Update README.md

README.md

@@ -8,4 +8,80 @@ pipeline_tag: image-to-text
 tags:
 - medical
 - retinal
----
+---
+
+
+# RetinalGPT: Large Language-and-Vision Assistant for Retinal Health 👁️
+
+
+**RetinalGPT** is a specialized multimodal vision-language model (VLM) based on the **LLaVA-v1.5** architecture. It is specifically engineered for the high-precision domain of **ophthalmology**, with a focus on interpreting retinal fundus photography and Optical Coherence Tomography (OCT) scans.
+
+---
+
+## 📌 Model Summary
+
+RetinalGPT bridges the gap between general-purpose VLMs and specialized ophthalmic diagnostics. By fine-tuning on a curated corpus of retinal image-text pairs, the model demonstrates advanced capabilities in identifying pathologies such as Diabetic Retinopathy (DR), Glaucoma, and Age-related Macular Degeneration (AMD).
+
+- **Base LLM:** Llama-7b 
+- **Vision Tower:** CLIP-ViT-L-14-336px
+- **Connector:** MLP Projection Layer
+- **Domain:** Ophthalmology / Retinal Imaging
+
+---
+
+## 🚀 Key Capabilities
+
+RetinalGPT is trained to perform complex visual reasoning tasks including:
+* **Automated Screening:** Grading Diabetic Retinopathy severity (Stage 0-4).
+* **Lesion Characterization:** Identifying and describing microaneurysms, hemorrhages, and exudates.
+* **Anatomical Mapping:** Precise description of the optic disc, cup-to-disc ratio, and foveal reflex.
+* **Clinical QA:** Engaging in multi-turn dialogues about specific clinical findings in a retinal scan.
+
+---
+
+## 💻 How to Use
+
+RetinalGPT follows the standard LLaVA inference pipeline. You will need the `llava` library installed.
+
+### Installation
+```bash
+pip install git+[https://github.com/haotian-liu/LLaVA.git](https://github.com/haotian-liu/LLaVA.git)
+Python Inference
+```
+
+```Python
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from PIL import Image
+import torch
+
+model_path = "your-username/retinalgpt"
+model_name = get_model_name_from_path(model_path)
+
+tokenizer, model, image_processor, context_len = load_pretrained_model(
+    model_path=model_path, 
+    model_base=None, 
+    model_name=model_name
+)
+
+# Prepare Image
+image = Image.open("fundus_sample.jpg")
+image_tensor = image_processor.preprocess(image, return_tensors='pt')['images'].half().cuda()
+
+prompt = "Can you describe this image?"
+
+input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
+
+# Generate Response
+with torch.inference_mode():
+    output_ids = model.generate(
+        input_ids,
+        images=image_tensor,
+        do_sample=True,
+        temperature=0.2,
+        max_new_tokens=512,
+        use_cache=True
+    )
+print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
+```