app.py

import spaces
import io
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: <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> 

### How To use

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: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [Poly](https://github.com/tonic-ai/poly) 🤗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):
    # Convert the uploaded file to an image and process
    image = Image.open(io.BytesIO(image.read())).convert("RGB")
    images = [image]

    # Prepare inputs
    inputs = processor(images=images, text=f" USER: <s>{prompt} ASSISTANT: <s>", return_tensors="pt").to(device=device, dtype=dtype)

    # Generate the findings
    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("# AI Medical Image Analysis")
    gr.Markdown("Upload a medical image and enter a prompt to receive an AI-generated analysis.")

    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type="file")
            prompt_input = gr.Textbox(label="Prompt")
        with gr.Column():
            output_text = gr.Textbox(label="Response")

    generate_button = gr.Button("Generate")
    generate_button.click(fn=generate, inputs=[image_input, prompt_input], outputs=output_text)

demo.launch()