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import gradio as gr |
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import matplotlib.pyplot as plt |
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import io |
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import numpy as np |
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import base64 |
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from PIL import Image |
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import requests |
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import json |
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def get_image_data(plt): |
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buf = io.BytesIO() |
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plt.savefig(buf, format='PNG') |
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buf.seek(0) |
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img = Image.open(buf) |
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return img |
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def execute_code(code): |
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exec(code) |
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return get_image_data(plt) |
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def gpt_inference(base_url, model, openai_key, prompt): |
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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.' |
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headers = { |
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'Content-Type': 'application/json', |
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'Authorization': f'Bearer {openai_key}' |
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} |
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data = { |
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"model": model, |
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"messages": [ |
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{ |
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"role": "system", |
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"content": "You are a helpful assistant." |
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}, |
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{ |
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"role": "user", |
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"content": newprompt |
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} |
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] |
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} |
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response = requests.post(f"{base_url}/v1/chat/completions", headers=headers, data=json.dumps(data)) |
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print(f"xxxxxx: {response.text}") |
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response_json = response.json() |
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code = response_json['choices'][0]['message']['content'].strip() |
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img = execute_code(code) |
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return img |
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iface = gr.Interface( |
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fn=gpt_inference, |
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inputs=["text", gr.inputs.Dropdown(choices=["gpt3.5-turbo", "gpt4"], label="Model"), "text", "text"], |
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outputs=gr.outputs.Image(type="pil"), |
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input_labels=["Base URL", "Model", "OpenAI Key","Prompt"] |
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) |
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iface.launch() |
