112 lines
4.7 KiB
Python
112 lines
4.7 KiB
Python
import gradio as gr
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import os
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import markdown, mdtex2html
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from predict import predict
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from show_math import convert as convert_math
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from config import proxies, WEB_PORT
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def find_free_port():
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import socket
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from contextlib import closing
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with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
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s.bind(('', 0))
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s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
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return s.getsockname()[1]
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PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
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initial_prompt = "Serve me as a writing and programming assistant."
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title_html = """<h1 align="center">ChatGPT 学术优化</h1>"""
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import logging
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os.makedirs('gpt_log', exist_ok=True)
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logging.basicConfig(filename='gpt_log/chat_secrets.log', level=logging.INFO, encoding='utf-8')
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print('所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log，请注意自我隐私保护哦！')
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from functional import get_functionals
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functional = get_functionals()
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def reset_textbox(): return gr.update(value='')
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def text_divide_paragraph(text):
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if '```' in text:
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# careful input
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return text
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else:
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# wtf input
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lines = text.split("\n")
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for i, line in enumerate(lines):
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if i!=0: lines[i] = "<p>"+lines[i].replace(" ", " ")+"</p>"
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text = "".join(lines)
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return text
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def markdown_convertion(txt):
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if ('$' in txt) and ('```' not in txt):
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math_config = {'mdx_math': {'enable_dollar_delimiter': True}}
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return markdown.markdown(txt,extensions=['fenced_code','tables']) + '<br><br>' + \
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markdown.markdown(convert_math(txt, splitParagraphs=False),extensions=['fenced_code','tables'])
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else:
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return markdown.markdown(txt,extensions=['fenced_code','tables'])
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def format_io(self,y):
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if y is None:
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return []
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i_ask, gpt_reply = y[-1]
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i_ask = text_divide_paragraph(i_ask) # 输入部分太自由，预处理一波
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y[-1] = (
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None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code','tables']),
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None if gpt_reply is None else markdown_convertion(gpt_reply)
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)
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return y
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gr.Chatbot.postprocess = format_io
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with gr.Blocks() as demo:
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gr.HTML(title_html)
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with gr.Row():
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with gr.Column(scale=2):
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chatbot = gr.Chatbot()
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chatbot.style(height=700)
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chatbot.style()
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history = gr.State([])
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TRUE = gr.State(True)
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FALSE = gr.State(False)
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with gr.Column(scale=1):
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with gr.Row():
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with gr.Column(scale=12):
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txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
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with gr.Column(scale=1):
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submitBtn = gr.Button("Ask", variant="primary")
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with gr.Row():
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for k in functional:
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variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
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functional[k]["Button"] = gr.Button(k, variant=variant)
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from check_proxy import check_proxy
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statusDisplay = gr.Markdown(f"{check_proxy(proxies)}")
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systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt).style(container=True)
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#inputs, top_p, temperature, top_k, repetition_penalty
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with gr.Accordion("arguments", open=False):
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top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
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temperature = gr.Slider(minimum=-0, maximum=5.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
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txt.submit(predict, [txt, top_p, temperature, chatbot, history, systemPromptTxt], [chatbot, history, statusDisplay])
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submitBtn.click(predict, [txt, top_p, temperature, chatbot, history, systemPromptTxt], [chatbot, history, statusDisplay], show_progress=True)
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# submitBtn.click(reset_textbox, [], [txt])
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for k in functional:
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functional[k]["Button"].click(predict,
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[txt, top_p, temperature, chatbot,history, systemPromptTxt, FALSE, TRUE, gr.State(k)], [chatbot, history, statusDisplay], show_progress=True)
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print(f"URL http://localhost:{PORT}")
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demo.title = "ChatGPT 学术优化"
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def auto_opentab_delay():
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import threading, webbrowser, time
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def open(): time.sleep(2)
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webbrowser.open_new_tab(f'http://localhost:{PORT}')
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t = threading.Thread(target=open)
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t.daemon = True; t.start()
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auto_opentab_delay()
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demo.queue().launch(server_name="0.0.0.0", share=True, server_port=PORT)
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