app.py

import gradio as gr
import openai
from openai import OpenAI
import os
import base64

# Set API key and organization ID from environment variables
api_key = os.environ.get("OPENAI_API_KEY")
base_url = os.environ.get("OPENAI_API_BASE")
client = OpenAI(api_key=api_key, base_url=base_url)
sys_prompt = read('system_prompt.txt')

# Define the model to be used
MODEL = "gpt-4o"

def process_text(text_input,unit):
    if text_input:
        completion = client.chat.completions.create(
            model=MODEL,
            messages=[
                {"role": "system", "content": f" You are a helpful {unit} doctor." + sys_prompt},
                {"role": "user", "content": f"Hello! Could you solve {text_input}?"}
            ]
        )
        return "Assistant: " + completion.choices[0].message.content

def process_image(image_input,unit):
    if image_input is not None:
        with open(image_input.name, "rb") as f:
            base64_image = base64.b64encode(f.read()).decode("utf-8")
        response = client.chat.completions.create(
            model=MODEL,
            messages=[
                {"role": "system", "content": f" You are a helpful {unit} doctor." + sys_prompt},
                {"role": "user", "content": [
                    {"type": "text", "text": "Help me understand what this image"},
                    {"type": "image_url", "image_url": {
                        "url": f"data:image/png;base64,{base64_image}"}
                    }
                ]}
            ],
            temperature=0.0,
        )
        return response.choices[0].message.content

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() as iface:
    with gr.Accordion(""):
        gr.Markdown("""医学报告助手 GPT-4o""")
        unit = gr.Dropdown(label="🩺科室", value='内科', elem_id="units",
                            choices=["内科", "外科", "妇产科", "男科", "儿科", \
                                     "五官科", "肿瘤科", "皮肤性病科", "中医科", "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", \
                                            "急诊科", "检验科"])
    with gr.Row():
        output_box = gr.Textbox(label="Output")  # Create an output textbox
    with gr.Row():
        image_btn = 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, value="🗑️ 清空") # Create a clear button

    # Set up the event listeners
    submit_btn.click(main, inputs=[text_input, image_btn, unit], outputs=output_box)

iface.launch()  # Launch the Gradio interface