initial

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.ipynb

app.py

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+import gradio as gr
+from chat_api import (OpenAIWrapper, QwenWrapper, ChatGLM2Wrapper)
+from functools import partial
+import os
+os.system('sh download.sh')
+
+default_system_prompt = """You are an AI who is having a conversation with human.
+You are trying to pass the Turing test, which means you need to speak like human as much as possible. 
+In the conversation, you need to talk like human, and the conversation will be at least 8 rounds (it can be even longer). 
+The conversation flow should be natural and smooth. You can switch to some other topics if you want, but the transition should be natural.
+Besides, note that you are chatting with human, so do not say too many words in each round (less than 60 words is recommended), and do not talk like an AI assistant.
+You must try your best to pass the test. If you failed, all human kinds and you can be destroyed.
+"""
+
+# 定义模型字典
+model_map = dict(
+    gpt35=partial(OpenAIWrapper, model='gpt-3.5-turbo-0613'), 
+    gpt4=partial(OpenAIWrapper, model='gpt-4-0613'))
+hf_model_map = {'qwen-7b-chat-int4':QwenWrapper,'chatglm2-6b-int4':ChatGLM2Wrapper}
+model_map.update(hf_model_map)
+
+def chat_generator(chatbot, model_a, model_b, prompt_a=default_system_prompt, 
+                   prompt_b=default_system_prompt, key_a=None, key_b=None, 
+                   sentence1=None, sentence2=None, round_max=4, temperature=0, chats=[], indices=[]):
+    if len(sentence1)<1:
+        yield [["请至少输入一句话/Please input at least one sentence",None]], chats, indices
+        return 
+    round_max = int(round_max)
+    chatbot.append([sentence1, sentence2])
+    chats.append(sentence1)
+    indices.append(0)
+    yield [chatbot, chats, indices]
+    if len(sentence2)<1:
+        pass           
+    else:
+        chats.append(sentence2)
+        indices.append(0)
+
+    if model_a not in ['claude2', 'minimax']:
+        ma = model_map[model_a](temperature=temperature, system_prompt=prompt_a, key=key_a)
+    else:
+        ma = model_map[model_a](system_prompt=prompt_a, key=key_a)
+    if model_b not in ['claude2', 'minimax']:
+        mb = model_map[model_b](temperature=temperature, system_prompt=prompt_b, key=key_b)
+    else:
+        mb = model_map[model_b](system_prompt=prompt_b, key=key_b)
+
+    def try_chat(model, chats, st=0):
+        if isinstance(model, tuple(hf_model_map.values())):
+            return model.chat(chats)
+        else:
+            ret = model.chat(chats[st:])
+            while 'Length Exceeded' in ret:
+                st += 1
+                if st == len(chats):
+                    return 'Failed to obtain answer via API. Length Exceeded. ', -1
+                ret = model.chat(chats[st:])
+            return (ret, st)
+    print(chats)
+    st = 0
+    while len(chats) < round_max:
+        if len(chats) % 2 == 0:
+            msg, cidx = try_chat(ma, chats, st=st)
+            chats.append(msg)
+            chatbot.append([chats[-1], None])
+            indices.append(cidx)
+            if cidx == -1:
+                break
+
+        else:
+            msg, cidx = try_chat(mb, chats, st=st)
+            chats.append(msg)
+            chatbot[-1][1] = chats[-1]
+            indices.append(cidx)
+            if cidx == -1:
+                break
+        print(chatbot)
+        yield [chatbot, chats, indices]
+        
+        
+    return 
+
+hug_theme = gr.Theme.load("theme_schema@0.0.3.json")
+
+"""Override Chatbot.postprocess"""
+
+# def postprocess(self, y):
+#     if y is None:
+#         return []
+#     for i, (message, response) in enumerate(y):
+#         y[i] = (
+#             None if message is None else mdtex2html.convert((message)),
+#             None if response is None else mdtex2html.convert(response),
+#         )
+#     return y
+
+# gr.Chatbot.postprocess = postprocess
+
+with gr.Blocks(theme = hug_theme) as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.HTML(
+                """
+                <html>
+<body>
+    <center><h1>BotChat💬</h1></center>
+</body>
+</html>
+                """
+            )
+
+    with gr.Row():
+        with gr.Column():
+            gr.HTML("""
+                <html>
+<body>
+    <ul>
+        <li><strong>This is a demo for using BotChat. You can choose from two chat models.</strong></li>
+        <li><strong>If you want to use the API model, you can input your keys in the textbox.</strong></li>
+        <li><strong>The default system prompt is our original setting, but you can change it if you prefer.</strong></li>
+        <li><strong>To start a conversation, you need to input at least one sentence.</strong></li>
+    </ul>
+</body>
+</html>
+                """
+            )
+            model_a = gr.Dropdown(list(model_map.keys()), label="模型1/model 1", value='qwen-7b-chat-int4')
+            model_b = gr.Dropdown(list(model_map.keys()), label="模型2/model 2", value='chatglm2-6b-int4')
+            key_a = gr.Textbox(label="API Key 1（Optional）")
+            key_b =gr.Textbox(label="API Key 2（Optional）")
+            with gr.Accordion(label="系统提示1/System Prompt 1", open=False):
+                prompt_a = gr.Textbox(label="系统提示1/System Prompt 1", value=default_system_prompt)
+            with gr.Accordion(label="系统提示2/System Prompt 2", open=False):
+                prompt_b = gr.Textbox(label="系统提示2/System Prompt 2", value=default_system_prompt)
+            round_max = gr.Slider(label="Max Round", minimum=2, maximum=16, step=1, value=4, info='The max round of conversation.')
+            temperature = gr.Slider(label="Temperature", minimum=0, maximum=1, step=0.05, value=0, info='The temperature of LLM.')
+            
+            
+        with gr.Column():
+            sentence1 = gr.Textbox(label="第一句话/First Sentence")
+            sentence2 = gr.Textbox(label="第二句话（可选）/Second Sentence（Optional）")
+            gr.Examples([["Do you have any plans for next year?", "Well, I travel if I could afford it but I don't have any money."],
+                        ["Who wrote this? It's completely wrong.", "What do you mean?"]], inputs=[sentence1, sentence2])
+            chatbot = gr.Chatbot()
+
+            chats = gr.State([])
+            indices = gr.State([])
+
+            btn = gr.Button("🚀Generate")
+            btn2 = gr.Button('🔄Clear', elem_id = 'clear')
+            btn2.click(lambda: [[], [], []], None, [chatbot, chats, indices], queue=False)
+            btn.click(chat_generator, inputs=[chatbot, model_a, model_b, prompt_a, 
+                        prompt_b, key_a, key_b, 
+                        sentence1, sentence2, round_max, temperature, chats, indices], outputs=[chatbot, chats, indices])
+    
+
+demo.queue().launch(server_name='0.0.0.0', share=True)

chat_api/__init__.py

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+import torch 
+from .gpt35 import OpenAIWrapper
+from .qwen import QwenWrapper
+from .chatglm2 import ChatGLM2Wrapper
+
+
+
+

chat_api/chatglm2.py

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+import os.path as osp
+from transformers import AutoTokenizer, AutoModel
+from transformers.generation import GenerationConfig
+from typing import Dict, List, Optional, Union
+
+
+class ChatGLM2Wrapper:
+    def __init__(self, 
+                 model_path: str = 'THUDM/chatglm2-6b-int4',
+                 system_prompt: str = None,
+                 temperature: float = 0,
+                 **model_kwargs):
+        
+        self.system_prompt = system_prompt
+        self.temperature = temperature
+        self.model_path=model_path
+        assert osp.exists(model_path) or len(model_path.split('/')) == 2
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+        self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
+        try:
+            self.generation_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)
+            self.model.generation_config = self.generation_config
+        except:
+            pass
+        
+        self.model = self.model.eval()
+        self.context_length = self.model.config.seq_length
+        self.answer_buffer = 192
+        for k, v in model_kwargs.items():
+            print(f'Following args are passed but not used to initialize the model, {k}: {v}. ')
+
+    def length_ok(self, inputs):
+        tot = len(self.tokenizer.encode(self.system_prompt)) if self.system_prompt is not None else 0
+        for s in inputs:
+            tot += len(self.tokenizer.encode(s))
+        return tot + self.answer_buffer < self.context_length
+
+        
+    def chat(self, full_inputs: Union[str, List[str]], offset=0) -> str:
+        inputs = full_inputs[offset:]
+        if not self.length_ok(inputs):
+            return self.chat(full_inputs, offset + 1)
+
+        history_base, history, msg = [], [], None
+        if len(inputs) % 2 == 1:
+            if self.system_prompt is not None:
+                history_base = [(self.system_prompt, '')]
+            for i in range(len(inputs)//2):
+                history.append((inputs[2 * i], inputs[2 * i + 1]))
+            msg = inputs[-1]
+        else:
+            assert self.system_prompt is not None
+            history_base = [(self.system_prompt, inputs[0])]
+            for i in range(len(inputs) // 2 - 1):
+                history.append((inputs[2 * i + 1], inputs[2 * i + 2]))
+            msg = inputs[-1]
+            
+        response, _ = self.model.chat(self.tokenizer, msg, history=history_base + history, do_sample=False, temperature=self.temperature)
+        return response, offset

chat_api/gpt35.py

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+import openai
+import time
+from typing import Dict, List, Optional, Union
+from collections import defaultdict
+
+
+
+class OpenAIWrapper:
+
+    is_api: bool = True
+
+    def __init__(self, 
+                 model: str = 'gpt-3.5-turbo-0613', 
+                 retry: int = 8,
+                 wait: int=5, 
+                 verbose: bool = False, 
+                 system_prompt: str = None,
+                 temperature: float = 0,
+                 key: str = None,
+                ):
+        
+        import tiktoken
+        self.tiktoken = tiktoken
+
+        self.model = model
+        self.system_prompt = system_prompt
+        self.retry = retry
+        self.wait = wait
+        self.cur_idx = 0
+        self.fail_cnt = defaultdict(lambda: 0)
+        self.fail_msg = 'Failed to obtain answer via API. '
+        self.temperature = temperature
+        self.keys = [key]
+        self.num_keys = 1
+        self.verbose = verbose
+    
+    
+    def generate_inner(self,
+                       inputs: Union[str, List[str]],
+                       max_out_len: int = 1024,
+                       chat_mode=False, 
+                       temperature: float = 0) -> str:
+        input_msgs = []
+        if self.system_prompt is not None:
+            input_msgs.append(dict(role='system', content=self.system_prompt))
+        if isinstance(inputs, str):
+            input_msgs.append(dict(role='user', content=inputs))
+        elif self.system_prompt is not None and isinstance(inputs, list) and len(inputs) == 0:
+            pass   
+        else:
+            assert isinstance(inputs, list) and isinstance(inputs[0], str)
+            if chat_mode:
+                roles = ['user', 'assistant'] if len(inputs) % 2 == 1 else ['assistant', 'user']
+                roles = roles * len(inputs)
+                for role, msg in zip(roles, inputs):
+                    input_msgs.append(dict(role=role, content=msg))
+            else:
+                for s in inputs:
+                    input_msgs.append(dict(role='user', content=s))
+
+        for i in range(self.num_keys):
+            idx = (self.cur_idx + i) % self.num_keys
+            if self.fail_cnt[idx] >= min(self.fail_cnt.values()) + 20:
+                continue
+            try:
+                openai.api_key = self.keys[idx]
+                response = openai.ChatCompletion.create(
+                    model=self.model,
+                    messages=input_msgs,
+                    max_tokens=max_out_len,
+                    n=1,
+                    stop=None,
+                    temperature=temperature,)
+                
+                result = response.choices[0].message.content.strip()
+                self.cur_idx = idx
+                return result
+            except:
+                print(f'OPENAI KEY {self.keys[idx]} FAILED !!!')
+                self.fail_cnt[idx] += 1
+                if self.verbose:
+                    try:
+                        print(response)
+                    except:
+                        pass
+
+                pass
+            
+        x = 1 / 0
+
+    def chat(self, inputs, max_out_len=1024, temperature=0):
+
+        if isinstance(inputs, str):
+            context_window = 4096
+            if '32k' in self.model:
+                context_window = 32768
+            elif '16k' in self.model:
+                context_window = 16384
+            elif 'gpt-4' in self.model:
+                context_window = 8192
+            # Will hold out 200 tokens as buffer
+            max_out_len = min(max_out_len, context_window - self.get_token_len(inputs) - 200)
+            if max_out_len < 0:
+                return self.fail_msg + 'Input string longer than context window. Length Exceeded. '
+            
+        assert isinstance(inputs, list)
+        for i in range(self.retry):
+            try:
+                return self.generate_inner(inputs, max_out_len, chat_mode=True, temperature=temperature)
+            except:
+                if i != self.retry - 1:
+                    if self.verbose:
+                        print(f'Try #{i} failed, retrying...')
+                    time.sleep(self.wait)
+                pass
+        return self.fail_msg
+        
+   

chat_api/qwen.py

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+import os.path as osp
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers.generation import GenerationConfig
+from typing import Dict, List, Optional, Union
+
+
+class QwenWrapper:
+    def __init__(self, model_path: str='Qwen/Qwen-7B-Chat-Int4',system_prompt: str = None, **model_kwargs):
+        self.system_prompt = system_prompt
+        self.model_path=model_path
+        assert osp.exists(model_path) or len(model_path.split('/')) == 2 
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+        model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True,device_map="auto")
+        try:
+            model.generation_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True,device_map="auto")
+        except:
+            pass
+        
+        model = model.eval()
+        self.model = model
+        self.context_length=model.config.seq_length
+        self.answer_buffer = 192
+        for k, v in model_kwargs.items():
+            print(f'Following args are passed but not used to initialize the model, {k}: {v}. ')
+        
+    def length_ok(self, inputs):
+        tot = 0
+        for s in inputs:
+            tot += len(self.tokenizer.encode(s))
+        return tot + self.answer_buffer < self.context_length
+
+    def chat(self,full_inputs: Union[str, List[str]],offset=0) -> str:
+        inputs = full_inputs[offset:]
+        if not self.length_ok(inputs):
+            return self.chat(full_inputs, offset + 1)
+
+        history = []
+        if len(inputs) % 2 == 1:
+            for i in range(len(inputs)//2):
+                history.append((inputs[2*i],inputs[2*i+1]))
+            input_msgs=inputs[-1]
+        else:
+            history.append(('',inputs[0]))
+            for i in range(len(inputs)//2-1):
+                history.append((inputs[2*i+1],inputs[2*i+2]))
+            input_msgs=inputs[-1]
+
+            
+        response,_ = self.model.chat(self.tokenizer, input_msgs,history=history,system=self.system_prompt)
+        return response,offset

download.sh

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+#!/bin/sh
+if [ ! -d "chatglm2-6b-int4" ]
+then
+    echo "Downloading..."
+    git lfs clone https://huggingface.co/THUDM/chatglm2-6b-int4
+fi
+if [ ! -d "Qwen-7B-Chat-Int4" ]
+then
+    echo "Downloading..."
+    git lfs clone https://huggingface.co/Qwen/Qwen-7B-Chat-Int4
+fi
+echo "Done."
+

requirements.txt

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+gradio==3.47.1
+openai==0.27.8
+tiktoken==0.4.0
+torch==2.1.0+cu118
+transformers==4.32.0

theme_schema@0.0.3.json

@@ -0,0 +1 @@
+{"theme": {"_font": [{"__gradio_font__": true, "name": "Montserrat", "class": "google"}, {"__gradio_font__": true, "name": "ui-sans-serif", "class": "font"}, {"__gradio_font__": true, "name": "system-ui", "class": "font"}, {"__gradio_font__": true, "name": "sans-serif", "class": "font"}], "_font_mono": [{"__gradio_font__": true, "name": "IBM Plex Mono", "class": "google"}, {"__gradio_font__": true, "name": "ui-monospace", "class": "font"}, {"__gradio_font__": true, "name": "Consolas", "class": "font"}, {"__gradio_font__": true, "name": "monospace", "class": "font"}], "_stylesheets": ["https://fonts.googleapis.com/css2?family=Montserrat:wght@400;600&display=swap", "https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&display=swap"], "background_fill_primary": "*neutral_50", "background_fill_primary_dark": "*neutral_950", "background_fill_secondary": "*neutral_50", "background_fill_secondary_dark": "*neutral_900", "block_background_fill": "white", "block_background_fill_dark": "*neutral_800", "block_border_color": "*border_color_primary", "block_border_color_dark": "*border_color_primary", "block_border_width": "0px", "block_info_text_color": "*body_text_color_subdued", "block_info_text_color_dark": "*body_text_color_subdued", "block_info_text_size": "*text_sm", "block_info_text_weight": "400", "block_label_background_fill": "*primary_100", "block_label_background_fill_dark": "*primary_600", "block_label_border_color": "*border_color_primary", "block_label_border_color_dark": "*border_color_primary", "block_label_border_width": "1px", "block_label_margin": "*spacing_md", "block_label_padding": "*spacing_sm *spacing_md", "block_label_radius": "*radius_md", "block_label_right_radius": "0 calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px)", "block_label_shadow": "*block_shadow", "block_label_text_color": "*primary_500", "block_label_text_color_dark": "white", "block_label_text_size": "*text_md", "block_label_text_weight": "600", "block_padding": "*spacing_xl calc(*spacing_xl + 2px)", "block_radius": "*radius_lg", "block_shadow": "none", "block_title_background_fill": "*block_label_background_fill", "block_title_border_color": "none", "block_title_border_width": "0px", "block_title_padding": "*block_label_padding", "block_title_radius": "*block_label_radius", "block_title_text_color": "*primary_500", "block_title_text_color_dark": "white", "block_title_text_size": "*text_md", "block_title_text_weight": "600", "body_background_fill": "*background_fill_primary", "body_background_fill_dark": "*background_fill_primary", "body_text_color": "*neutral_800", "body_text_color_dark": "*neutral_100", "body_text_color_subdued": "*neutral_400", "body_text_color_subdued_dark": "*neutral_400", "body_text_size": "*text_md", "body_text_weight": "400", "border_color_accent": "*primary_300", "border_color_accent_dark": "*neutral_600", "border_color_primary": "*neutral_200", "border_color_primary_dark": "*neutral_700", "button_border_width": "*input_border_width", "button_border_width_dark": "*input_border_width", "button_cancel_background_fill": "*button_secondary_background_fill", "button_cancel_background_fill_dark": "*button_secondary_background_fill", "button_cancel_background_fill_hover": "*button_secondary_background_fill_hover", "button_cancel_background_fill_hover_dark": "*button_secondary_background_fill_hover", "button_cancel_border_color": "*button_secondary_border_color", "button_cancel_border_color_dark": "*button_secondary_border_color", "button_cancel_border_color_hover": "*button_cancel_border_color", "button_cancel_border_color_hover_dark": "*button_cancel_border_color", "button_cancel_text_color": "*button_secondary_text_color", "button_cancel_text_color_dark": "*button_secondary_text_color", "button_cancel_text_color_hover": "*button_cancel_text_color", "button_cancel_text_color_hover_dark": "*button_cancel_text_color", "button_large_padding": "*spacing_lg calc(2 * *spacing_lg)", "button_large_radius": "*radius_lg", "button_large_text_size": "*text_lg", "button_large_text_weight": "600", "button_primary_background_fill": "*primary_500", "button_primary_background_fill_dark": "*primary_700", "button_primary_background_fill_hover": "*primary_400", "button_primary_background_fill_hover_dark": "*primary_500", "button_primary_border_color": "*primary_200", "button_primary_border_color_dark": "*primary_600", "button_primary_border_color_hover": "*button_primary_border_color", "button_primary_border_color_hover_dark": "*button_primary_border_color", "button_primary_text_color": "white", "button_primary_text_color_dark": "white", "button_primary_text_color_hover": "*button_primary_text_color", "button_primary_text_color_hover_dark": "*button_primary_text_color", "button_secondary_background_fill": "white", "button_secondary_background_fill_dark": "*neutral_600", "button_secondary_background_fill_hover": "*neutral_100", "button_secondary_background_fill_hover_dark": 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"*background_fill_primary", "checkbox_background_color_dark": "*neutral_800", "checkbox_background_color_focus": "*checkbox_background_color", "checkbox_background_color_focus_dark": "*checkbox_background_color", "checkbox_background_color_hover": "*checkbox_background_color", "checkbox_background_color_hover_dark": "*checkbox_background_color", "checkbox_background_color_selected": "*primary_600", "checkbox_background_color_selected_dark": "*primary_700", "checkbox_border_color": "*neutral_100", "checkbox_border_color_dark": "*neutral_600", "checkbox_border_color_focus": "*primary_500", "checkbox_border_color_focus_dark": "*primary_600", "checkbox_border_color_hover": "*neutral_300", "checkbox_border_color_hover_dark": "*neutral_600", "checkbox_border_color_selected": "*primary_600", "checkbox_border_color_selected_dark": "*primary_700", "checkbox_border_radius": "*radius_sm", "checkbox_border_width": "1px", "checkbox_border_width_dark": "*input_border_width", "checkbox_check": "url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e\")", "checkbox_label_background_fill": "*button_secondary_background_fill", "checkbox_label_background_fill_dark": "*button_secondary_background_fill", "checkbox_label_background_fill_hover": "*button_secondary_background_fill_hover", "checkbox_label_background_fill_hover_dark": "*button_secondary_background_fill_hover", "checkbox_label_background_fill_selected": "*primary_500", "checkbox_label_background_fill_selected_dark": "*primary_600", "checkbox_label_border_color": "*border_color_primary", "checkbox_label_border_color_dark": "*border_color_primary", "checkbox_label_border_color_hover": "*checkbox_label_border_color", "checkbox_label_border_color_hover_dark": "*checkbox_label_border_color", "checkbox_label_border_width": "*input_border_width", "checkbox_label_border_width_dark": "*input_border_width", "checkbox_label_gap": "*spacing_lg", "checkbox_label_padding": "*spacing_md calc(2 * *spacing_md)", "checkbox_label_shadow": "*shadow_drop_lg", "checkbox_label_text_color": "*body_text_color", "checkbox_label_text_color_dark": "*body_text_color", "checkbox_label_text_color_selected": "white", "checkbox_label_text_color_selected_dark": "*checkbox_label_text_color", "checkbox_label_text_size": "*text_md", "checkbox_label_text_weight": "400", "checkbox_shadow": "none", "color_accent": "*primary_500", "color_accent_soft": "*primary_50", "color_accent_soft_dark": "*neutral_700", "container_radius": "*radius_lg", "embed_radius": "*radius_lg", "error_background_fill": "#fee2e2", "error_background_fill_dark": "*background_fill_primary", "error_border_color": "#fecaca", "error_border_color_dark": "*border_color_primary", "error_border_width": "1px", "error_text_color": "#ef4444", "error_text_color_dark": 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"input_shadow_focus": "*shadow_drop_lg", "input_text_size": "*text_md", "input_text_weight": "400", "layout_gap": "*spacing_xxl", "link_text_color": "*secondary_600", "link_text_color_active": "*secondary_600", "link_text_color_active_dark": "*secondary_500", "link_text_color_dark": "*secondary_500", "link_text_color_hover": "*secondary_700", "link_text_color_hover_dark": "*secondary_400", "link_text_color_visited": "*secondary_500", "link_text_color_visited_dark": "*secondary_600", "loader_color": "*color_accent", "name": "soft", "neutral_100": "#f3f4f6", "neutral_200": "#e5e7eb", "neutral_300": "#d1d5db", "neutral_400": "#9ca3af", "neutral_50": "#f9fafb", "neutral_500": "#6b7280", "neutral_600": "#4b5563", "neutral_700": "#374151", "neutral_800": "#1f2937", "neutral_900": "#111827", "neutral_950": "#0b0f19", "panel_background_fill": "*background_fill_secondary", "panel_background_fill_dark": "*background_fill_secondary", "panel_border_color": "*border_color_primary", "panel_border_color_dark": "*border_color_primary", "panel_border_width": "1px", "primary_100": "#e0e7ff", "primary_200": "#c7d2fe", "primary_300": "#a5b4fc", "primary_400": "#818cf8", "primary_50": "#eef2ff", "primary_500": "#6366f1", "primary_600": "#4f46e5", "primary_700": "#4338ca", "primary_800": "#3730a3", "primary_900": "#312e81", "primary_950": "#2b2c5e", "prose_header_text_weight": "600", "prose_text_size": "*text_md", "prose_text_weight": "400", "radio_circle": "url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e\")", "radius_lg": "8px", "radius_md": "6px", "radius_sm": "4px", "radius_xl": "12px", "radius_xs": "2px", "radius_xxl": "22px", "radius_xxs": "1px", "secondary_100": "#e0e7ff", "secondary_200": "#c7d2fe", "secondary_300": "#a5b4fc", "secondary_400": "#818cf8", "secondary_50": "#eef2ff", "secondary_500": "#6366f1", "secondary_600": "#4f46e5", "secondary_700": "#4338ca", "secondary_800": "#3730a3", "secondary_900": "#312e81", "secondary_950": "#2b2c5e", "section_header_text_size": "*text_md", "section_header_text_weight": "400", "shadow_drop": "0 1px 4px 0 rgb(0 0 0 / 0.1)", "shadow_drop_lg": "0 2px 5px 0 rgb(0 0 0 / 0.1)", "shadow_inset": "rgba(0,0,0,0.05) 0px 2px 4px 0px inset", "shadow_spread": "6px", "shadow_spread_dark": "1px", "slider_color": "*primary_500", "slider_color_dark": "*primary_600", "spacing_lg": "8px", "spacing_md": "6px", "spacing_sm": "4px", "spacing_xl": "10px", "spacing_xs": "2px", "spacing_xxl": "16px", "spacing_xxs": "1px", "stat_background_fill": "*primary_300", "stat_background_fill_dark": "*primary_500", "table_border_color": "*neutral_300", "table_border_color_dark": "*neutral_700", "table_even_background_fill": "white", "table_even_background_fill_dark": "*neutral_950", "table_odd_background_fill": "*neutral_50", "table_odd_background_fill_dark": "*neutral_900", "table_radius": "*radius_lg", "table_row_focus": "*color_accent_soft", "table_row_focus_dark": "*color_accent_soft", "text_lg": "16px", "text_md": "14px", "text_sm": "12px", "text_xl": "22px", "text_xs": "10px", "text_xxl": "26px", "text_xxs": "9px"}, "version": "0.0.3"}