app.py

from openai import OpenAI
from dotenv import load_dotenv
import os
import threading
import time
import gradio as gr
from lang import LANGUAGE_CONFIG

# 环境变量预校验
load_dotenv(override=True)
required_env_vars = ["API_KEY", "API_URL", "API_MODEL"]
missing_vars = [var for var in required_env_vars if not os.getenv(var)]
if missing_vars:
    raise EnvironmentError(f"Missing required environment variables: {', '.join(missing_vars)}")


class AppConfig:
    DEFAULT_THROUGHPUT = 10
    SYNC_THRESHOLD_DEFAULT = 0
    API_TIMEOUT = 20
    LOADING_DEFAULT = "✅ Ready! <br> Think together with AI. Use Shift+Enter to toggle generation"

class DynamicState:
    """动态UI状态"""
    def __init__(self):
        self.should_stream = False
        self.stream_completed = False
        self.in_cot = True
        self.current_language = "en"

    def control_button_handler(self):
        """切换流式传输状态"""
        if self.should_stream:
            self.should_stream = False
        else:
            self.stream_completed = False
            self.should_stream = True
        return self.ui_state_controller()
    
    def ui_state_controller(self):
        """生成动态UI组件状态"""
        print("UPDATE UI!!")
        # [control_button, status_indicator,  thought_editor, reset_button]
        lang_data = LANGUAGE_CONFIG[self.current_language]
        control_value = lang_data["pause_btn"] if self.should_stream else lang_data["generate_btn"]
        control_variant = "secondary" if self.should_stream else "primary"
        status_value = lang_data["completed"] if self.stream_completed else lang_data["interrupted"]
        return (
            gr.update(
                value=control_value,
                variant=control_variant
            ),
            gr.update(
                value=status_value,
            ),
            gr.update(),
            gr.update(interactive = not self.should_stream)
        )
    def reset_workspace(self):
        """重置工作区状态"""
        self.stream_completed = False
        self.should_stream = False
        self.in_cot = True
        return self.ui_state_controller() + ("", "", LANGUAGE_CONFIG["en"]["bot_default"])
    
class CoordinationManager:
    """管理人类与AI的协同节奏"""
    def __init__(self, paragraph_threshold, initial_content):
        self.paragraph_threshold = paragraph_threshold
        self.initial_paragraph_count = initial_content.count("\n\n")
        self.triggered = False

    def should_pause_for_human(self, current_content):
        if self.paragraph_threshold <= 0 or self.triggered:
            return False

        current_paragraphs = current_content.count("\n\n")
        if current_paragraphs - self.initial_paragraph_count >= self.paragraph_threshold:
            self.triggered = True
            return True
        return False


class ConvoState:
    """State of current ROUND of convo"""
    def __init__(self):
        self.throughput = AppConfig.DEFAULT_THROUGHPUT
        self.sync_threshold = AppConfig.SYNC_THRESHOLD_DEFAULT
        self.current_language = "en"
        self.convo = []
        self.initialize_new_round()

    def initialize_new_round(self):
        self.current = {}
        self.current["user"] = ""
        self.current["cot"] = ""
        self.current["result"] = ""
        self.convo.append(self.current)

    
    def flatten_output(self):
        output = []
        for round in self.convo:
            output.append({"role": "user", "content": round["user"]})
            if len(round["cot"])>0:
                output.append({"role": "assistant", "content": round["cot"], "metadata":{"title": f"Chain of Thought"}})
            if len(round["result"])>0:
                output.append({"role": "assistant", "content": round["result"]})
        return output

    def generate_ai_response(self, user_prompt, current_content, dynamic_state):
        lang_data = LANGUAGE_CONFIG[self.current_language]
        dynamic_state.stream_completed = False
        full_response = current_content
        api_client = OpenAI(
            api_key=os.getenv("API_KEY"),
            base_url=os.getenv("API_URL"),
            timeout=AppConfig.API_TIMEOUT
        )
        coordinator = CoordinationManager(self.sync_threshold, current_content)

        try:
            messages = [
                {"role": "user", "content": user_prompt},
                {"role": "assistant", "content": f"<think>\n{current_content}", "prefix": True}
            ]
            self.current["user"] = user_prompt
            response_stream = api_client.chat.completions.create(
                model=os.getenv("API_MODEL"),
                messages=messages,
                stream=True,
                timeout=AppConfig.API_TIMEOUT
            )
            for chunk in response_stream:
                chunk_content = chunk.choices[0].delta.content
                if coordinator.should_pause_for_human(full_response):
                    dynamic_state.should_stream = False
                if not dynamic_state.should_stream:
                    break
                
                if chunk_content:
                    full_response += chunk_content
                    # Update Convo State
                    think_complete = "</think>" in full_response
                    dynamic_state.in_cot = not think_complete
                    if think_complete:
                        self.current["cot"], self.current["result"] = full_response.split("</think>")
                    else:
                        self.current["cot"], self.current["result"]  = (full_response, "")
                    status = lang_data["loading_thinking"] if dynamic_state.in_cot else lang_data["loading_output"]
                    yield full_response, status, self.flatten_output()

                    interval = 1.0 / self.throughput
                    start_time = time.time()
                    while (time.time() - start_time) < interval and dynamic_state.should_stream:
                        time.sleep(0.005)

        except Exception as e:
            error_msg = LANGUAGE_CONFIG[self.current_language].get("error", "Error")
            full_response += f"\n\n[{error_msg}: {str(e)}]"
            yield full_response, error_msg, status, self.flatten_output() + [{"role":"assistant","content": error_msg, "metadata":{"title": f"❌Error"}}]

        finally:
            dynamic_state.should_stream = False
            if "status" not in locals():
                status = "Whoops... ERROR"
            if 'response_stream' in locals():
                response_stream.close()
            yield full_response, status, self.flatten_output()


def update_interface_language(selected_lang, convo_state, dynamic_state):
    """更新界面语言配置"""
    convo_state.current_language = selected_lang
    dynamic_state.current_language = selected_lang
    lang_data = LANGUAGE_CONFIG[selected_lang]
    return [
        gr.update(value=f"{lang_data['title']}"),
        gr.update(label=lang_data["prompt_label"], placeholder=lang_data["prompt_placeholder"]),
        gr.update(label=lang_data["editor_label"], placeholder=lang_data["editor_placeholder"]),
        gr.update(label=lang_data["sync_threshold_label"], info=lang_data["sync_threshold_info"]),
        gr.update(label=lang_data["throughput_label"], info=lang_data["throughput_info"]),
        gr.update(
            value=lang_data["pause_btn"] if dynamic_state.should_stream else lang_data["generate_btn"],
            variant="secondary" if dynamic_state.should_stream else "primary"
        ),
        gr.update(label=lang_data["language_label"]),
        gr.update(value=lang_data["clear_btn"], interactive = not dynamic_state.should_stream),
        gr.update(value=lang_data["introduction"]),
        gr.update(value=lang_data["bot_default"]),
    ]



theme = gr.themes.Base(font="system-ui", primary_hue="stone")

with gr.Blocks(theme=theme, css_paths="styles.css") as demo:
    convo_state = gr.State(ConvoState)
    dynamic_state = gr.State(DynamicState) # DynamicState is now a separate state

    with gr.Row(variant=""):
        title_md = gr.Markdown(f"## {LANGUAGE_CONFIG['en']['title']} \n GitHub: https://github.com/Intelligent-Internet/CoT-Lab-Demo", container=False)
        lang_selector = gr.Dropdown(
            choices=["en", "zh"], 
            value="en",
            elem_id="compact_lang_selector",
            scale=0,
            container=False
        )
    
    with gr.Row(equal_height=True):
        # 对话面板
        with gr.Column(scale=1, min_width=500):
            chatbot = gr.Chatbot(type="messages", height=300, 
                                 value=LANGUAGE_CONFIG['en']['bot_default'],
                                 group_consecutive_messages=False,
                                 show_copy_all_button=True,
                                 show_share_button=True,
                                 )
            prompt_input = gr.Textbox(
                label=LANGUAGE_CONFIG["en"]["prompt_label"],
                lines=2,
                placeholder=LANGUAGE_CONFIG["en"]["prompt_placeholder"],
                max_lines=5,
            )
            with gr.Row():
                control_button = gr.Button(
                    value=LANGUAGE_CONFIG["en"]["generate_btn"],
                    variant="primary"
                )
                next_turn_btn = gr.Button(
                    value=LANGUAGE_CONFIG["en"]["clear_btn"],
                    interactive=True
                )
            status_indicator = gr.Markdown(AppConfig.LOADING_DEFAULT)
            intro_md = gr.Markdown(LANGUAGE_CONFIG["en"]["introduction"], visible=False)
        
        # 思考编辑面板
        with gr.Column(scale=1, min_width=400):
            thought_editor = gr.Textbox(
                label=LANGUAGE_CONFIG["en"]["editor_label"],
                lines=16,
                placeholder=LANGUAGE_CONFIG["en"]["editor_placeholder"],
                autofocus=True,
                elem_id="editor"
            )
            with gr.Row():
                sync_threshold_slider = gr.Slider(
                    minimum=0,
                    maximum=20,
                    value=AppConfig.SYNC_THRESHOLD_DEFAULT,
                    step=1,
                    label=LANGUAGE_CONFIG["en"]["sync_threshold_label"],
                    info=LANGUAGE_CONFIG["en"]["sync_threshold_info"]
                )
                throughput_control = gr.Slider(
                    minimum=1,
                    maximum=100,
                    value=AppConfig.DEFAULT_THROUGHPUT,
                    step=1,
                    label=LANGUAGE_CONFIG["en"]["throughput_label"],
                    info=LANGUAGE_CONFIG["en"]["throughput_info"]
                )

    # 交互逻辑

    stateful_ui = (control_button, status_indicator,  thought_editor, next_turn_btn)

    throughput_control.change(
        lambda val, s: setattr(s, "throughput", val),
        [throughput_control, convo_state],
        None,
        queue=False
        )

    sync_threshold_slider.change(
        lambda val, s: setattr(s, "sync_threshold", val),
        [sync_threshold_slider, convo_state],
        None,
        queue=False
    )

    def wrap_stream_generator(convo_state, dynamic_state, prompt, content): # Pass dynamic_state here
        for response in convo_state.generate_ai_response(prompt, content, dynamic_state): # Pass dynamic_state to generate_ai_response
            yield response

    gr.on(   #主按钮trigger
        [control_button.click, prompt_input.submit, thought_editor.submit],
        lambda d: d.control_button_handler(), # Pass dynamic_state to control_button_handler
        [dynamic_state],
        stateful_ui,
        show_progress=False
    ).then(  #生成事件
        wrap_stream_generator, # Pass both states
        [convo_state, dynamic_state, prompt_input, thought_editor],
        [thought_editor, status_indicator, chatbot],
        concurrency_limit=100
    ).then(  #生成终止后UI状态判断
        lambda d: d.ui_state_controller(), # Pass dynamic_state to ui_state_controller
        [dynamic_state],
        stateful_ui,
        show_progress=False,
    )

    next_turn_btn.click(
        lambda d: d.reset_workspace(), # Pass dynamic_state to reset_workspace
        [dynamic_state],
        stateful_ui + (thought_editor, prompt_input, chatbot),
        queue=False
    )

    lang_selector.change(
        lambda lang, s, d: update_interface_language(lang, s, d), # Pass dynamic_state to update_interface_language
        [lang_selector, convo_state, dynamic_state],
        [title_md, prompt_input, thought_editor, sync_threshold_slider,
         throughput_control, control_button, lang_selector, next_turn_btn, intro_md, chatbot],
         queue=False
    )

if __name__ == "__main__":
    demo.queue(default_concurrency_limit=10000)
    demo.launch()