Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+  - en
+pretty_name: Clinvar Annotations
+---
+
+part of 
+
+# 🧬 Genomic Reasoning Agent
+### LLM-driven agentic system for personal genomic variant interpretation
+
+[![HuggingFace](https://img.shields.io/badge/🤗_HuggingFace-Space-yellow)](https://huggingface.co/spaces/mioulin/genomic-reasoning-agent)
+[![Dataset](https://img.shields.io/badge/🤗_Dataset-genomic--qa-blue)](https://huggingface.co/datasets/mioulin/genomic-reasoning-qa)
+[![Model](https://img.shields.io/badge/🤗_Model-genomic--reasoning--llm-green)](https://huggingface.co/mioulin/genomic-reasoning-grpo)
+[![License: MIT](https://img.shields.io/badge/License-MIT-purple)](LICENSE)
+
+---
+
+## Overview
+
+This project builds a **multi-step reasoning agent** that interprets personal genomic data from 23andMe against biomedical knowledge databases (ClinVar, GWAS Catalog, gnomAD). The agent is trained with **GRPO** (Group Relative Policy Optimization) using fully verifiable reward signals — no human labelers needed.
+
+The core insight mirrors DeepSeek-R1's approach to mathematics: **genomic variant interpretation has verifiable ground truth** (ClinVar classifications, GWAS p-values, population frequencies), making it ideal for RL-based reasoning training.
+
+```
+23andMe SNPs (631K)
+        ↓
+  ClinVar Annotation          ← rs1801133 → MTHFR → Pathogenic
+        ↓
+ Tool-Using LLM Agent         ← 5 tools: lookup / scan / haplotype / stats / reward
+        ↓
+   GRPO Training Loop         ← verifiable reward from ClinVar ground truth
+        ↓
+  Reasoning Model             ← factual · calibrated · evidence-grounded
+        ↓
+   HF Spaces Demo             ← upload 23andMe → ask questions → reasoning trace
+```
+
+---
+
+## Motivation
+
+Standard LLMs hallucinate on genomic questions. This project trains a model that:
+
+- **Cites sources** (ClinVar review status, GWAS p-values, PubMed IDs)
+- **Shows reasoning chains** (mechanism → evidence → conclusion)
+- **Calibrates uncertainty** (risk factor ≠ diagnosis)
+- **Uses tools** to look up live databases rather than relying on memorised weights
+
+The training signal is 100% verifiable — reward is computed by checking responses against ClinVar annotations, not scored by humans.
+
+---
+
+## Repository Structure
+
+```
+genomic-reasoning-agent/
+├── genomic_pipeline.py              # Step 1: Parse 23andMe .txt → DataFrame
+├── clinvar_pipeline_full.py         # Step 2: Annotate variants via ClinVar API
+├── genomic_agent_huggingface.py     # Step 3: smolagents tool-using agent (5 tools)
+├── train_grpo.py                    # Step 4: GRPO training with TRL
+├── app.py                           # HF Spaces Gradio UI
+├── data/
+│   ├── clinvar_annotations.json     # 12 variants with full ClinVar metadata
+│   └── genomic_qa_dataset.json      # 36 Q&A pairs (3 task types × 12 variants)
+└── README.md
+```
+
+---
+
+## Pipeline: Step by Step
+
+### Step 1 — Parse 23andMe
+
+Reads the raw `.txt` export (Build GRCh37) into a DataFrame of 631,455 SNPs.
+
+```python
+from genomic_pipeline import parse_23andme
+df = parse_23andme("genome_23andme.txt")
+# → 631,455 SNPs across chromosomes 1–22, X, Y, MT
+# → 104,617 heterozygous (16.6%) | 522,909 homozygous (82.8%)
+```
+
+### Step 2 — ClinVar Annotation
+
+Queries NCBI E-utilities and GWAS Catalog for each rsID. Builds a Q&A dataset with verifiable answers.
+
+```python
+from clinvar_pipeline_full import query_clinvar_batch, build_qa_dataset
+annotations = query_clinvar_batch(rsids, email="your@email.com")
+qa_dataset  = build_qa_dataset(annotations, genome_df)
+# → 12 variants annotated
+# → 36 Q&A pairs: variant_interpretation / genotype_interpretation / pathway_reasoning
+```
+
+**Sample annotation:**
+
+| rsID | Gene | Genotype | Significance | Condition |
+|------|------|----------|-------------|-----------|
+| rs1801133 | MTHFR | GG | Pathogenic/Likely pathogenic | Homocystinuria |
+| rs429358 + rs7412 | APOE | TT / CC | risk factor | Alzheimer disease |
+| rs9939609 | FTO | AT | risk factor | Obesity |
+| rs762551 | CYP1A2 | AC | drug response | Caffeine metabolism |
+
+### Step 3 — Tool-Using Agent (smolagents)
+
+A `ToolCallingAgent` with 5 tools that plans multi-step queries across databases.
+
+```python
+from smolagents import ToolCallingAgent, InferenceClientModel
+from genomic_agent_huggingface import (
+    VariantLookupTool,    # rsID → ClinVar + genotype
+    GeneScannerTool,      # gene/trait → all patient variants
+    HaplotypeCallerTool,  # APOE ε2/ε3/ε4 from two SNPs
+    GenomeStatsTool,      # 631K SNPs overview
+    RewardEvaluatorTool,  # GRPO reward score (used during training)
+)
+model = InferenceClientModel("meta-llama/Llama-3.1-8B-Instruct")
+agent = ToolCallingAgent(tools=[...], model=model, max_steps=10)
+answer = agent.run("What is my APOE haplotype and what does it mean?")
+```
+
+**6-step reasoning trace for "Give me a genomic health summary":**
+
+```
+Step 1 [genome_stats]       → 631,455 SNPs | 16.6% heterozygous
+Step 2 [variant_lookup]     → rs1801133 | MTHFR | GG | Pathogenic
+Step 3 [call_haplotype]     → APOE ε3/ε3 | Neutral Alzheimer's risk
+Step 4 [scan_gene_variants] → dopamine: ANKK1 GG (risk), COMT GG (Val/Val)
+Step 5 [scan_gene_variants] → caffeine: CYP1A2 AC (intermediate), ADORA2A CT
+Step 6 [evaluate_reasoning] → reward: 0.93 / 1.00 (excellent)
+```
+
+### Step 4 — GRPO Training
+
+Trains the base LLM to reason better about genomic questions using reinforcement learning with verifiable rewards — no human annotation required.
+
+```python
+from train_grpo import train, TrainingConfig
+config = TrainingConfig(
+    model_name="meta-llama/Llama-3.1-8B-Instruct",
+    num_epochs=3,
+    num_generations=4,        # G: completions per question
+    beta=0.04,                # KL penalty
+    use_lora=True,
+)
+trainer = train(config)
+```
+
+**Reward function — 6 verifiable components:**
+
+| Component | Weight | Verifiable against |
+|-----------|--------|--------------------|
+| Factual accuracy | 0.35 | ClinVar clinical significance |
+| Condition coverage | 0.25 | ClinVar associated conditions |
+| Gene mention | 0.15 | ClinVar gene annotation |
+| Reasoning chain | 0.15 | Presence of causal language |
+| Uncertainty calibration | 0.05 | Hedging language |
+| Response completeness | 0.05 | Word count |
+
+**Training progression (simulated):**
+
+```
+untrained  reward=0.06  |█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░|
+epoch_1    reward=0.56  |████████████████░░░░░░░░░░░░░░|
+epoch_3    reward=0.71  |█████████████████████░░░░░░░░░|
+epoch_5    reward=0.93  |███████████████████████████░░░|
+epoch_10   reward=1.00  |██████████████████████████████|
+```
+
+**GRPO advantage formula:**
+
+```
+advantage_i = (reward_i − mean(rewards)) / std(rewards)
+No critic network. No value function. No human labeler.
+Just relative comparison within each group of G=4 completions.
+```
+
+---
+
+## Key Results from Real Genome Data
+
+Running the full pipeline on a real 23andMe export (Zalina Dezhina, v5 chip):
+
+| Variant | Gene | Genotype | Clinical Note |
+|---------|------|----------|---------------|
+| 🔴 rs1801133 | MTHFR | GG | Pathogenic — folate metabolism (p.Ala222Val) |
+| 🟡 rs9939609 | FTO | **AT** | Risk factor — obesity, 1 risk allele (40.4% population) |
+| 🧠 APOE | — | **ε3/ε3** | Neutral — most common haplotype, no elevated AD risk |
+| 💊 rs762551 | CYP1A2 | **AC** | Drug response — intermediate caffeine metabolizer |
+| 🟡 rs1800497 | ANKK1 | GG | Risk factor — reward pathway / DRD2 association |
+| 🟢 rs6265 | BDNF | CC | Benign (Val/Val) — better episodic memory |
+
+> ⚠️ This is a research and portfolio project, not medical advice.
+> All interpretations are for educational purposes only.
+
+---
+
+## Tech Stack
+
+| Layer | Technology |
+|-------|-----------|
+| Genome parsing | pandas, Python |
+| Variant annotation | NCBI E-utilities (ClinVar), EBI GWAS Catalog |
+| Agentic framework | [smolagents](https://github.com/huggingface/smolagents) (HuggingFace) |
+| RL training | [TRL](https://github.com/huggingface/trl) GRPOTrainer |
+| Fine-tuning | LoRA (PEFT) + 4-bit quantization (bitsandbytes) |
+| Base model | meta-llama/Llama-3.1-8B-Instruct |
+| Demo UI | Gradio (HF Spaces) |
+| Evaluation | Per-task reward breakdown, 3 task types |
+
+---
+
+## HuggingFace Deployment
+
+**Spaces (interactive demo):**
+```
+1. Create new Space → Gradio SDK
+2. Upload: genomic_agent_huggingface.py, app.py, data/
+3. Add secret: HF_TOKEN
+4. Upload your 23andMe .txt → ask questions in chat
+```
+
+**Model Hub (trained weights):**
+```python
+trainer.push_to_hub("mioulin/genomic-reasoning-llm")
+```
+
+**Dataset Hub:**
+```python
+from datasets import Dataset
+Dataset.from_list(qa_dataset).push_to_hub("mioulin/genomic-reasoning-qa")
+```
+
+---
+
+## Connection to ML Scientist Role
+
+This project was built to demonstrate the exact skills required for ML Scientist roles in AI×biology:
+
+- **Agentic systems** — 5-tool ToolCallingAgent with multi-step planning
+- **RL/RLHF training** — GRPO with verifiable reward, no human labelers
+- **Biomedical data integration** — ClinVar, GWAS Catalog, gnomAD, PubMed
+- **Evaluation framework** — 6-component reward breakdown across 3 task types
+- **Real scientific domain** — 631,455 SNPs from real 23andMe genome
+- **Reasoning over evidence** — multi-hop: SNP → gene → pathway → phenotype
+
+---
+
+## Running Locally
+
+```bash
+git clone https://huggingface.co/spaces/mioulin/genomic-reasoning-agent
+cd genomic-reasoning-agent
+pip install smolagents trl transformers accelerate peft datasets gradio
+# Annotate your genome
+python clinvar_pipeline_full.py \
+  --genome your_23andme.txt \
+  --output data/clinvar_annotations.json \
+  --email your@email.com
+# Run agent (requires HF token for model inference)
+export HF_TOKEN=hf_...
+python genomic_agent_huggingface.py
+# Train with GRPO (requires GPU)
+python train_grpo.py \
+  --model meta-llama/Llama-3.1-8B-Instruct \
+  --epochs 3 --lora --G 4
+```
+
+---
+
+## Author
+
+**Zalina Dezhina, PhD**  
+[![HuggingFace](https://img.shields.io/badge/🤗-mioulin-yellow)](https://huggingface.co/mioulin)
+
+## Citation
+
+```bibtex
+@misc{dezhina2026genomic,
+  title  = {Genomic Reasoning Agent: GRPO Training on Personal SNP Data},
+  author = {Dezhina, Zalina},
+  year   = {2026},
+  url    = {https://huggingface.co/spaces/mioulin/genomic-reasoning-agent}
+}
+```
+
+---
+
+## License
+
+MIT — research and educational use only.
+Not intended for clinical or medical decision-making.
+
+---
+
+*Built with 🧬 smolagents · TRL · HuggingFace · ClinVar · 23andMe*