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import spaces |
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import io |
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import os |
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import torch |
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from PIL import Image |
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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig |
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title = """# Welcome to🌟Tonic's CheXRay⚕⚛ ! |
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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. |
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You can also useCheXRay⚕⚛ by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/CheXRay?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> |
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### How To use |
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Upload a medical image and enter a prompt to receive an AI-generated analysis. |
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simply upload an image with the right prompt (coming soon!) and anaylze your Xray ! |
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Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻 [](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 🤗 |
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""" |
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device = "cuda" |
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dtype = torch.float16 |
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example_images = ["00000174_003.png", "00006596_000.png", "00006663_000.png", |
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"00012976_002.png", "00018401_000.png", "00019799_000.png"] |
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processor = AutoProcessor.from_pretrained("liuhaotian/llava-v1.6-mistral-7b", trust_remote_code=True) |
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generation_config = GenerationConfig.from_pretrained("liuhaotian/llava-v1.6-mistral-7b") |
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@spaces.GPU |
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def generate(image, prompt): |
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model = AutoModelForCausalLM.from_pretrained("liuhaotian/llava-v1.6-mistral-7b", torch_dtype=dtype, trust_remote_code=True).to(device) |
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if hasattr(image, "read"): |
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image = Image.open(io.BytesIO(image.read())).convert("RGB") |
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else: |
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image = image |
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images = [image] |
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inputs = processor(images=images, text=f" USER: <s>{prompt} ASSISTANT: <s>", return_tensors="pt").to(device=device, dtype=dtype) |
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output = model.generate(**inputs, generation_config=generation_config)[0] |
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response = processor.tokenizer.decode(output, skip_special_tokens=True) |
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return response |
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with gr.Blocks() as demo: |
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gr.Markdown(title) |
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with gr.Accordion("Custom Prompt Analysis", open=False): |
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with gr.Row(): |
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image_input_custom = gr.Image(type="pil") |
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prompt_input_custom = gr.Textbox(label="Enter your custom prompt") |
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generate_button_custom = gr.Button("Generate") |
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output_text_custom = gr.Textbox(label="Response") |
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def custom_generate(image, prompt): |
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if isinstance(image, str) and os.path.exists(image): |
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with open(image, 'rb') as file: |
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return generate(file, prompt) |
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else: |
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return generate(image, prompt) |
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generate_button_custom.click(fn=custom_generate, inputs=[image_input_custom, prompt_input_custom], outputs=output_text_custom) |
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custom_prompt_examples = [ |
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[os.path.join(os.path.dirname(__file__), img), "You are an expert X-Ray Analyst, describe this chest x-ray in detail focussing on the lung condition:"] |
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for img in example_images |
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] |
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with gr.Accordion("Examples", open=False): |
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gr.Examples( |
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examples=custom_prompt_examples, |
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inputs=[image_input_custom, prompt_input_custom], |
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outputs=[output_text_custom], |
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fn=custom_generate, |
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cache_examples=True |
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) |
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with gr.Accordion("Anatomical Feature Analysis", open=False): |
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anatomies = [ |
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"Airway", "Breathing", "Cardiac", "Diaphragm", |
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"Everything else (e.g., mediastinal contours, bones, soft tissues, tubes, valves, and pacemakers)" |
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] |
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with gr.Row(): |
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image_input_feature = gr.Image(type="pil") |
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prompt_select = gr.Dropdown(label="Select an anatomical feature", choices=anatomies) |
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generate_button_feature = gr.Button("Analyze Feature") |
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output_text_feature = gr.Textbox(label="Response") |
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generate_button_feature.click(fn=lambda image, feature: generate(image, f'Describe "{feature}"'), inputs=[image_input_feature, prompt_select], outputs=output_text_feature) |
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anatomical_feature_examples = [ |
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[os.path.join(os.path.dirname(__file__), img), "Airway"] |
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for img in example_images |
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] |
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with gr.Accordion("Examples", open=False): |
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gr.Examples( |
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examples=anatomical_feature_examples, |
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inputs=[image_input_feature, prompt_select], |
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outputs=[output_text_feature], |
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fn=lambda image, feature: generate(image, f'Describe "{feature}"'), |
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cache_examples=True |
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) |
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with gr.Accordion("Common Abnormalities Analysis", open=False): |
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common_abnormalities = ["Lung Nodule", "Pleural Effusion", "Pneumonia"] |
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with gr.Row(): |
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image_input_abnormality = gr.Image(type="pil") |
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abnormality_select = gr.Dropdown(label="Select a common abnormality", choices=common_abnormalities) |
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generate_button_abnormality = gr.Button("Analyze Abnormality") |
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output_text_abnormality = gr.Textbox(label="Response") |
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generate_button_abnormality.click(fn=lambda image, abnormality: generate(image, f'Analyze for "{abnormality}"'), inputs=[image_input_abnormality, abnormality_select], outputs=output_text_abnormality) |
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common_abnormalities_examples = [ |
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[os.path.join(os.path.dirname(__file__), img), "Lung Nodule"] |
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for img in example_images |
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] |
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with gr.Accordion("Examples", open=False): |
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gr.Examples( |
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examples=common_abnormalities_examples, |
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inputs=[image_input_abnormality, abnormality_select], |
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outputs=[output_text_abnormality], |
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fn=lambda image, abnormality: generate(image, f'Analyze for "{abnormality}"'), |
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cache_examples=True |
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) |
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demo.launch() |
