import spaces import io import os import torch from PIL import Image import gradio as gr from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig title = """# Welcome to🌟Tonic's CheXRay⚕⚛ ! You can use this ZeroGPU Space to test out the current model [StanfordAIMI/CheXagent-8b](https://huggingface.co/StanfordAIMI/CheXagent-8b). CheXRay⚕⚛ is fine tuned to analyze chest x-rays with a different and generally better results than other multimodal models. You can also useCheXRay⚕⚛ by cloning this space. 🧬🔬🔍 Simply click here: Duplicate Space ### How To use Upload a medical image and enter a prompt to receive an AI-generated analysis. simply upload an image with the right prompt (coming soon!) and anaylze your Xray ! Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻 [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to 🌟 [DataTonic](https://github.com/Tonic-AI/DataTonic) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗 """ device = "cuda" dtype = torch.float16 processor = AutoProcessor.from_pretrained("StanfordAIMI/CheXagent-8b", trust_remote_code=True) generation_config = GenerationConfig.from_pretrained("StanfordAIMI/CheXagent-8b") model = AutoModelForCausalLM.from_pretrained("StanfordAIMI/CheXagent-8b", torch_dtype=dtype, trust_remote_code=True) @spaces.GPU def generate(image, prompt): if hasattr(image, "read"): image = Image.open(io.BytesIO(image.read())).convert("RGB") else: image = image images = [image] inputs = processor(images=images, text=f" USER: {prompt} ASSISTANT: ", return_tensors="pt").to(device=device, dtype=dtype) output = model.generate(**inputs, generation_config=generation_config)[0] response = processor.tokenizer.decode(output, skip_special_tokens=True) return response with gr.Blocks() as demo: gr.Markdown(title) with gr.Accordion("Custom Prompt Analysis"): with gr.Row(): image_input_custom = gr.Image(type="pil") prompt_input_custom = gr.Textbox(label="Enter your custom prompt") generate_button_custom = gr.Button("Generate") output_text_custom = gr.Textbox(label="Response") def custom_generate(image, prompt): if isinstance(image, str) and os.path.exists(image): with open(image, 'rb') as file: return generate(file, prompt) else: return generate(image, prompt) generate_button_custom.click(fn=custom_generate, inputs=[image_input_custom, prompt_input_custom], outputs=output_text_custom) example_prompt = "65 y/m Chronic cough and weight loss x 6 months. Chest X-rays normal. Consulted multiple pulmonologists with not much benefit. One wise pulmonologist thinks of GERD and sends him to the Gastro department. Can you name the classical finding here?" example_image_path = os.path.join(os.path.dirname(__file__), "hegde.jpg") gr.Examples( examples=[[example_image_path, example_prompt]], inputs=[image_input_custom, prompt_input_custom], outputs=[output_text_custom], fn=custom_generate, cache_examples=True ) with gr.Accordion("Anatomical Feature Analysis"): anatomies = [ "Airway", "Breathing", "Cardiac", "Diaphragm", "Everything else (e.g., mediastinal contours, bones, soft tissues, tubes, valves, and pacemakers)" ] with gr.Row(): image_input_feature = gr.Image(type="pil") prompt_select = gr.Dropdown(label="Select an anatomical feature", choices=anatomies) generate_button_feature = gr.Button("Analyze Feature") output_text_feature = gr.Textbox(label="Response") generate_button_feature.click(fn=lambda image, feature: generate(image, f'Describe "{feature}"'), inputs=[image_input_feature, prompt_select], outputs=output_text_feature) with gr.Accordion("Common Abnormalities Analysis"): common_abnormalities = ["Lung Nodule", "Pleural Effusion", "Pneumonia"] with gr.Row(): image_input_abnormality = gr.Image(type="pil") abnormality_select = gr.Dropdown(label="Select a common abnormality", choices=common_abnormalities) generate_button_abnormality = gr.Button("Analyze Abnormality") output_text_abnormality = gr.Textbox(label="Response") generate_button_abnormality.click(fn=lambda image, abnormality: generate(image, f'Analyze for "{abnormality}"'), inputs=[image_input_abnormality, abnormality_select], outputs=output_text_abnormality) demo.launch()