app.py

import gradio as gr
import matplotlib.pyplot as plt
import io
import numpy as np
from PIL import Image
import requests
import json
import re



# 执行 Python 代码并生成图像
def execute_code(code):
    namespace = {}
    exec(code, namespace)
    fig = namespace.get('fig')  # Assume the code generates a matplotlib figure named 'fig'
    if fig:
        img = get_image_data(fig)
        
        img_byte_arr = io.BytesIO()
        img.save(img_byte_arr, format='PNG')
        img_byte_arr = img_byte_arr.getvalue()
        img_b64 = base64.b64encode(img_byte_arr).decode('utf-8')
        
        return img_b64
    else:
        raise ValueError("The code did not generate a matplotlib figure named 'fig'")

def gpt_inference(base_url, model, openai_key, prompt):
    
    newprompt = f'Write Python code that does the following: \n\n{prompt}\n\nNote, the code is going to be executed in a Jupyter Python kernel. The code should create a matplotlib figure and assign it to a variable named "fig". The "fig" variable will be used for further processing.\n\nLast instruction, and this is the most important, just return code. No other outputs, as your full response will directly be executed in the kernel.'

    data = {
      "model": model,
      "messages": [
        {
          "role": "user",
          "content": newprompt
        }
      ],
      "temperature": 0.7,
    }

    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {openai_key}",
    }

    response = requests.post(f"{base_url}/v1/chat/completions", headers=headers, data=json.dumps(data))
    
    code = response.json()["choices"][0]["message"]["content"]
    print(f"code:{code}")
    img = execute_code(code)
    return img

iface = gr.Interface(
    fn=gpt_inference, 
    inputs=["text", gr.inputs.Dropdown(choices=["gpt3.5-turbo", "gpt4"], label="Model"), "text", "text"], 
    outputs=gr.Image(),
    input_labels=["Base URL", "Model", "OpenAI Key","Prompt"]
)
iface.launch()