Sai1290/X-Rays-LLM

🩺 Medical Image QA Model — Vision-Language Expert

This is a multimodal model fine-tuned for image-based biomedical question answering and captioning, based on scientific figures from PMC Open Access subset. The model takes a biomedical image and an optional question, then generates an expert-level description or answer.

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🧠 Model Architecture

• Base Model: FastVisionModel (e.g., a BLIP, MiniGPT4, or Flamingo-style model)
• Backbone: Vision encoder + LLM (supports apply_chat_template for prompt formatting)
• Trained for Tasks:
  • Biomedical image captioning
  • Image-based question answering

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🧬 Dataset

• Name: axiong/pmc_oa_demo
• Samples: 100 samples (demo)
• Fields:
  • image: Biomedical figure (from scientific paper)
  • caption: Expert-written caption
  • question: (optional) User query about the image
  • answer: (optional) Expert response

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🧪 Example Usage

🔍 Visual Inference with Instruction & Optional Question

from transformers import TextStreamer
import matplotlib.pyplot as plt

# Prepare model and tokenizer
FastVisionModel.for_inference(model)

sample = dataset[10]
image = sample["image"]
caption = sample.get("caption", "")

# Display the image
plt.imshow(image)
plt.axis('off')
plt.title("Input Image")
plt.show()

instruction = "You are an expert Doctor. Describe accurately what you see in this image."
question = input("Please enter your question about the image (or press Enter to skip): ").strip()

# Build messages for the chat template
user_content = [
    {"type": "image", "image": image},
    {"type": "text", "text": instruction}
]
if question:
    user_content.append({"type": "text", "text": question})

messages = [{"role": "user", "content": user_content}]
input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

inputs = tokenizer(image, input_text, add_special_tokens=False, return_tensors="pt").to("cuda")
streamer = TextStreamer(tokenizer, skip_prompt=True)

_ = model.generate(
    **inputs,
    streamer=streamer,
    max_new_tokens=128,
    use_cache=True,
    temperature=1.5,
    min_p=0.1,
)

# Optional: display true caption for comparison
print("\nGround Truth Caption:\n", caption)