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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.'
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {openai_key}'
}
data = {
"model": model,
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": newprompt
}
]
}
response = requests.post(f"{base_url}/v1/chat/completions", headers=headers, data=json.dumps(data))
response_json = response.json()
code = response_json['choices'][0]['message']['content'].strip()
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() |