Update README.md

README.md

@@ -12,3 +12,47 @@ short_description: Clinical NER, Anatomy Detection, and POS Tagging with Gradio
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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Clinical NER, Anatomy Detection, and POS Tagging
+
+This Gradio application provides Named Entity Recognition (NER) for clinical text, anatomy detection, and Part-of-Speech (POS) tagging using state-of-the-art transformer models.
+
+## Features
+
+- **Clinical NER**: Extract medical entities (diseases, symptoms, treatments, etc.) from clinical text
+- **Anatomy Detection**: Identify anatomical terms in medical text
+- **POS Tagging**: Part-of-speech tagging for linguistic analysis
+- **Multiple Output Formats**: Get results in human-readable format or Prolog facts
+- **Combined Analysis**: Run all three analyses simultaneously
+
+## Models Used
+
+- **Clinical NER**: `samrawal/bert-base-uncased_clinical-ner`
+- **Anatomy Detection**: `OpenMed/OpenMed-NER-AnatomyDetect-BioPatient-108M`
+- **POS Tagging**: spaCy `en_core_web_sm`
+
+## Usage
+
+The app provides four tabs:
+
+1. **Clinical NER**: Extract clinical entities from medical text
+2. **Anatomy Detection**: Detect anatomical terms
+3. **POS Tagging**: Analyze part-of-speech tags
+4. **Combined Analysis**: Run all analyses at once
+
+Each tab supports:
+- Basic format: Human-readable output with entity highlighting
+- Prolog format: Structured facts for logic programming
+
+## Example
+
+Input:
+```
+Patient presents with pain in the left ventricle and elevated cardiac enzymes. The heart shows signs of inflammation.
+```
+
+Output includes detected medical conditions, anatomical structures, and linguistic analysis.
+
+## Based On
+
+This is a Gradio version of the clinical-ner FastAPI application, converted for easier demonstration and interaction.