README.md

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# Vitals Interpreter Model (Fine-Tuned LLM)

## Project Overview
This project implements a fine-tuned transformer model that interprets basic human vital signs and generates structured health guidance.

The model takes numerical vitals as input and produces a concise, human-readable output consisting of:
- Health status classification
- Suggested action/advice

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##  Objective
To build a lightweight, efficient AI system that:
- Understands structured vital inputs
- Classifies health condition into categories
- Generates consistent and controlled responses

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##  Model Details
- **Base Model:** t5-small  
- **Architecture:** Encoder-Decoder Transformer  
- **Fine-Tuning Type:** Supervised Fine-Tuning (SFT)  
- **Framework:** Hugging Face Transformers  

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##  Input Format
interpret vitals -> heart rate X, blood pressure Y/Z, temperature T


### Example:
interpret vitals -> heart rate 125, blood pressure 150/95, temperature 100


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##  Output Format
Status: <Normal | High | Low | Critical> | Advice: <short guidance>


### Example Output:
Status: High | Advice: Monitor and consult doctor


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##  Dataset
- **Type:** Synthetic dataset  
- **Size:** ~30–50 samples  
- **Design Approach:**  
  - Based on medically accepted ranges of vital signs  
  - Balanced across categories:
    - Normal
    - High
    - Low
    - Critical  

### Why Synthetic Data?
Due to lack of publicly available labeled text datasets for this task, a controlled dataset was generated to:
- Ensure consistency in output format  
- Improve learning efficiency  
- Avoid noisy or unstructured data  

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##  Training Configuration
- **Epochs:** 20–30  
- **Batch Size:** 2–4  
- **Learning Rate:** 5e-5  
- **Max Sequence Length:** 64  
- **Tokenizer:** AutoTokenizer (T5)

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##  Evaluation
### Method:
- Manual testing with unseen inputs  
- Verification of:
  - Correct classification (Normal / High / Low / Critical)  
  - Proper output structure  
  - Relevance of advice  

### Sample Predictions:
| Input | Output |
|------|--------|
| HR: 125, BP: 150/95, Temp: 100 | Status: High \| Advice: Monitor and consult doctor |
| HR: 72, BP: 120/80, Temp: 98.6 | Status: Normal \| Advice: No action needed |
| HR: 140, BP: 170/110, Temp: 103 | Status: Critical \| Advice: Emergency care required |

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##  How to Use

### Installation
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

Author:
Archee Sinha
B.Tech CSE (AI)
ABES Institute of Technology