Update README.md

README.md

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 ---
-license: apache-2.0
-metrics:
-- bleu
+language: en
+license: mit
+tags:
+- vision
+- text-generation
+- medical
+- chest-xray
+- healthcare
+- multimodal
 pipeline_tag: image-to-text
 ---
+
+# 🩺 ChestX – Chest X-ray Report Generation (ViT-GPT2)
+
+This model generates **medical diagnostic reports from chest X-ray images**.  
+It was developed for the **TWESD Healthcare AI Competition 2024** as part of my final-year engineering project.  
+
+The architecture combines a **Vision Transformer (ViT)** for image encoding with **GPT-2** as the language decoder, forming an **encoder–decoder multimodal model**.  
+
+---
+
+## 📌 Model Description
+- **Architecture:** VisionEncoderDecoderModel (ViT + GPT-2)  
+- **Input:** Chest X-ray image  
+- **Output:** Text report describing findings  
+- **Framework:** PyTorch + Hugging Face Transformers  
+
+---
+
+## 💡 Intended Uses & Limitations
+✅ Intended for:
+- Research in **medical AI & multimodal learning**  
+- Exploring **vision-to-text generation**  
+- Educational and prototyping purposes  
+
+⚠️ Limitations:
+- Not intended for **real clinical diagnosis**  
+- Trained on a limited dataset (IU Chest X-ray), may not generalize to all populations  
+
+---
+
+## 🛠️ How to Use
+
+```python
+from transformers import VisionEncoderDecoderModel, AutoTokenizer, AutoFeatureExtractor
+from PIL import Image
+import torch
+
+# Load model and tokenizer
+model = VisionEncoderDecoderModel.from_pretrained("Molkaatb/ChestX").to("cuda")
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
+tokenizer.pad_token = tokenizer.eos_token
+feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
+
+# Example image
+image = Image.open("example_xray.png").convert("RGB")
+inputs = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
+
+# Generate report
+outputs = model.generate(inputs, max_length=512, num_beams=4)
+report = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(report)