app.py

import gradio as gr
import requests
import re

# --- 配置 ---
# !!! 重要: 请将此 URL 替换为您的 API 服务器的实际地址
API_BASE_URL = "http://134.175.222.87:5006"  # 这是一个示例，请务必修改
START_ANALYSIS_ENDPOINT = f"{API_BASE_URL}/start_analysis_session"
ANALYZE_NEXT_ENDPOINT = f"{API_BASE_URL}/analyze_next"


# --- 核心逻辑函数 ---

def format_report(analysis_result):
    """将API返回的JSON格式化为美观的Markdown文本"""
    if not analysis_result or "ai_analysis" not in analysis_result:
        return "### ❌ 错误\n收到的分析结果格式不正确。"

    analysis = analysis_result["ai_analysis"]

    def create_tag_list(items):
        if not items:
            return "_暂无推断_"
        return " ".join([f"`{item}`" for item in items])

    assessment = analysis.get("评估体系", {})
    confidence = assessment.get("画像置信度", {})
    identity_level = assessment.get("身份明确度", "未知")
    badge_map = {"低": "🟢", "中": "🟡", "高": "🔴"}
    identity_badge = f"{badge_map.get(identity_level, '⚪️')} {identity_level}"

    report_md = f"""
    ## 深度人物画像报告
    > {analysis.get('核心摘要', 'AI未能生成摘要。')}
    ---
    #### 推断画像
    - **职业角色**: {create_tag_list(analysis.get("推断画像", {}).get("职业角色"))}
    - **能力/技能**: {create_tag_list(analysis.get("推断画像", {}).get("能力技能"))}
    - **兴趣爱好**: {create_tag_list(analysis.get("推断画像", {}).get("兴趣爱好"))}
    ---
    #### 关联网络
    - **关联人物**: {create_tag_list(analysis.get("关联网络", {}).get("关联人物"))}
    - **关联组织**: {create_tag_list(analysis.get("关联网络", {}).get("关联组织"))}
    ---
    #### 行为模式推断
    {analysis.get('行为模式推断', '暂无明确的行为模式推断。')}
    ---
    #### 评估体系
    - **身份明确度**: {identity_badge}
    - **画像总置信度**: **{confidence.get('总分', 0) * 100:.0f}%**
      - `职业置信度: {confidence.get('职业置信度', 0) * 100:.0f}%`
      - `爱好置信度: {confidence.get('爱好置信度', 0) * 100:.0f}%`
      - `关联网络置信度: {confidence.get('关联网络置信度', 0) * 100:.0f}%`
    """
    return report_md

def start_analysis(phone):
    """处理“生成画像”按钮点击事件"""
    if not phone or not re.match(r"^\d{7,15}$", phone):
        # 返回值对应 outputs 列表
        return (
            "号码格式不正确哦～",      # status_message
            gr.update(visible=False), # report_area
            gr.update(value=""),      # report_output
            gr.update(visible=False), # feedback_area
            gr.update(interactive=False), # correct_btn
            gr.update(interactive=False), # incorrect_btn
            None, 0, 0                # states
        )

    status_message = "正在启动分析会话，请稍候..."
    # 初始加载状态
    yield (
        status_message,
        gr.update(visible=False),
        gr.update(value=""),
        gr.update(visible=False),
        gr.update(interactive=False),
        gr.update(interactive=False),
        None, 0, 0
    )

    try:
        response = requests.post(START_ANALYSIS_ENDPOINT, data={"phone": phone}, timeout=20)
        response.raise_for_status()
        data = response.json()

        if not data.get("success"):
            raise Exception(data.get("message", "API返回错误但未提供消息"))

        total_count = data.get("total_count", 0)
        if total_count == 0:
            yield (
                "恭喜！您开启了隐身魔法呦👉👉", gr.update(visible=False), "", gr.update(visible=False),
                gr.update(interactive=False), gr.update(interactive=False), [], 0, 0
            )
            return
        
        references = data.get("data_references", [])
        analysis_result = data.get("analysis_result")
        report_md = format_report(analysis_result)
        status_message = f"发现 {total_count} 条线索，正在展示第 1 号情报..."
        next_btn_interactive = total_count > 1

        # 成功获取数据后的最终状态
        yield (
            status_message,
            gr.update(visible=True), # report_area
            gr.update(value=report_md), # report_output
            gr.update(visible=True), # feedback_area
            gr.update(interactive=True), # correct_btn
            gr.update(interactive=next_btn_interactive), # incorrect_btn
            references, 1, total_count
        )

    except (requests.exceptions.RequestException, Exception) as e:
        error_message = f"发生错误: {e}"
        yield (
            error_message, gr.update(visible=False), "", gr.update(visible=False),
            gr.update(interactive=False), gr.update(interactive=False), None, 0, 0
        )

def analyze_next_clue(references, current_index, total_count):
    """处理“换一条”按钮点击事件"""
    if not references or current_index >= total_count:
        yield (
            "所有线索已分析完毕，没有更多信息了。", # status_message
            gr.update(), # report_output (保持不变)
            gr.update(interactive=False), # correct_btn (可设为True或False，这里设为False)
            gr.update(interactive=False), # incorrect_btn
            references, current_index, total_count
        )
        return

    next_list_index = current_index
    reference_to_analyze = references[next_list_index]
    display_index = next_list_index + 1
    
    status_message = f"正在分析第 {display_index} / {total_count} 号情报..."
    
    # 加载时禁用按钮
    yield (
        status_message, gr.update(), 
        gr.update(interactive=False), gr.update(interactive=False),
        references, current_index, total_count
    )

    try:
        response = requests.post(ANALYZE_NEXT_ENDPOINT, json={"reference": reference_to_analyze}, timeout=20)
        response.raise_for_status()
        data = response.json()

        if not data.get("success"):
            raise Exception(data.get("message", "API返回分析失败"))

        analysis_result = data.get("analysis_result")
        report_md = format_report(analysis_result)
        new_current_index = current_index + 1
        
        status_message = f"第 {new_current_index} 号情报画像构建完毕！"
        next_btn_interactive = new_current_index < total_count

        yield (
            status_message,
            gr.update(value=report_md),
            gr.update(interactive=True),
            gr.update(interactive=next_btn_interactive),
            references, new_current_index, total_count
        )

    except (requests.exceptions.RequestException, Exception) as e:
        error_message = f"分析出错了: {e}"
        yield (
            error_message, gr.update(), 
            gr.update(interactive=True), gr.update(interactive=True), # 恢复按钮交互
            references, current_index, total_count
        )

def confirm_analysis():
    """处理“画像准确”按钮点击事件"""
    return (
        "感谢您的确认！AI酱很开心。🎉", # status_message
        gr.update(interactive=False), # correct_btn
        gr.update(interactive=False)  # incorrect_btn
    )

# --- Gradio 界面构建 ---
with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as demo:
    # 状态管理
    state_references = gr.State([])
    state_current_index = gr.State(0)
    state_total_count = gr.State(0)

    gr.Markdown("# AI深度人物画像系统")
    gr.Markdown("本系统利用AI对公开的互联网数据进行深度分析，构建推理性的人物画像...")

    with gr.Row():
        phone_input = gr.Textbox(label="手机号", placeholder="输入11位数字...", scale=3)
        start_button = gr.Button("生成画像", variant="primary", scale=1)

    status_message = gr.Textbox(label="状态", value="AI分析引擎待命中...", interactive=False)

    with gr.Column(visible=False) as report_area:
        report_output = gr.Markdown()
    
    with gr.Row(visible=False) as feedback_area:
        correct_btn = gr.Button("✅ 画像准确")
        incorrect_btn = gr.Button("❌ 与事实不符, 换一条")

    # --- 事件绑定 ---
    # **核心改动**: 将所有需要更新的组件都列在outputs中
    start_button.click(
        fn=start_analysis,
        inputs=[phone_input],
        outputs=[
            status_message, report_area, report_output, feedback_area, 
            correct_btn, incorrect_btn,
            state_references, state_current_index, state_total_count
        ]
    )
    
    incorrect_btn.click(
        fn=analyze_next_clue,
        inputs=[state_references, state_current_index, state_total_count],
        outputs=[
            status_message, report_output,
            correct_btn, incorrect_btn,
            state_references, state_current_index, state_total_count
        ]
    )
    
    correct_btn.click(
        fn=confirm_analysis,
        inputs=[],
        outputs=[status_message, correct_btn, incorrect_btn]
    )

if __name__ == "__main__":
    demo.launch()