mvp checklist

docs/plans/mvp_checklist.md

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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`