Update to_md function for illustrating code blocks

app.py

@@ -1,76 +1,10 @@
-import openai
-import tiktoken
-
-import datetime
-import json
-import os
-
-openai.api_key = os.getenv('API_KEY')
-openai.request_times = 0
-
-def ask(question, history, behavior):
-    openai.request_times += 1
-    print(f"request times {openai.request_times}: {datetime.datetime.now()}: {question}")
-    try:
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=forget_long_term(
-                [
-                    {"role":"system", "content":content}
-                    for content in behavior
-                ] + [
-                    {"role":"user" if i%2==0 else "assistant", "content":content}
-                    for i,content in enumerate(history + [question])
-                ]
-            )
-        )["choices"][0]["message"]["content"]
-        while response.startswith("\n"):
-            response = response[1:]
-    except Exception as e:
-        print(e)
-        response = 'Timeout! Please wait a few minutes and retry'
-    history = history + [question, response]
-    return history
-
-def num_tokens_from_messages(messages, model="gpt-3.5-turbo"):
-    """Returns the number of tokens used by a list of messages."""
-    try:
-        encoding = tiktoken.encoding_for_model(model)
-    except KeyError:
-        encoding = tiktoken.get_encoding("cl100k_base")
-    if model == "gpt-3.5-turbo":  # note: future models may deviate from this
-        num_tokens = 0
-        for message in messages:
-            num_tokens += 4  # every message follows <im_start>{role/name}\n{content}<im_end>\n
-            for key, value in message.items():
-                num_tokens += len(encoding.encode(value))
-                if key == "name":  # if there's a name, the role is omitted
-                    num_tokens += -1  # role is always required and always 1 token
-        num_tokens += 2  # every reply is primed with <im_start>assistant
-        return num_tokens
-    else:
-        raise NotImplementedError(f"""num_tokens_from_messages() is not presently implemented for model {model}.
-See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")
-
-def forget_long_term(messages, max_num_tokens=4000):
-    while num_tokens_from_messages(messages)>max_num_tokens:
-        if messages[0]["role"]=="system" and not len(messages[0]["content"]>=max_num_tokens):
-            messages = messages[:1] + messages[2:]
-        else:
-            messages = messages[1:]
-    return messages
-
-
-import gradio as gr
-
-
 def to_md(content):
     is_inside_code_block = False
     output_spans = []
     for i in range(len(content)):
         if content[i]=="\n" and not is_inside_code_block:
-            if len(output_spans)>0 and output_spans[-1]=="\n```\n":
-                pass
+            if len(output_spans)>0 and output_spans[-1].endswith("```"):
+                output_spans.append("\n")
             else:
                 output_spans.append("<br>")
         elif content[i]=="`":
@@ -78,70 +12,27 @@ def to_md(content):
             if len(output_spans)>=3 and all([output_spans[j]=="`" for j in [-3,-2,-1]]):
                 is_inside_code_block = not is_inside_code_block
                 output_spans = output_spans[:-3]
-                if not output_spans[-1]=="\n":
-                    output_spans.append("\n")
-                output_spans.append("\n```\n")
+                if is_inside_code_block:
+                    if len(output_spans)==0:
+                        output_spans.append("```")
+                    elif output_spans[-1]=="<br>":
+                        output_spans[-1] = "\n"
+                        output_spans.append("```")
+                    elif output_spans[-1].endswith("\n"):
+                        output_spans.append("```")
+                    else:
+                        output_spans.append("\n```")
+                    
+                    if i+1<len(content) and content[i+1]!="\n":
+                        output_spans.append("\n")
+                else:
+                    if output_spans[-1].endswith("\n"):
+                        output_spans.append("```")
+                    else:
+                        output_spans.append("\n```")
+                    
+                    if i+1<len(content) and content[i+1]!="\n":
+                        output_spans.append("\n")
         else:
             output_spans.append(content[i])
-    return "".join(output_spans)
-
-
-def predict(question, history=[], behavior=[]):
-    history = ask(question, history, behavior)
-    response = [(to_md(history[i]),to_md(history[i+1])) for i in range(0,len(history)-1,2)]
-    return "", history, response
-
-
-with gr.Blocks() as demo:
-    
-    examples_txt = [
-        ['帮我写一个python脚本实现快排'],
-        ['如何用numpy提取数组的分位数？'],
-        ['how to match the code block in markdown such like ```def foo():\n    pass``` through regex in python?'],
-        ['how to load a pre-trained language model and generate sentences?'],
-    ]
-    
-    examples_bhv = [
-        f"You are a helpful assistant. You will answer all the questions step-by-step.",
-        f"You are a helpful assistant. Today is {datetime.date.today()}.",
-    ]
-    
-    gr.Markdown(
-        """
-        朋友你好，
-        
-        这是我利用[gradio](https://gradio.app/creating-a-chatbot/)编写的一个小网页，用于以网页的形式给大家分享ChatGPT请求服务，希望你玩的开心。关于使用技巧或学术研讨，欢迎在[Community](https://huggingface.co/spaces/zhangjf/chatbot/discussions)中和我交流。
-        
-        这一版相比于原版的[chatbot](https://huggingface.co/spaces/zhangjf/chatbot)，用了较低版本的gradio==3.16.2，因而能更好地展示markdown中的源代码
-        
-        p.s. 响应时间和聊天内容长度正相关，一般能在5秒~30秒内响应。
-        """)
-    
-    behavior = gr.State([])
-    
-    with gr.Column(variant="panel"):
-        with gr.Row().style(equal_height=True):
-            with gr.Column(scale=0.85):
-                bhv = gr.Textbox(show_label=False, placeholder="输入你想让ChatGPT扮演的人设").style(container=False)
-            with gr.Column(scale=0.15, min_width=0):
-                button_set = gr.Button("Set")
-    bhv.submit(fn=lambda x:(x,[x]), inputs=[bhv], outputs=[bhv, behavior])
-    button_set.click(fn=lambda x:(x,[x]), inputs=[bhv], outputs=[bhv, behavior])
-    
-
-    state = gr.State([])
-    
-    with gr.Column(variant="panel"):
-        chatbot = gr.Chatbot()
-        txt = gr.Textbox(show_label=False, placeholder="输入你想让ChatGPT回答的问题").style(container=False)
-        with gr.Row():
-            button_gen = gr.Button("Submit")
-            button_clr = gr.Button("Clear")
-        
-    gr.Examples(examples=examples_bhv, inputs=bhv, label="Examples for setting behavior")
-    gr.Examples(examples=examples_txt, inputs=txt, label="Examples for asking question")
-    txt.submit(predict, [txt, state, behavior], [txt, state, chatbot])
-    button_gen.click(fn=predict, inputs=[txt, state, behavior], outputs=[txt, state, chatbot])
-    button_clr.click(fn=lambda :([],[]), inputs=None, outputs=[chatbot, state])
-
-demo.launch()
+    return "".join(output_spans)