app.py

import os
import base64
import gradio as gr
from gradio_client import Client, file
import time
import json
import threading

MODEL_NAME = "QWEN"
client_chat = os.environ.get("CHAT_URL")
client_vl = os.environ.get("VL_URL")
def read(filename):
    with open(filename) as f:
        data = f.read()
    return data
    
SYS_PROMPT = read('system_prompt.txt')

DESCRIPTION = '''
<div>
<h1 style="text-align: center;">家庭医生demo</h1>
<p>🩺一个帮助您分析症状和检验报告的家庭医生(AI诊疗助手)。</p>
<p>🔎 选择您需要咨询的科室医生，在输入框中输入症状描述或者体检信息等；您也可以在图片框中上传检测报告图。</p>
<p>🦕 请注意生成信息可能不准确，且不具备任何实际参考价值，如有需要请联系专业医生。</p>
</div>
'''


css = """
h1 {
    text-align: center;
    display: block;
}
footer {
    display:none !important
}
"""


LICENSE = '采用 ' + MODEL_NAME + ' 模型'


result = "等待分析..."
json_path = ""

def process_text(text_input, unit):
    client = Client(client_chat)
    print(client.view_api())
    job = client.submit(
        query=str(text_input),
        history=None,
        system=f"You are a experienced {unit} doctor AI assistant." + SYS_PROMPT,
        api_name="/model_chat"
    )
    response = job.result()
    print(response)
    result = response[1][0][1]
    return result

def process_image(image_input, unit):
    global result, json_path
    if image_input is not None:
        image = str(image_input)
        print(image)
        #with open(image_input, "rb") as f:
        #   base64_image = base64.b64encode(f.read()).decode("utf-8")
        client = Client(client_vl)
        print(client.view_api())
        prompt = f" You are a experienced {unit} doctor AI assistant." + SYS_PROMPT + "Help me understand what is in this picture and analysis."
        
        res5 = client.predict(
            "",
            image,
            fn_index=5
        )
        print(res5)
        
        res0 = client.predict(
            res5,
            prompt,
            fn_index=0
        )
        
        print(res0)
        json_path = res0

        def update():
            job = client.submit(
                    json_path,
                    fn_index=1         
            )
            response = job.result(timeout=60)
            with open(response, 'r') as f:
                data = json.load(f)
            print(data)
            result = data[-1][1]
            
        threading.Thread(target=update).start()

        return "等待分析..."

def fetch_result():
    return result


def main(text_input="", image_input=None, unit=""):
    if text_input and image_input is None:
        return process_text(text_input, unit)
    elif image_input is not None:
        return process_image(image_input, unit)

with gr.Blocks(css=css, title="家庭医生AI助手") as iface:
    with gr.Accordion(""):
        gr.Markdown(DESCRIPTION)
        unit = gr.Dropdown(label="🩺科室", value='中医科', elem_id="units",
                            choices=["中医科", "内科", "外科", "妇产科",  "儿科", \
                                     "五官科", "男科", "皮肤性病科", "传染科", "精神心理科", \
                                        "整形美容科", "营养科", "生殖中心", "麻醉医学科", "医学影像科", \
                                            "骨科", "肿瘤科", "急诊科", "检验科"])
    with gr.Row():
        output_box = gr.Markdown(value=result, every=10, label="分析", fn=fetch_result)
        output_box.change(update)
    with gr.Row():
        image_input = gr.Image(type="filepath", label="上传图片")  # Create an image upload button
        text_input = gr.Textbox(label="输入")  # Create a text input box
    with gr.Row():
        submit_btn = gr.Button("🚀 确认")  # Create a submit button
        clear_btn = gr.ClearButton([output_box, image_input, text_input], value="🗑️ 清空") # Create a clear button


    # Set up the event listeners
    submit_btn.click(main, inputs=[text_input, image_input, unit], outputs=output_box)
    gr.Markdown(LICENSE)
    
#gr.close_all()

iface.queue().launch(show_api=False)  # Launch the Gradio interface