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| * [x] Initialize new Hugging Face Space with Gradio SDK 5.x | |
| * Add `mcp-server-track` tag in `README.md` | |
| * [ ] Write Python function `symptom_to_diagnosis(symptom_text)` | |
| * Use OpenAI or Anthropic API to generate JSON | |
| * Format prompt to request JSON output | |
| * Parse model response into Python dict | |
| * Handle JSON formatting quirks (trim extra text, use `json.loads`) | |
| * Implement fallback rule-based mapping for demo cases | |
| * [ ] Test `symptom_to_diagnosis` function | |
| * Input common symptom examples and combinations | |
| * Verify relevance and correctness of ICD codes and diagnoses | |
| * Tweak prompt to improve specificity and JSON validity | |
| * [ ] Define confidence score methodology | |
| * Decide whether to use model’s self-reported scores or rank order as proxy | |
| * Document how confidence is calculated and interpreted | |
| * [ ] Integrate function into Gradio Blocks interface | |
| * Use `gr.Interface` or `gr.ChatInterface` to accept symptom text input and display JSON output | |
| * Configure Gradio app metadata to expose MCP endpoint | |
| * [ ] Build demonstration client or script (optional) | |
| * Create minimal client using `gradio.Client` or `requests` to call Space’s prediction API | |
| * Alternatively, build a second Gradio Space as a simple chatbot that calls the MCP tool | |
| * Prepare screen recording showing AI agent (e.g., Claude Desktop) calling the MCP endpoint with example query | |
| * [ ] Update `README.md` documentation | |
| * Describe tool functionality and usage examples | |
| * Include `mcp-server-track` tag, link to video or client demo | |
| * List technologies used (e.g., “OpenAI GPT-4 API for symptom→ICD mapping”) | |
| * [ ] Configure OpenAI/Anthropic API usage | |
| * Use cheaper models (e.g., GPT-3.5) during development | |
| * Reserve GPT-4 or Claude-2 for final demo queries to conserve credits | |
| * [ ] Evaluate Hugging Face / Mistral credits for alternative inference | |
| * Identify open ICD-10 prediction models on HF Inference API (e.g., `AkshatSurolia/ICD-10-Code-Prediction`) | |
| * Consider running open-source models on Mistral if time allows | |
| * [ ] Plan Modal Labs usage for cloud compute (optional) | |
| * Pre-compute ICD-10 embeddings in Modal job if semantic search is added | |
| * Host backend microservice or Gradio app on Modal if HF Space resources are insufficient | |
| * [ ] Reserve Nebius or Hyperbolic Labs credits for GPU-intensive tasks (if needed) | |
| * Spin up GPU instance to host or fine-tune open-source model only if HF Space times out | |
| * [ ] Consider LlamaIndex integration for retrieval-augmented generation (bonus) | |
| * Load ICD-10 dataset into LlamaIndex and test semantic search for candidate codes | |
| * Implement minimal index of common diagnoses for demo if time permits | |
| * [ ] Record and document final demo | |
| * Capture symptom input, MCP tool invocation, and JSON output in a short video | |
| * Host video link in `README.md` | |