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: 
### 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 👻 [](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
example_images = ["00000174_003.png", "00006596_000.png", "00006663_000.png",
"00012976_002.png", "00018401_000.png", "00019799_000.png"]
processor = AutoProcessor.from_pretrained("liuhaotian/llava-v1.6-mistral-7b", trust_remote_code=True)
generation_config = GenerationConfig.from_pretrained("liuhaotian/llava-v1.6-mistral-7b")
# model = AutoModelForCausalLM.from_pretrained("StanfordAIMI/CheXagent-8b", torch_dtype=dtype, trust_remote_code=True)
@spaces.GPU
def generate(image, prompt):
model = AutoModelForCausalLM.from_pretrained("liuhaotian/llava-v1.6-mistral-7b", torch_dtype=dtype, trust_remote_code=True).to(device)
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", open=False):
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)
custom_prompt_examples = [
[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:"]
for img in example_images
]
# 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")
with gr.Accordion("Examples", open=False):
gr.Examples(
examples=custom_prompt_examples,
inputs=[image_input_custom, prompt_input_custom],
outputs=[output_text_custom],
fn=custom_generate,
cache_examples=True
)
with gr.Accordion("Anatomical Feature Analysis", open=False):
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)
anatomical_feature_examples = [
[os.path.join(os.path.dirname(__file__), img), "Airway"]
for img in example_images
]
with gr.Accordion("Examples", open=False):
gr.Examples(
examples=anatomical_feature_examples,
inputs=[image_input_feature, prompt_select],
outputs=[output_text_feature],
fn=lambda image, feature: generate(image, f'Describe "{feature}"'),
cache_examples=True
)
with gr.Accordion("Common Abnormalities Analysis", open=False):
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)
common_abnormalities_examples = [
[os.path.join(os.path.dirname(__file__), img), "Lung Nodule"]
for img in example_images
]
with gr.Accordion("Examples", open=False):
gr.Examples(
examples=common_abnormalities_examples,
inputs=[image_input_abnormality, abnormality_select],
outputs=[output_text_abnormality],
fn=lambda image, abnormality: generate(image, f'Analyze for "{abnormality}"'),
cache_examples=True
)
demo.launch()