import gradio as gr import time from smart_writer_kit.agent_for_streaming_completion import fetch_flow_suggestion_agent, accept_flow_suggestion_agent from smart_writer_kit.agent_for_inspiration_expansion import fetch_inspiration_agent, apply_inspiration_agent from smart_writer_kit.agent_for_paragraph_continuation import fetch_paragraph_continuation_agent from smart_writer_kit.agent_for_prompt_suggestion import fetch_prompt_suggestions_agent from smart_writer_kit.agent_for_outline_update import update_outline_status_agent from smart_writer_kit.agent_for_kb_update import suggest_new_kb_terms_agent from ui_components.debounce_manager import DebounceManager # --- Mock Data (for UI population only) --- MOCK_STYLE = """故事：人类逐渐走向消亡时，人形机器人的休闲生活。 风格：自然平淡，文字细腻，描绘未来的荒凉与宁静交织的景象。 主题：探索人类与机器的界限，记忆与身份的意义。 """ MOCK_KNOWLEDGE_BASE = [ ["Alpha", "故事的主角，女性人形机器人，外表与人类无异。性格有线"], ["横滨", "故事发生的主要城市。由于海平面上升，城市部分地区被淹没，形成独特的水上景观。"] ] MOCK_SHORT_TERM_OUTLINE = [ [False, "故事的场景设定：海平面上升后的城市景观。"], [False, "介绍主角 Alpha 的日常生活和她与其他机器人的互动。"], [False, "Alpha 发现了一张旧照片，勾起了她对过去人类生活的好奇心。"], [False, "奶油蛋糕的制作方法。"] ] ## 按日常向动画剧情走向写的长纲要。具体。 MOCK_LONG_TERM_OUTLINE = [ [False, "介绍故事背景。人类逐渐减少，机器人和人的互动。"], [False, "Alpha 决定离开居住地，到东京寻找失散的朋友。"], [False, "月亮变成了一个巨大的 Disco 灯球。机器人不受控制地开始跳舞，导致全球范围内的混乱。"], ] # --- UI Helper Functions --- def get_stats(text): """Calculate word count and read time.""" if not text: return "0 Words | 0 mins" words = len(text.split()) read_time = max(1, words // 200) # Average reading speed return f"{words} Words | ~{read_time} mins" # --- UI Construction --- def create_smart_writer_tab(): # Initialize DebounceManager debounce_manager = DebounceManager(debounce_time=2.0, tick_time=0.3, loading_text="稍后开始续写") with gr.Row(equal_height=False, elem_id="indicator-writing-tab"): # --- Left Column: Entity Console --- with gr.Column(scale=1) as left_panel: style_input = gr.Textbox( label="整体故事和风格", lines=8, value=MOCK_STYLE, interactive=True ) with gr.Accordion("写作知识库", open=True): kb_input = gr.Dataframe( headers=["名称", "说明"], datatype=["str", "str"], value=MOCK_KNOWLEDGE_BASE, interactive=True, wrap=True ) with gr.Row(): btn_suggest_kb = gr.Button("🔍 提取新词条", size="sm") suggested_kb_dataframe = gr.Dataframe( headers=["Term", "Description"], datatype=["str", "str"], visible=False, interactive=False, label="推荐词条" ) with gr.Accordion("当前章节大纲", open=True): short_outline_input = gr.Dataframe( headers=["Done", "Task"], datatype=["bool", "str"], value=MOCK_SHORT_TERM_OUTLINE, interactive=True, label="当前章节大纲", col_count=(2, "fixed"), ) with gr.Row(): btn_sync_outline = gr.Button("🔄 同步状态", size="sm") with gr.Accordion("故事整体大纲", open=False): long_outline_input = gr.Dataframe( headers=["Done", "Task"], datatype=["bool", "str"], value=MOCK_LONG_TERM_OUTLINE, interactive=True, label="故事总纲", col_count=(2, "fixed"), ) # --- Right Column: Writing Canvas --- with gr.Column(scale=5): # --- RIBBON AREA (Top of Editor) --- with gr.Row(variant="panel", elem_classes=["ribbon-container"]): # Area 1: Real-time Continuation (Flow) with gr.Column(scale=1, min_width=200): flow_suggestion_display = gr.Textbox( show_label=True, label="实时续写建议", placeholder="（等待输入或点击“换一个”...）", lines=3, interactive=False, elem_classes=["flow-suggestion-box"], ) btn_accept_flow = gr.Button("采纳续写 (Tab)", size="sm", variant="primary", elem_id='btn-action-accept-flow') btn_change_flow = gr.Button("换一个 (Shift+Tab)", size="sm", elem_id='btn-action-change-flow') # Debounce Progress Indicator (Using Manager) debounce_state, debounce_timer, debounce_progress = debounce_manager.create_ui() debounce_progress.visible = True # Area 2: Paragraph Continuation (Inspiration) with gr.Column(scale=1, min_width=200): inspiration_prompt_input = gr.Textbox( label="续写提示", placeholder="例如：写一段关于...的描写", lines=2 ) prompt_suggestions_dataset = gr.Dataset( label="推荐提示 (点击填入)", components=[gr.Textbox(visible=False)], samples=[["生成建议..."], ["生成建议..."], ["生成建议..."]], type="values" ) refresh_suggestions_btn = gr.Button("🎲 换一批建议", size="sm", variant="secondary") # Combined trigger with gr.Row(): btn_generate_para = gr.Button("整段续写 (Cmd+Enter)", size="sm", variant="primary", elem_id="btn-action-create-paragraph") btn_change_para = gr.Button("换一个", size="sm") btn_accept_para = gr.Button("采纳", size="sm") para_suggestion_display = gr.Textbox( show_label=False, placeholder="（点击“整段续写”生成内容...）", lines=3, interactive=False ) # Area 3: Adjust/Polish (Placeholder) with gr.Column(scale=1, min_width=200): gr.Markdown("#### 🛠️ 调整润色") gr.Markdown("(Coming Soon)") # --- TOOLBAR --- with gr.Row(elem_classes=["toolbar"]): stats_display = gr.Markdown("0 Words | 0 mins") # --- EDITOR --- editor = gr.Textbox( label="沉浸写作画布", placeholder="开始你的创作...", lines=25, # Reduced lines slightly to accommodate ribbon elem_classes=["writing-editor"], elem_id="writing-editor", show_label=False, ) # --- Interactions --- # 1. Stats editor.change(fn=get_stats, inputs=editor, outputs=stats_display) # 2. Flow Suggestion Logic (Using DebounceManager) # Bind reset logic to editor change editor.change( fn=debounce_manager.reset, inputs=[editor, style_input, kb_input, short_outline_input, long_outline_input], # Capture all context as payload outputs=[debounce_state, debounce_timer, debounce_progress] ) # Bind tick logic def flow_suggestion_trigger(editor_content, style, kb, short_outline, long_outline): return fetch_flow_suggestion_agent(editor_content, style, kb, short_outline, long_outline) # Note: debounce_manager.tick calls the trigger function. # The lambda is used to pass the specific trigger function for this tab. debounce_timer.tick( fn=lambda s: debounce_manager.tick(s, flow_suggestion_trigger), inputs=[debounce_state], outputs=[debounce_progress, debounce_state, debounce_timer, flow_suggestion_display] ) btn_change_flow.click(fn=fetch_flow_suggestion_agent, inputs=[editor, style_input, kb_input, short_outline_input, long_outline_input], outputs=flow_suggestion_display) accept_flow_fn_inputs = [editor, flow_suggestion_display] # accept_flow_suggestion_agent returns modified editor text btn_accept_flow.click( fn=lambda e, s: (accept_flow_suggestion_agent(e, s), ""), # Accept and clear suggestion inputs=accept_flow_fn_inputs, outputs=[editor, flow_suggestion_display] ) # 3. Paragraph Continuation Logic (Updated with prompt input) def generate_paragraph_wrapper(prompt_val, editor_val, style, kb, short, long_): return fetch_paragraph_continuation_agent(prompt_val, editor_val, style, kb, short, long_) for btn in [btn_generate_para, btn_change_para]: btn.click( fn=generate_paragraph_wrapper, inputs=[inspiration_prompt_input, editor, style_input, kb_input, short_outline_input, long_outline_input], outputs=[para_suggestion_display] ) def accept_para_wrapper(curr, new): # Reuse apply_inspiration_agent but extract text. # It returns (new_text, modal_update, empty_string) res = apply_inspiration_agent(curr, new) return res[0], "" btn_accept_para.click( fn=accept_para_wrapper, inputs=[editor, para_suggestion_display], outputs=[editor, para_suggestion_display] ) # Suggestions Logic # Trigger for suggestion generation def refresh_suggestions_wrapper(editor_content, style, kb, short_outline, long_outline): s1, s2, s3 = fetch_prompt_suggestions_agent(editor_content, style, kb, short_outline, long_outline) # Return a gr.update object to properly update the Dataset component return gr.update(samples=[[s1], [s2], [s3]]) refresh_suggestions_btn.click( fn=refresh_suggestions_wrapper, inputs=[editor, style_input, kb_input, short_outline_input, long_outline_input], outputs=[prompt_suggestions_dataset] ) # Dataset click -> fill prompt input def fill_prompt_from_dataset(val): return val[0] prompt_suggestions_dataset.click( fn=fill_prompt_from_dataset, inputs=prompt_suggestions_dataset, outputs=inspiration_prompt_input ) # 4. Agent-based Context Updates btn_sync_outline.click( fn=update_outline_status_agent, inputs=[short_outline_input, editor], outputs=[short_outline_input] ) btn_suggest_kb.click( fn=suggest_new_kb_terms_agent, inputs=[kb_input, editor], outputs=[suggested_kb_dataframe] )