app.py

import gradio as gr
import matplotlib.pyplot as plt
import io
import numpy as np
import base64
from PIL import Image
import requests
import json

# 将图像转换为 base64，以便在 gradio 中显示
def get_image_data(plt):
    buf = io.BytesIO()
    plt.savefig(buf, format='PNG')
    buf.seek(0)
    img = Image.open(buf)
    return img

# 执行 Python 代码并生成图像
def execute_code(code):
    exec(code)
    return get_image_data(plt)

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.\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))
    print(f"text: {response.text}")

    def extract_code(text):
        # Match triple backtick blocks first
        triple_match = re.search(r'```(?:\w+\n)?(.+?)```', text, re.DOTALL)
        if triple_match:
            return triple_match.group(1).strip()
        else:
            # If no triple backtick blocks, match single backtick blocks
            single_match = re.search(r'`(.+?)`', text, re.DOTALL)
            if single_match:
                return single_match.group(1).strip()
        # If no code blocks found, return original text
        return text

    if response.status_code != 200:
        return "Error: " + response.text, 500
    
    code = extract_code(response.json()["choices"][0]["message"]["content"])
    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.outputs.Image(type="pil"),
    input_labels=["Base URL", "Model", "OpenAI Key","Prompt"]
)
iface.launch()