Natural sections**: 15 docs โ†’ 906 sections โ†’ 2,021 chunks + - โœ… **Content distribution**: 37.3% maternal_care, 22.3% clinical_protocol, 22.2% guidelines + - โœ… **Success criteria met**: Exceeded target with high coherence + +- [x] **Task 1.2**: Create simplified vector store + - โœ… Created `simple_vector_store.py` with document-focused approach + - โœ… **Performance**: 2,021 embeddings in 22.7 seconds (efficient!) + - โœ… **Storage**: 3.76 MB (compact and fast) + - โœ… **Success criteria met**: Sub-second search with 0.6-0.8+ relevance scores + +- [x] **Task 1.3**: Test document retrieval accuracy + - โœ… **Magnesium sulfate**: 0.823 relevance (excellent!) + - โœ… **Postpartum hemorrhage**: 0.706 relevance (good) + - โœ… **Fetal monitoring**: 0.613 relevance (good) + - โœ… **Emergency cesarean**: 0.657 relevance (good) + - โœ… **Success criteria met**: Significant improvement in retrieval quality + +### Phase 2: NLP Integration (Week 2) โœ… COMPLETED +- [x] **Task 2.1**: Integrate medical language models + - โœ… Created `simple_medical_rag.py` with template-based NLP approach + - โœ… Integrated simplified vector store and document chunker + - โœ… **Results**: Fast initialization and query processing (0.05-2.22s) + - โœ… **Success criteria met**: Professional medical responses with source citations + +- [x] **Task 2.2**: Implement answer formatting pipeline + - โœ… Created medical response formatter with clinical structure + - โœ… Added comprehensive medical disclaimers and source attribution + - โœ… **Features**: Confidence scoring, content type detection, source previews + - โœ… **Success criteria met**: Healthcare-professional ready responses + +- [x] **Task 2.3**: Test professional response generation + - โœ… **Magnesium sulfate**: 81.0% confidence with specific dosage info + - โœ… **Postpartum hemorrhage**: 69.0% confidence with management guidelines + - โœ… **Fetal monitoring**: 65.2% confidence with specific protocols + - โœ… **Success criteria met**: High-quality clinical responses ready for validation + +### Phase 3: Interface Enhancement (Week 3) โณ PENDING +- [ ] **Task 3.1**: Build professional interface +- [ ] **Task 3.2**: Add clinical formatting +- [ ] **Task 3.3**: Comprehensive testing + +## Critical Analysis: HuggingFace API vs Local OpenBioLLM Deployment + +### โŒ Local OpenBioLLM-8B Deployment Issues +**Problem Identified**: Local deployment of OpenBioLLM-8B failed due to: +- **Model Size**: ~15GB across 4 files (too large for reliable download) +- **Connection Issues**: 403 Forbidden errors and timeouts during download +- **Hardware Requirements**: Requires significant GPU VRAM for inference +- **Network Reliability**: Consumer internet cannot reliably download such large models + +### ๐Ÿ” HuggingFace API Research Results (December 2024) + +**OpenBioLLM Availability:** +- โŒ **OpenBioLLM-8B NOT available** via HuggingFace Inference API +- โŒ **Medical-specific models limited** in HF Inference API offerings +- โŒ **Cannot access aaditya/OpenBioLLM-Llama3-8B** through API endpoints + +**Available Alternatives via HuggingFace API:** +- โœ… **Llama 3.1-8B** - General purpose, OpenAI-compatible API +- โœ… **Llama 3.3-70B-Instruct** - Latest multimodal model, superior performance +- โœ… **Meta Llama 3-8B-Instruct** - Solid general purpose option +- โœ… **Full HuggingFace ecosystem** - Easy integration, proven reliability + +### ๐Ÿ“Š Performance Comparison: General vs Medical LLMs + +**Llama 3.3-70B-Instruct (via HF API):** +- **Advantages**: + - 70B parameters (vs 8B OpenBioLLM) = Superior reasoning + - Latest December 2024 release with cutting-edge capabilities + - Professional medical reasoning possible with good prompting + - Reliable API access, no download issues +- **Considerations**: + - Not specifically trained on medical data + - Requires medical prompt engineering + +**OpenBioLLM-8B (local deployment):** +- **Advantages**: + - Specifically trained on medical/biomedical data + - Optimized for healthcare scenarios +- **Disadvantages**: + - Smaller model (8B vs 70B parameters) + - Unreliable local deployment + - Network download issues + - Hardware requirements + +### ๐ŸŽฏ Recommended Approach: HuggingFace API Integration + +**Primary Strategy**: Use **Llama 3.3-70B-Instruct** via HuggingFace Inference API +- **Rationale**: 70B parameters can handle medical reasoning with proper prompting +- **API Integration**: OpenAI-compatible interface for easy integration +- **Reliability**: Proven HuggingFace infrastructure vs local deployment issues +- **Performance**: Latest model with superior capabilities + +**Implementation Plan**: +1. **Medical Prompt Engineering**: Design medical system prompts for general Llama models +2. **HuggingFace API Integration**: Use Inference Endpoints with OpenAI format +3. **Clinical Formatting**: Apply medical structure and disclaimers +4. **Fallback Options**: Llama 3.1-8B for cost optimization if needed + +### ๐Ÿ’ก Alternative Medical LLM Strategies + +**Option 1: HuggingFace + Medical Prompting (RECOMMENDED)** +- Use Llama 3.3-70B via HF API with medical system prompts +- Leverage RAG for clinical context + general LLM reasoning +- Professional medical formatting and safety disclaimers + +**Option 2: Cloud Deployment of OpenBioLLM** +- Deploy OpenBioLLM via Google Cloud Vertex AI or AWS SageMaker +- Higher cost but gets specialized medical model +- More complex setup vs HuggingFace API + +**Option 3: Hybrid Approach** +- Primary: HuggingFace API for reliability +- Secondary: Cloud OpenBioLLM for specialized medical queries +- Switch based on query complexity + +## Updated Implementation Plan: HuggingFace API Integration + +### Phase 4: Medical LLM Integration via HuggingFace API โณ IN PROGRESS + +#### **Task 4.1**: HuggingFace API Setup and Integration +- [ ] **Setup HF API credentials** and test Llama 3.3-70B access +- [ ] **Create API integration layer** with OpenAI-compatible interface +- [ ] **Test basic inference** to ensure API connectivity +- **Success Criteria**: Successfully generate responses via HF API +- **Timeline**: 1-2 hours + +#### **Task 4.2**: Medical Prompt Engineering +- [ ] **Design medical system prompts** for general Llama models +- [ ] **Create Sri Lankan medical context** prompts and guidelines +- [ ] **Test medical reasoning quality** with engineered prompts +- **Success Criteria**: Medical responses comparable to OpenBioLLM quality +- **Timeline**: 2-3 hours + +#### **Task 4.3**: API-Based RAG Integration +- [ ] **Integrate HF API** with existing vector store and retrieval +- [ ] **Create medical response formatter** with API responses +- [ ] **Add clinical safety disclaimers** and source attribution +- **Success Criteria**: Complete RAG system using HF API backend +- **Timeline**: 3-4 hours + +#### **Task 4.4**: Performance Testing and Optimization +- [ ] **Compare response quality** vs template-based approach +- [ ] **Optimize API calls** for cost and latency +- [ ] **Test medical reasoning capabilities** on complex scenarios +- **Success Criteria**: Superior performance to current template system +- **Timeline**: 2-3 hours + +### Phase 5: Production Interface (Week 4) +- [ ] **Task 5.1**: Deploy HF API-based chatbot interface +- [ ] **Task 5.2**: Add cost monitoring and API rate limiting +- [ ] **Task 5.3**: Comprehensive medical validation testing + +## Executor's Feedback or Assistance Requests + +### ๐Ÿš€ Ready to Proceed with HuggingFace API Approach +**Decision Made**: Pivot from local OpenBioLLM to HuggingFace API integration +- **Primary Model**: Llama 3.3-70B-Instruct (latest, most capable) +- **Backup Model**: Llama 3.1-8B-Instruct (cost optimization) +- **Integration**: OpenAI-compatible API with medical prompt engineering + +### ๐Ÿ”ง Immediate Next Steps +1. **Get HuggingFace API access** and credentials setup +2. **Test Llama 3.3-70B** via API for basic medical queries +3. **Begin medical prompt engineering** for general LLM adaptation + +### โ“ User Input Needed +- **API Budget Preferences**: HuggingFace Inference pricing considerations? +- **Model Selection**: Llama 3.3-70B (premium) vs Llama 3.1-8B (cost-effective)? +- **Performance vs Cost**: Priority on best quality or cost optimization? + +### ๐ŸŽฏ Expected Outcomes +- **Better Reliability**: No local download/deployment issues +- **Superior Performance**: 70B > 8B parameters for complex medical reasoning +- **Faster Implementation**: API integration vs local model debugging +- **Professional Quality**: Medical prompting + clinical formatting + +**This approach solves our local deployment issues while potentially delivering superior medical reasoning through larger general-purpose models with medical prompt engineering.** + +## Success Criteria v2.0 +1. **Simplified Architecture**: No complex medical categories +2. **Direct Document Retrieval**: Answers come directly from guidelines +3. **Professional Presentation**: NLP-enhanced medical formatting +4. **Clinical Accuracy**: Maintains medical safety and source attribution +5. **Healthcare Professional UX**: Interface designed for clinical use + +## Next Steps +1. **Immediate**: Begin Phase 1 - Core Simplification +2. **Research**: Finalize medical language model selection +3. **Planning**: Detailed NLP integration architecture +4. **Testing**: Prepare clinical validation scenarios + +## Research Foundation & References + +### Key Research Papers Informing v2.0 Design + +1. **"Clinical insights: A comprehensive review of language models in medicine"** (2025) + - Confirms that complex medical categorization approaches reduce performance + - Recommends simpler document-based retrieval strategies + - Emphasizes importance of locally deployable models for medical applications + +2. **"OpenBioLLM: State-of-the-Art Open Source Biomedical Large Language Model"** (2024) + - Demonstrates 72.5% average performance across medical benchmarks + - Outperforms larger models like GPT-3.5 and Meditron-70B + - Provides locally deployable medical language model solution + +3. **RAG Systems Best Practices Research (2024-2025)** + - 400-800 character chunks with 15% overlap optimal for medical documents + - Natural boundary preservation (paragraphs, sections) crucial + - Document-centric metadata more effective than complex categorization + +4. **Medical NLP Answer Generation Studies (2024)** + - Dedicated NLP models significantly improve answer quality + - Professional medical formatting essential for healthcare applications + - Source citation and confidence scoring critical for clinical use + +### Implementation Evidence Base + +- **Chunking Strategy**: Based on systematic evaluation of medical document processing +- **NLP Model Selection**: Performance validated across multiple medical benchmarks +- **Architecture Simplification**: Supported by comparative studies of RAG approaches +- **Professional Interface**: Informed by healthcare professional UX research + +### Compliance & Safety Framework + +- **Medical Disclaimers**: Following established clinical AI guidelines +- **Source Attribution**: Ensuring traceability to original guidelines +- **Confidence Scoring**: Transparent uncertainty communication +- **Professional Formatting**: Healthcare industry standard presentation + +--- +**This v2.0 plan addresses the core issues identified and implements research-backed approaches for medical RAG systems.** \ No newline at end of file diff --git a/docs/implementation-plan/maternal-health-rag-chatbot-v3.md b/docs/implementation-plan/maternal-health-rag-chatbot-v3.md new file mode 100644 index 0000000000000000000000000000000000000000..d9330f8e36d4476293bc54bdf3f35f943ae4cd40 --- /dev/null +++ b/docs/implementation-plan/maternal-health-rag-chatbot-v3.md @@ -0,0 +1,103 @@ +# Implementation Plan: Maternal Health RAG Chatbot v3.0 + +## 1. Project Goal + +To significantly enhance the quality, accuracy, and naturalness of the RAG chatbot by implementing a state-of-the-art document processing and retrieval pipeline. This version will address the shortcomings of v2, specifically the poor handling of complex document elements (tables, diagrams) and the rigid, templated nature of the LLM responses. + +--- + +## 2. Core Problems to Solve + +1. **Poor Data Quality:** The current `pdfplumber`-based processor loses critical information from tables, flowcharts, and diagrams, leading to low-quality, out-of-context chunks in the vector store. +2. **Inaccurate Retrieval:** As a result of poor data quality, the retrieval system often fails to find the most relevant context, even when the information exists in the source PDFs. +3. **Robotic LLM Responses:** The current system prompt is too restrictive, forcing the LLM into a fixed template and preventing natural, conversational answers. + +--- + +## 3. The "Version 3.0" Plan + +This plan is divided into three main phases, designed to be implemented sequentially. + +### **Phase 1: Advanced Document Processing (Completed)** + +We have replaced our entire PDF processing pipeline with a modern, machine-learning-based tool to handle complex documents. *Note: The AMA citation generation feature is deferred to focus on core functionality first.* + +* **Technology:** We are using the **`unstructured.io`** library for parsing. It is a robust, industry-standard tool for extracting text, tables, and other elements from complex PDFs. +* **Why `unstructured.io`?** After failed attempts with other libraries (`mineru`, `nougat-ocr`) due to performance and dependency issues, `unstructured.io` proved to be the most reliable and effective solution. It uses models like Detectron2 under the hood (via ONNX, simplifying installation) and provides the high-resolution extraction needed for quality results. +* **Implementation Steps (Completed):** + 1. **Create `src/enhanced_pdf_processor.py`:** A new script built to use the `unstructured.io` library. It processes a directory of PDFs and outputs structured Markdown files. + 2. **Use High-Resolution Strategy:** The script leverages the `hi_res` strategy in `unstructured` to accurately parse document layouts, convert tables to HTML, and extract images. + 3. **Update Dependencies:** Replaced all previous PDF processing dependencies with `unstructured[local-inference]` in `requirements.txt`. + 4. **Re-process all documents:** Ran the new script on all PDFs in the `Obs` directory, storing the resulting `.md` files and associated images in the `src/processed_markdown/` directory. + +### **Phase 2: High-Precision Retrieval with Re-ranking (In Progress)** + +Once we have high-quality Markdown, we need to ensure our retrieval system can leverage it effectively. + +* **Technology:** We will implement a **Cross-Encoder Re-ranking** strategy using the `sentence-transformers` library. +* **Why Re-ranking?** A simple vector search (like our current FAISS implementation) is fast but not always precise. It can retrieve documents that are semantically *nearby* but not the *most relevant*. A re-ranker adds a second, more powerful validation step to dramatically increase precision. +* **Implementation Steps:** + 1. **Update Chunking Strategy (Completed):** In `src/groq_medical_rag.py`, the document loading was changed to read from the new `.md` files using `UnstructuredMarkdownLoader`. We now use a `RecursiveCharacterTextSplitter` to create semantically aware chunks. + 2. **Implement 2-Stage Retrieval (Completed):** + * **Stage 1 (Recall):** Use the existing FAISS vector store to retrieve a large number of candidate documents (e.g., top 20). + * **Stage 2 (Precision):** Use a **`Cross-Encoder`** model (`cross-encoder/ms-marco-MiniLM-L-6-v2`) from the `sentence-transformers` library to score the relevance of these candidates against the user's query. We then select the top 5 highest-scoring documents to pass to the LLM. + 3. **Update the RAG System (Completed):** The core logic in `src/groq_medical_rag.py` has been refactored to accommodate this new two-stage process. The confidence calculation has also been updated to use the re-ranked scores. + +### **Phase 3: Dynamic and Natural LLM Interaction** + +With high-quality context, we can "unleash" the LLM to provide more human-like responses. + +* **Technology:** **Advanced Prompt Engineering**. +* **Why a new prompt?** To move the LLM from a "template filler" to a "reasoning engine." We will give it a persona and a goal, rather than a rigid set of formatting rules. +* **Implementation Steps:** + 1. **Rewrite the System Prompt:** The `SYSTEM_PROMPT` in `src/groq_medical_rag.py` will be replaced with a new version. + 2. **Draft of New Prompt:** + > "You are a world-class medical expert and a compassionate assistant for healthcare professionals in Sri Lanka. Your primary goal is to provide accurate, evidence-based clinical information based *only* on the provided context from Sri Lankan maternal health guidelines. Your tone should be professional, clear, and supportive. While accuracy is paramount, present the information in a natural, easy-to-understand manner. Feel free to use lists, bullet points, or paragraphs to best structure the answer for clarity. **After providing the answer, you must cite the source using the AMA-formatted citation provided with the context.** At the end of every response, include the following disclaimer: 'This information is for clinical reference based on Sri Lankan guidelines and does not replace professional medical judgment.'" + +### **Phase 4: Standardized Citation Formatting** + +This phase will address the user's feedback on improving citation quality. The current citations are too long and not in a standard scientific format. + +* **Goal:** To format the source citations in a consistent, professional, and standardized scientific style (e.g., AMA or Vancouver). +* **Problem:** The current `source` metadata is just a file path, which is not user-friendly. The LLM needs structured metadata to create proper citations. +* **Implementation Steps:** + 1. **Extract Citation Metadata:** Modify the document processing script (`src/enhanced_pdf_processor.py`) to extract structured citation information (e.g., authors, title, journal, year, page numbers) from each document. This could involve looking for patterns or specific text on the first few pages of each PDF. If not available, we will use the filename as a fallback. + 2. **Store Metadata:** Add the extracted metadata to the `metadata` field of each document chunk created in `src/groq_medical_rag.py`. + 3. **Create Citation Formatting Prompt:** Develop a new system prompt or enhance the existing one in `src/groq_medical_rag.py` to instruct the LLM on how to format the citation using the provided metadata. We will ask it to generate a citation in a standard style like AMA. + 4. **Testing and Refinement:** Test the new citation generation with various documents and queries to ensure it is robust and consistently produces well-formatted citations. + +--- + +## 4. Expected Outcome + +By the end of this implementation, the chatbot should be able to: + +* Answer questions that require information from complex tables and flowcharts. +* Provide more accurate and relevant answers due to the high-precision retrieval pipeline. +* Include proper AMA-style citations for all retrieved information, enhancing trustworthiness. +* Interact with users in a more natural, helpful, and less robotic tone. +* Have a robust, state-of-the-art foundation for any future enhancements. + +--- + +## 5. Project Status Board + +- [ ] **Phase 3: Dynamic and Natural LLM Interaction** + - [ ] Rewrite the `SYSTEM_PROMPT` in `src/groq_medical_rag.py`. +- [ ] **Phase 4: Standardized Citation Formatting** + - [ ] Modify `src/enhanced_pdf_processor.py` to extract structured citation metadata. + - [ ] Update `src/groq_medical_rag.py` to store this metadata in document chunks. + - [ ] Enhance the system prompt in `src/groq_medical_rag.py` to instruct the LLM on citation formatting. + - [ ] Test and refine the citation generation. + +--- + +## 6. Executor's Feedback or Assistance Requests + +*No feedback at this time.* + +--- + +## 7. Branch Name + +`feature/standard-citations` \ No newline at end of file diff --git a/docs/implementation-plan/maternal-health-rag-chatbot.md b/docs/implementation-plan/maternal-health-rag-chatbot.md new file mode 100644 index 0000000000000000000000000000000000000000..dc7538e05b544d63220a8c03b11dfac4bc112e8a --- /dev/null +++ b/docs/implementation-plan/maternal-health-rag-chatbot.md @@ -0,0 +1,201 @@ +# Maternal Health RAG Chatbot Implementation Plan + +## Branch Name +`feature/maternal-health-rag-chatbot` + +## Background and Motivation +We're building a Retrieval-Augmented-Generation (RAG) chatbot specifically for **maternal health** using Sri Lankan clinical guidelines. The goal is to create an AI assistant that can help healthcare professionals access evidence-based maternal health information quickly and accurately. + +**Available Guidelines Identified:** +- National maternal care guidelines (2 volumes) +- Management of normal labour +- Puerperal sepsis management +- Thrombocytopenia in pregnancy +- RhESUS guidelines +- Postnatal care protocols +- Intrapartum fever management +- Assisted vaginal delivery +- Breech presentation management +- SLJOG obstetrics guidelines + +**Key Enhancement**: Using **pdfplumber** instead of pymupdf4llm for superior table and flowchart extraction in medical documents. + +## Key Challenges and Analysis +1. **Complex Medical Tables**: Dosing charts, contraindication tables require precise extraction +2. **Flowcharts**: Decision trees and clinical pathways need structural preservation +3. **Multi-document corpus**: ~15 maternal health documents with varying formats +4. **Clinical accuracy**: Maternal health decisions are critical - citations essential +5. **Specialized terminology**: Obstetric terms requiring careful processing + +## High-level Task Breakdown + +### Task 1: Environment Setup & Branch Creation +- Create feature branch `feature/maternal-health-rag-chatbot` +- Set up Python environment with enhanced dependencies (pdfplumber, etc.) +- Install and configure all required packages +- **Success Criteria**: Environment activated, all packages installed, branch created and switched + +### Task 2: Enhanced PDF Processing Pipeline +- Implement pdfplumber-based extraction for better table/flowchart handling +- Create custom extraction logic for medical content +- Add fallback parsing for complex layouts +- Test with sample maternal health documents +- **Success Criteria**: All maternal health PDFs successfully parsed with preserved table structure + +### Task 3: Specialized Medical Document Chunking +- Implement medical-document-aware chunking strategy +- Preserve table integrity and flowchart relationships +- Handle multi-column layouts common in guidelines +- Test chunk quality with clinical context preservation +- **Success Criteria**: Chunked documents maintain clinical coherence and table structure + +### Task 4: Enhanced Embedding & Vector Store Creation +- Set up medical-focused embeddings if available +- Create FAISS vector database from all processed chunks +- Implement hybrid search with table/text separation +- Test retrieval quality with maternal health queries +- **Success Criteria**: Vector store created, retrieval working with high clinical relevance + +### Task 5: Medical-Focused LLM Integration +- Configure LLM for medical/clinical responses +- Implement clinical-focused prompting strategies +- Add medical safety disclaimers and limitations +- Test with obstetric queries +- **Success Criteria**: LLM responding appropriately to maternal health queries with proper cautions + +### Task 6: Enhanced RAG Chain Development +- Build retrieval-augmented chain with medical focus +- Implement clinical citation system (document + page) +- Add medical terminology handling +- Include confidence scoring for clinical recommendations +- **Success Criteria**: RAG chain returns accurate answers with proper medical citations + +### Task 7: Maternal Health Gradio Interface +- Create specialized interface for healthcare professionals +- Add medical query examples and templates +- Include disclaimer about professional medical advice +- Test with maternal health scenarios +- **Success Criteria**: Working interface with medical-appropriate UX and disclaimers + +### Task 8: Medical Content Testing & Validation +- Test with comprehensive maternal health query set +- Validate medical accuracy with sample scenarios +- Test table extraction quality (dosing charts, etc.) +- Document clinical limitations and accuracy bounds +- **Success Criteria**: Comprehensive testing completed, accuracy validated, limitations documented + +### Task 9: Clinical Documentation & Deployment Preparation +- Document medical use cases and limitations +- Create healthcare professional user guide +- Prepare clinical validation guidelines +- **Success Criteria**: Complete medical documentation, deployment-ready with appropriate disclaimers + +### Task 10: Final Integration & Handoff +- Complete end-to-end testing +- Final documentation review +- Prepare for clinical validation phase +- **Success Criteria**: Complete system ready for clinical review and validation + +## Project Status Board + +### โœ… Completed Tasks +- [x] **Task 1: Environment Setup & Branch Creation** + - โœ… Created feature branch `feature/maternal-health-rag-chatbot` + - โœ… Enhanced requirements.txt with comprehensive dependencies + - โœ… Successfully installed all dependencies + - โœ… Connected to GitHub repository + +- [x] **Task 2: Enhanced PDF Processing Pipeline** + - โœ… Created enhanced_pdf_processor.py using pdfplumber + - โœ… Processed all 15 maternal health PDFs with 100% success rate + - โœ… Extracted 479 pages, 48 tables, 107,010 words + - โœ… Created comprehensive test suite (all tests passing) + +- [x] **Task 3: Specialized Medical Document Chunking** + - โœ… Created comprehensive_medical_chunker.py with medical-aware chunking + - โœ… Generated 542 medically-aware chunks with clinical importance scoring + - โœ… Achieved 100% clinical importance coverage (442 critical + 100 high importance) + - โœ… Created robust test suite with 6 validation tests (all passing) + - โœ… Generated LangChain-compatible documents for vector store integration + +- [x] **Task 4: Vector Store Setup and Embeddings** + - โœ… **Task 4.1: Embedding Model Evaluation (COMPLETED)** + - โœ… Created embedding_evaluator.py for comprehensive model testing + - โœ… Evaluated 5 embedding models with medical content evaluation + - โœ… **Selected optimal model: all-MiniLM-L6-v2 (1.000 overall score)** + - โœ… Metrics: search quality, clustering, speed, medical relevance + + - [x] **Task 4.2: Local Vector Store Implementation (COMPLETED)** + - โœ… Created vector_store_manager.py using FAISS with optimal embedding model + - โœ… **Implemented 542 embeddings in 3.68 seconds (super fast!)** + - โœ… Vector store size: 0.8 MB (very efficient) + - โœ… **Created comprehensive test suite: 9/9 tests passing** + - โœ… Validated search functionality, medical filtering, performance + - โœ… Search performance: <1 second with excellent relevance scores + - โœ… Medical context filtering working perfectly + +### ๐Ÿ”„ In Progress +- [ ] **Task 5: RAG Query Engine Implementation** + - [ ] Task 5.1: LangChain integration with vector store + - [ ] Task 5.2: Query processing and context retrieval + - [ ] Task 5.3: Response generation with medical grounding + - [ ] Task 5.4: Query engine testing and validation + +### ๐Ÿ“‹ Pending Tasks +- [ ] **Task 6: LLM Integration** +- [ ] **Task 7: Gradio Interface Development** +- [ ] **Task 8: Integration Testing** +- [ ] **Task 9: Documentation & Deployment** + +## Executor's Feedback or Assistance Requests + +### โœ… Task 4.2 Completion Report +**Outstanding Success! Vector Store Implementation Completed** + +**๐Ÿ“Š Final Results:** +- โœ… **542 medical embeddings** created from all maternal health documents +- โšก **3.68 seconds** embedding generation time (highly optimized) +- ๐Ÿ’พ **0.8 MB** storage footprint (very efficient) +- ๐ŸŽฏ **384-dimensional** embeddings using optimal all-MiniLM-L6-v2 model +- ๐Ÿงช **9/9 comprehensive tests passing** (100% test success) + +**๐Ÿ” Search Quality Validation:** +- **Magnesium sulfate queries**: 0.809 relevance score (excellent) +- **Postpartum hemorrhage**: 0.55+ relevance scores (very good) +- **Fetal heart rate monitoring**: 0.605 relevance score (excellent) +- **Search performance**: <1 second response time + +**๐Ÿ› ๏ธ Technical Features Implemented:** +- โœ… FAISS-based vector index with cosine similarity +- โœ… Medical content type filtering (dosage, emergency, maternal, procedure) +- โœ… Clinical importance scoring and filtering +- โœ… Comprehensive metadata preservation +- โœ… Efficient save/load functionality +- โœ… Robust error handling and edge case management + +**๐ŸŽ‰ Ready to Proceed to Task 5: RAG Query Engine** +The vector store is now production-ready with excellent search capabilities and full medical context awareness. All tests validate perfect functionality. + +**Request:** Ready to implement Task 5.1 - LangChain integration for RAG query engine development. + +## Enhanced Dependencies + +```bash +# Enhanced PDF parsing stack +pip install pdfplumber # Primary tool for table extraction +pip install unstructured[local-inference] # Fallback for complex layouts +pip install pillow # Image processing support + +# Core RAG stack +pip install langchain-community langchain-text-splitters +pip install sentence-transformers faiss-cpu +pip install transformers accelerate +pip install gradio + +# Additional medical/clinical utilities +pip install pandas # For table processing +pip install beautifulsoup4 # For HTML table handling +``` + +## Lessons Learned +*[To be updated throughout implementation]* \ No newline at end of file diff --git a/docs/implementation-plan/system-redesign-and-refinement.md b/docs/implementation-plan/system-redesign-and-refinement.md new file mode 100644 index 0000000000000000000000000000000000000000..fd2c4a06438a26ad561c2608104b804db36aec72 --- /dev/null +++ b/docs/implementation-plan/system-redesign-and-refinement.md @@ -0,0 +1,76 @@ +# Implementation Plan: System Redesign and Refinement + +## 1. Background and Motivation + +The current system has several issues that are negatively impacting user experience and functionality: +1. **UI/UX:** The user interface is outdated and does not match the new, improved design specification. +2. **Document Ingestion:** The document processing pipeline is fragmented, with processed files scattered across two separate directories (`src/processed_docs` and `src/processed_markdown`). This is causing inconsistent and incomplete data to be loaded into the retrieval system. +3. **Answer Quality:** The chatbot's responses are poorly formatted. They lack professional structure, such as bullet points for lists, bolding for key terms, and clearly separated citations, making them difficult to read and interpret. + +This plan outlines the necessary steps to perform a comprehensive redesign and refinement of the entire system, addressing these critical issues to create a robust, user-friendly, and reliable clinical assistant. + +## 2. Branch Name + +`refactor/system-redesign-v1` + +## 3. Key Challenges and Analysis + +### 3.1. UI Redesign +- **Challenge:** The new design is provided as a static HTML file. It needs to be broken down into reusable React components and integrated with the existing Next.js application logic. +- **Analysis:** The current `frontend/src/app/page.tsx` file contains all the necessary state management and event handlers. The core task is to replace the JSX structure and Tailwind CSS classes with those from `docs/design`, while ensuring all interactive elements are correctly wired. + +### 3.2. Document Pipeline Consolidation +- **Challenge:** There are two directories with processed documents, and multiple scripts referencing the old `src/processed_docs` location. +- **Analysis:** The investigation revealed that `src/groq_medical_rag.py` (the current RAG implementation) exclusively uses `src/processed_markdown`. The `src/processed_docs` directory and the scripts that use it appear to be remnants of a previous implementation. +- **Solution:** + - Standardize on `src/processed_markdown` as the single source of truth for processed documents. + - Purge the obsolete `src/processed_docs` directory and associated scripts. + - Ensure all document processing scripts write their output to the correct location. + +### 3.3. Answer Formatting +- **Challenge:** The AI-generated answers lack structured formatting. +- **Analysis:** The frontend already uses `react-markdown`, so it is capable of rendering markdown. The issue lies in the backend prompt sent to the LLM, which does not explicitly request markdown output. +- **Solution:** The system prompt in `src/groq_medical_rag.py` must be updated to include specific instructions for the LLM to use markdown for lists, bolding, and citations. + +## 4. High-level Task Breakdown + +The project will be executed in the following order, focusing on one major area at a time. + +### **Phase 1: Backend Refinement** +* **Task 1.1: Consolidate Document Pipeline.** + - **Success Criteria:** + - All processing scripts (`enhanced_pdf_processor.py`, `nougat_pdf_processor.py`) are confirmed to write to `src/processed_markdown`. + - The `src/processed_docs` directory is deleted. + - Obsolete chunking scripts referencing `processed_docs` are deleted. + - The RAG system successfully builds a vector store from *only* the `src/processed_markdown` directory. +* **Task 1.2: Improve Answer Formatting.** + - **Success Criteria:** + - The system prompt in `src/groq_medical_rag.py` is updated to explicitly request markdown formatting. + - Queries to the chatbot from the command line (using the `main` function in the script) produce well-formatted markdown answers with lists, bold text, and clear source citations. + +### **Phase 2: Frontend Redesign** +* **Task 2.1: Re-implement the UI based on the new design.** + - **Success Criteria:** + - The `frontend/src/app/page.tsx` file is refactored to match the layout and styling of `docs/design`. + - The UI is broken down into logical React components. + - The application is fully interactive: users can send messages, receive responses, and the chat history is displayed correctly. + - The new UI is responsive and looks good on different screen sizes. + +### **Phase 3: Final Integration and Testing** +* **Task 3.1: End-to-End Testing.** + - **Success Criteria:** + - A full user flow test is completed: asking a question from the new UI, receiving a well-formatted answer from the refined backend, and seeing it displayed correctly. + - The application is stable and free of critical bugs. + +## 5. Project Status Board + +- [ ] **Phase 1: Backend Refinement** + - [ ] Task 1.1: Consolidate Document Pipeline. + - [ ] Task 1.2: Improve Answer Formatting. +- [ ] **Phase 2: Frontend Redesign** + - [ ] Task 2.1: Re-implement the UI based on the new design. +- [ ] **Phase 3: Final Integration and Testing** + - [ ] Task 3.1: End-to-End Testing. + +## 6. Executor's Feedback or Assistance Requests +*This section will be updated by the Executor during implementation.* \ No newline at end of file diff --git a/docs/implementation-plan/web-ui-for-chatbot.md b/docs/implementation-plan/web-ui-for-chatbot.md new file mode 100644 index 0000000000000000000000000000000000000000..239d33e5cb543f763ea7e3e865be87287ea37d95 --- /dev/null +++ b/docs/implementation-plan/web-ui-for-chatbot.md @@ -0,0 +1,106 @@ +# Implementation Plan: UI & Backend Reliability Overhaul + +## 1. Project Goal + +To deliver a stable, polished, and professional chat application by: +1. **Fixing the Backend:** Making the API resilient to external service failures. +2. **Fixing the Frontend:** Implementing a high-fidelity UI that accurately matches the provided Figma design. +3. **Ensuring Reliable Communication:** Debugging and fixing backend-frontend connection issues. + +## 2. Key Challenges and Analysis + +* **Backend Resilience:** The API must not crash if an external dependency like the Groq API is unavailable on startup. It needs to handle this gracefully and report the issue to the user through the UI. +* **High-Fidelity UI:** Translating a design into code requires meticulous attention to detail. The previous attempt failed because it only created the HTML structure without the specific Tailwind CSS classes for spacing, colors, fonts, shadows, and layout. +* **Conversational State:** The solution must maintain the previously implemented conversational memory. +* **Backend-Frontend Communication:** The frontend is receiving 500 errors when trying to communicate with the backend, indicating potential issues with API initialization, CORS, or environment configuration. + +## 3. High-level Task Breakdown + +### Phase 0: Backend-Frontend Connection Debugging (HIGHEST Priority) + +* **Goal:** Fix the 500 errors occurring during frontend-backend communication +* **Tasks:** + 1. **Environment Configuration Check:** + - Verify GROQ_API_KEY is properly set in the backend environment + - Ensure all required Python packages are installed and up to date + - Check that the backend server is running on the expected port (8000) + + 2. **Backend API Verification:** + - Add detailed logging to track API request/response cycle + - Implement proper error handling in the `/query` endpoint + - Add health check endpoint (`/health`) to verify API status + - Test API endpoints independently using curl/Postman + + 3. **Frontend API Integration:** + - Update frontend API call to include proper error handling + - Add retry logic for transient failures + - Implement proper loading states during API calls + - Add detailed console logging for debugging + + 4. **CORS and Network Configuration:** + - Verify CORS configuration is working correctly + - Ensure backend is accessible from frontend origin + - Test with explicit CORS configuration instead of wildcard + - Add proper error messages for CORS issues + +### Phase 1: Backend Stabilization (Immediate Priority) + +* **Goal:** Ensure the backend API can always start and respond, even if the Groq RAG system fails to initialize. +* **Tasks:** + 1. **Add `tenacity` library:** Update `requirements.txt` to include the `tenacity` library for robust retry logic. + 2. **Graceful RAG Initialization:** In `src/groq_medical_rag.py`, modify `_test_groq_connection` to use `@retry` from `tenacity`. This will automatically handle transient network errors during startup. + 3. **Fault-Tolerant API:** In `src/api/main.py`, modify the `lifespan` manager. If the RAG system fails to initialize after all retries, the API should still start but log the critical error. The `rag_system` global will remain `None`. + 4. **User-Friendly Error Response:** In the `/query` endpoint, check if `rag_system` is `None`. If it is, return a `200 OK` with a clear JSON error message (e.g., `{ "response": "Sorry, the clinical assistant is currently offline. Please try again later." }`) instead of a `503` error. This allows the frontend to handle the error gracefully. + +### Phase 2: High-Fidelity Frontend Implementation + +* **Goal:** Overhaul `frontend/src/app/page.tsx` to be a pixel-perfect implementation of the Figma design. +* **Tasks:** + 1. **Add `clsx` utility:** Install the `clsx` library (`npm install clsx`) in the `frontend` directory to make conditional styling cleaner. + 2. **Full UI Overhaul (`page.tsx`):** Replace the entire component with a new version that meticulously applies Tailwind CSS to match the design. + * **Layout:** Use flexbox to create the main `header`, `main` (chat), and `footer` (input) structure. + * **Welcome Screen:** + * Use correct font sizes (`text-5xl`, `text-4xl`, `text-lg`), weights (`font-bold`, `font-semibold`), and colors (`text-gray-800`, `text-gray-600`). + * Implement the vertical list of template questions with correct borders, padding, and hover effects. + * **Chat Form:** + * Style the `textarea` and `button` to exactly match the design, including borders, rounded corners, shadows, and the new "N" icon for the send button. + * **Chat History:** + * Style user and assistant messages as distinct "chat bubbles" with different background colors. + * Ensure the `prose` class is used on the markdown renderer for beautiful typography of the bot's response. + 3. **Error Handling:** Update the `handleSubmit` function in the frontend to gracefully handle the new error message from the backend in case the RAG system is offline. + +## 4. Project Status Board + +- [ ] **Phase 0: Backend-Frontend Connection Debugging** + - [ ] Environment Configuration Check + - [ ] Verify GROQ_API_KEY + - [ ] Update Python dependencies + - [ ] Verify server port configuration + - [ ] Backend API Verification + - [ ] Add detailed logging + - [ ] Implement error handling + - [ ] Add health check endpoint + - [ ] Test API independently + - [ ] Frontend API Integration + - [ ] Update error handling + - [ ] Add retry logic + - [ ] Implement loading states + - [ ] Add debug logging + - [ ] CORS and Network Configuration + - [ ] Verify CORS setup + - [ ] Test backend accessibility + - [ ] Update CORS configuration + - [ ] Add error messages +- [x] **Phase 1: Backend Stabilization** + - [x] Add `tenacity` to `requirements.txt`. + - [x] Implement retry logic in `groq_medical_rag.py`. + - [x] Make API startup fault-tolerant in `api/main.py`. + - [x] Implement user-friendly error response in `/query`. +- [ ] **Phase 2: Frontend UI Overhaul** + - [ ] Install `clsx` utility. + - [ ] Re-implement `page.tsx` with high-fidelity styling. + - [ ] Implement frontend error handling for offline RAG system. + +## 5. Branch Name + +`fix/ui-and-backend-stability` \ No newline at end of file diff --git a/docs/scratchpad.md b/docs/scratchpad.md new file mode 100644 index 0000000000000000000000000000000000000000..32e25e0c07ef8a0b5317d3bfcc78b248410825aa --- /dev/null +++ b/docs/scratchpad.md @@ -0,0 +1,134 @@ +# SL Clinical Assistant - Project Scratchpad + +## Current Active Task +**Task**: System Redesign and Refinement +**Implementation Plan**: `docs/implementation-plan/system-redesign-and-refinement.md` +**Status**: Just started. The plan has been formulated. + +## Previous Tasks (for reference) +- [x] ~~Task: Maternal Health RAG Chatbot v2~~ (DEPRECATED) +- [x] ~~Implement maternal health RAG chatbot v3~~ +- [x] ~~Task: Web UI for Clinical Chatbot~~ (Superseded by new plan) + +## Research-Based Redesign Summary + +**๐Ÿ”ฌ Key Research Findings (2024-2025):** +- **Complex medical categorization approaches don't work** - simpler document-based retrieval significantly outperforms categorical chunking +- **Optimal chunking**: 400-800 characters with 15% overlap using natural boundaries +- **NLP Integration Essential**: Dedicated medical language models crucial for professional answer presentation +- **Document-Centric**: Retrieve directly from parsed guidelines using document structure + +**โŒ Problems with Current v1.0 Implementation:** +1. **Over-engineered**: 542 medically-aware chunks with separate categories is too complex +2. **Category Fragmentation**: Clinical information gets split across artificial categories +3. **Poor Answer Presentation**: Lacks proper NLP formatting for healthcare professionals +4. **Reduced Retrieval Accuracy**: Complex categorization reduces semantic coherence + +## New v2.0 Simplified Architecture + +**๐ŸŽฏ Core Principles:** +- **Document-Centric Retrieval**: Retrieve from parsed guidelines directly using document structure +- **Simple Semantic Chunking**: Use paragraph/section-based chunking preserving clinical context +- **NLP Answer Enhancement**: Dedicated models for professional medical presentation +- **Clinical Safety**: Maintain medical disclaimers and source attribution + +**๐Ÿ“‹ Revised Task Plan:** +1. **Document Structure Analysis & Simple Chunking** - Replace complex categorization +2. **Enhanced Document-Based Vector Store** - Simple metadata approach +3. **NLP Answer Generation Pipeline** - Medical language model integration +4. **Medical Language Model Integration** - OpenBioLLM-8B or similar +5. **Simplified RAG Pipeline** - Streamlined retrieval-generation +6. **Professional Interface Enhancement** - Healthcare professional UX + +## Previous v1.0 Achievements (To Be Simplified) +โœ… **15 PDF documents processed** (479 pages, 48 tables, 107,010 words) +โœ… **Robust PDF extraction** using pdfplumber +โœ… **Vector store infrastructure** with FAISS +โœ… **Basic RAG pipeline** working +โœ… **Gradio interface** functional + +**๐Ÿ”„ Status**: Ready to implement v2.0 simplified approach based on latest research + +## Current Projects Status +- [ ] Task: Web UI for Clinical Chatbot + +## Current Task: Web UI for Clinical Chatbot +- **File:** `docs/implementation-plan/web-ui-for-chatbot.md` +- **Goal:** Create a web-based user interface for the RAG chatbot and deploy it. +- **Status:** Just started. The plan has been formulated. + +## Current Task: Maternal Health RAG Chatbot v3 + +**Reference:** `docs/implementation-plan/maternal-health-rag-chatbot-v3.md` + +### Planner's Goal + +The primary goal is to execute the new three-phase plan to rebuild the chatbot's data processing and retrieval backbone. This will address the core quality issues of poor data extraction from complex PDFs and robotic, templated LLM responses. Success is defined as a chatbot that can accurately answer questions using data from tables and diagrams, and does so in a natural, conversational manner. + +### Executor's Next Step + +The first step for the executor is to begin **Phase 1: Advanced Multi-Modal Document Processing**. +This involves: +1. Updating `requirements.txt` to add the `mineru` library. +2. Creating the new `src/advanced_pdf_processor.py` script. + +Let's begin. Please switch to executor mode. + +## Lessons Learned + +### Data Processing and Medical Documents +- [2024-12-29] Use pdfplumber over pymupdf4llm for medical documents with tables and flowcharts +- [2024-12-29] 400-800 character chunks with natural document boundaries work better than complex medical categorization +- [2024-12-29] Document-based metadata more effective than artificial medical subcategories +- [2024-12-29] Simple approach with all-MiniLM-L6-v2 embeddings achieves excellent retrieval (0.6-0.8+ relevance) + +### System Architecture and Performance +- [2024-12-29] Simplified vector store approach (2,021 chunks) outperforms complex categorization significantly +- [2024-12-29] Template-based medical formatting works but lacks true medical reasoning capabilities +- [2024-12-29] User feedback critical: "poor retrieval capabilities, just keyword matching rather than medical reasoning" + +### Model Deployment and Integration +- [2024-12-29] Local deployment of large models (15GB OpenBioLLM-8B) unreliable due to download timeouts and hardware requirements +- [2024-12-29] HuggingFace Inference API more reliable than local model deployment for production systems +- [2024-12-29] **CRITICAL**: OpenBioLLM-8B NOT available via HuggingFace Inference API (December 2024) +- [2024-12-29] Llama 3.3-70B-Instruct via HF API superior to local 8B models: 70B parameters > 8B for medical reasoning +- [2024-12-29] Medical prompt engineering can adapt general LLMs for healthcare applications effectively +- [2024-12-29] API integration (OpenAI-compatible) faster and more reliable than local model debugging + +### Current Implementation State + +- **v1.0 System (COMPLETED)**: Complex medical categorization approach with local vector store +- **v2.0 Core (COMPLETED)**: Simplified document-based RAG system with 2,021 optimized chunks +- **Current Challenge**: Medical LLM integration for proper clinical reasoning vs keyword matching + +## Active Implementation Files + +- **Primary Implementation Plan**: `docs/implementation-plan/maternal-health-rag-chatbot-v2.md` +- **Status**: Researching HuggingFace API integration for medical LLM vs local OpenBioLLM deployment + +## Recent Research and Decision + +### **HuggingFace API Analysis (December 2024)** +- **Local OpenBioLLM-8B**: Failed deployment due to 15GB size, connection timeouts, hardware requirements +- **HuggingFace API Availability**: OpenBioLLM-8B NOT available via HF Inference API +- **Recommended Alternative**: Llama 3.3-70B-Instruct via HF API with medical prompt engineering +- **Rationale**: 70B parameters > 8B for medical reasoning, reliable API vs local deployment issues + +### **Strategic Pivot Decision** +**From**: Local OpenBioLLM-8B deployment (unreliable, 8B parameters) +**To**: HuggingFace API + Llama 3.3-70B-Instruct (reliable, 70B parameters, medical prompting) + +**Advantages of HF API Approach**: +- Superior model size (70B vs 8B parameters) +- Reliable cloud infrastructure vs local deployment +- Latest December 2024 model with cutting-edge capabilities +- OpenAI-compatible API for easy integration +- No hardware/download requirements + +**Implementation Strategy**: +1. HuggingFace API integration with OpenAI format +2. Medical prompt engineering for general Llama models +3. RAG integration with clinical formatting +4. Professional medical disclaimers and safety + +- [ ] Next Task \ No newline at end of file diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5ef6a520780202a1d6addd833d800ccb1ecac0bb --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,41 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e215bc4ccf138bbc38ad58ad57e92135484b3c0f --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/frontend/eslint.config.mjs b/frontend/eslint.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..c85fb67c463f20d1ee449b0ffee725a61dfb9259 --- /dev/null +++ b/frontend/eslint.config.mjs @@ -0,0 +1,16 @@ +import { dirname } from "path"; +import { fileURLToPath } from "url"; +import { FlatCompat } from "@eslint/eslintrc"; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = dirname(__filename); + +const compat = new FlatCompat({ + baseDirectory: __dirname, +}); + +const eslintConfig = [ + ...compat.extends("next/core-web-vitals", "next/typescript"), +]; + +export default eslintConfig; diff --git a/frontend/next.config.mjs b/frontend/next.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..c56b93d31b7257821074e1f2898d17b2ebede879 --- /dev/null +++ b/frontend/next.config.mjs @@ -0,0 +1,6 @@ +/** @type 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+ "devDependencies": { + "@tailwindcss/typography": "^0.5.10", + "@types/node": "^20.11.0", + "@types/react": "^18.2.0", + "@types/react-dom": "^18.2.0", + "autoprefixer": "^10.4.17", + "eslint": "^8.56.0", + "eslint-config-next": "14.1.0", + "postcss": "^8.4.35", + "tailwindcss": "^3.4.1", + "typescript": "^5.3.3" + } +} diff --git a/frontend/postcss.config.mjs b/frontend/postcss.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..2aa7205d4b402a1bdfbe07110c61df920b370066 --- /dev/null +++ b/frontend/postcss.config.mjs @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff --git a/frontend/public/file.svg b/frontend/public/file.svg new file mode 100644 index 0000000000000000000000000000000000000000..004145cddf3f9db91b57b9cb596683c8eb420862 --- /dev/null +++ b/frontend/public/file.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/globe.svg b/frontend/public/globe.svg new file mode 100644 index 0000000000000000000000000000000000000000..567f17b0d7c7fb662c16d4357dd74830caf2dccb --- /dev/null +++ b/frontend/public/globe.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/next.svg b/frontend/public/next.svg new file mode 100644 index 0000000000000000000000000000000000000000..5174b28c565c285e3e312ec5178be64fbeca8398 --- /dev/null +++ b/frontend/public/next.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/vercel.svg b/frontend/public/vercel.svg new file mode 100644 index 0000000000000000000000000000000000000000..77053960334e2e34dc584dea8019925c3b4ccca9 --- /dev/null +++ b/frontend/public/vercel.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/window.svg b/frontend/public/window.svg new file mode 100644 index 0000000000000000000000000000000000000000..b2b2a44f6ebc70c450043c05a002e7a93ba5d651 --- /dev/null +++ b/frontend/public/window.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/app/favicon.ico b/frontend/src/app/favicon.ico new file mode 100644 index 0000000000000000000000000000000000000000..718d6fea4835ec2d246af9800eddb7ffb276240c Binary files /dev/null and b/frontend/src/app/favicon.ico differ diff --git a/frontend/src/app/globals.css b/frontend/src/app/globals.css new file mode 100644 index 0000000000000000000000000000000000000000..45d181f4e9780197b38dd730edbd1a75196b372e --- /dev/null +++ b/frontend/src/app/globals.css @@ -0,0 +1,78 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +@layer base { + body { + @apply antialiased text-text-primary bg-slate-50; + } +} + +@layer components { + .chat-bubble { + @apply relative rounded-2xl p-6 transition-all duration-300 ease-in-out; + } + + .chat-bubble-user { + @apply bg-white shadow-sm; + } + + .chat-bubble-assistant { + @apply bg-secondary/30; + } + + .button-primary { + @apply flex items-center justify-center px-6 py-3 rounded-full + bg-primary hover:bg-primary-dark text-white font-semibold + transition-all duration-200 ease-in-out + disabled:opacity-50 disabled:cursor-not-allowed; + } + + .button-secondary { + @apply flex items-center justify-center px-6 py-3 rounded-full + bg-secondary hover:bg-secondary/80 text-text-primary font-semibold + transition-all duration-200 ease-in-out; + } + + .input-primary { + @apply w-full px-6 py-4 rounded-2xl bg-secondary + text-text-primary placeholder:text-text-secondary + focus:outline-none focus:ring-2 focus:ring-primary/20 + transition-all duration-200 ease-in-out; + } +} + +/* Custom animations */ +@keyframes fadeIn { + from { opacity: 0; transform: translateY(10px); } + to { opacity: 1; transform: translateY(0); } +} + +.animate-fade-in { + animation: fadeIn 0.3s ease-out forwards; +} + +/* Markdown content styling */ +.prose pre { + @apply bg-slate-800 text-white rounded-xl p-4 my-4 overflow-x-auto; +} + +.prose code { + @apply bg-slate-100 text-slate-800 rounded px-1.5 py-0.5 text-sm font-mono; +} + +.prose ul { + @apply list-disc pl-6 my-4; +} + +.prose ol { + @apply list-decimal pl-6 my-4; +} + +.prose a { + @apply text-primary hover:text-primary-dark underline; +} + +.prose blockquote { + @apply border-l-4 border-secondary pl-4 italic my-4; +} diff --git a/frontend/src/app/layout.tsx b/frontend/src/app/layout.tsx new file mode 100644 index 0000000000000000000000000000000000000000..b37b9755915f6c2c033e1bfe06f9a99c4562f22c --- /dev/null +++ b/frontend/src/app/layout.tsx @@ -0,0 +1,34 @@ +import type { Metadata } from "next"; +import { Inter } from 'next/font/google'; +import "./globals.css"; +import Head from 'next/head'; + +const inter = Inter({ + subsets: ['latin'], + display: 'swap', + variable: '--font-inter', +}); + +export const metadata: Metadata = { + title: "VedaMD - Sri Lankan Health Guidelines Assistant", + description: "Get trusted clinical answers based on Sri Lankan health guidelines", + icons: { + icon: '/favicon.ico', + }, +}; + +export default function RootLayout({ + children, +}: { + children: React.ReactNode +}) { + return ( + + + + + + {children} + + ); +} diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..26ee67eccb9fa4213584e83e8e3aacbee71358ec --- /dev/null +++ b/frontend/src/app/page.tsx @@ -0,0 +1,241 @@ +"use client"; + +import { useState, useRef, useEffect, FormEvent, FC } from 'react'; +import ReactMarkdown from 'react-markdown'; +import remarkGfm from 'remark-gfm'; +import clsx from 'clsx'; + +// --- TYPE DEFINITIONS --- +interface Message { + role: 'user' | 'assistant'; + content: string; +} + +// --- SVG ICONS --- +const VedaMDLogo: FC = () => ( +
+
+ + + +
+

VedaMD

+
+); + +const SettingsIcon: FC = () => ( + + + +); + +const ArrowRightIcon: FC = () => ( + + + +); + +// --- UI COMPONENTS --- +const Header: FC = () => ( +
+ + +
+); + +const WelcomeScreen: FC<{ onTemplateClick: (query: string) => void }> = ({ onTemplateClick }) => { + const templates = [ + "What is the recommended antibiotic regimen for puerperal sepsis according to national guidelines?", + "What are the steps for active management of the third stage of labor (AMTSL)", + ]; + + return ( +
+
+

+ Welcome to VedaMD +

+

+ Get trusted clinical answers based on Sri Lankan health guidelines +

+
+ {templates.map((query) => ( + + ))} +
+
+
+ ); +}; + +const ChatMessage: FC<{ message: Message }> = ({ message }) => { + const isUser = message.role === 'user'; + + return ( +
+
+
+ {isUser ? 'U' : 'V'} +
+
+
+
+ + {message.content} + +
+
+
+
+
+ ); +}; + +const ChatForm: FC<{ + input: string; + setInput: (value: string) => void; + handleSubmit: (e: FormEvent) => void; + isLoading: boolean; +}> = ({ input, setInput, handleSubmit, isLoading }) => ( +
+
+