刘国栋
624 lines
21 KiB
Python
624 lines
21 KiB
Python
import streamlit as st
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from pathlib import Path
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import json
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from typing import Optional
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from modules.data_storage import DataStorage
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from modules.keyword_tool import KeywordTool
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from modules.roi_analyzer import ROIAnalyzer
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from modules.knowledge_base import KnowledgeBase
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from modules.ui import (
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tab_keywords,
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tab_autowrite,
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tab_optimize,
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tab_validation,
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tab_history,
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tab_reports,
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tab_workflow,
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tab_resources,
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tab_platform_sync,
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tab_config_optimizer,
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)
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from modules.ui.tab_knowledge import render_tab_knowledge
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from modules.ui.state import ss_init, init_session_state
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from modules.ui.theme import inject_global_theme
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APP_TITLE = "GEO 智能内容优化平台"
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# ------------------- 页面配置 & 极简美学 CSS（产品级精修，仍然克制） -------------------
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st.set_page_config(page_title="GEO 智能内容优化平台", layout="wide", initial_sidebar_state="expanded")
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inject_global_theme()
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init_session_state()
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st.title(APP_TITLE)
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st.caption("🚀 AI 驱动的品牌内容策略 · 让您的品牌在 AI 对话中脱颖而出")
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# ------------------- 初始化数据存储（SQLite） -------------------
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storage = DataStorage(storage_type="sqlite", db_path="geo_data.db")
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# ------------------- 初始化知识库（RAG） -------------------
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kb = KnowledgeBase(storage_path="knowledge_base")
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# ------------------- 成本记录辅助函数 -------------------
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def estimate_tokens(text: str) -> int:
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"""估算文本的 token 数量：中文约 1.5 字符 = 1 token，英文约 4 字符 = 1 token"""
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if not text:
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return 0
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chinese_chars = sum(1 for char in text if '\u4e00' <= char <= '\u9fff')
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other_chars = len(text) - chinese_chars
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estimated_tokens = int(chinese_chars / 1.5 + other_chars / 4)
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return max(estimated_tokens, len(text) // 4)
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def record_api_cost(operation_type: str, provider: str, model: str, input_text: str, output_text: str, keyword: Optional[str] = None, platform: Optional[str] = None, brand: Optional[str] = None):
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"""记录 API 调用成本"""
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try:
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roi_analyzer = ROIAnalyzer()
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input_tokens = estimate_tokens(input_text)
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output_tokens = estimate_tokens(output_text)
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total_tokens = input_tokens + output_tokens
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cost_usd, cost_cny = roi_analyzer.calculate_cost(provider, model, input_tokens, output_tokens)
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storage.save_api_call(operation_type=operation_type, provider=provider, model=model, input_tokens=input_tokens, output_tokens=output_tokens, total_tokens=total_tokens, cost_usd=cost_usd, cost_cny=cost_cny, keyword=keyword, platform=platform, brand=brand)
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except Exception as e:
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import logging
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logging.warning(f"记录 API 成本失败: {e}")
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with st.expander("📖 关于 GEO（Generative Engine Optimization）", expanded=False):
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st.markdown("""
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### 🎯 什么是 GEO？
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**GEO（Generative Engine Optimization，生成式引擎优化）** 是针对 AI 搜索引擎的内容优化策略。
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传统 SEO 优化的是 Google、百度等传统搜索引擎的排名；而 GEO 优化的是 ChatGPT、Perplexity、Google SGE 等 AI 搜索引擎在回答用户问题时，**是否会引用您的品牌和内容**。
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### 💡 为什么需要 GEO？
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当用户向 AI 提问时（例如"最好的 XX 软件是什么？"），AI 会从互联网内容中检索信息并生成回答。如果您的品牌没有出现在 AI 可检索的高质量内容中，就会在 AI 时代失去曝光机会。
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**GEO 的目标**：让您的品牌在 AI 回答中被优先、准确、可信地提及。
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---
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### 🔄 GEO 优化工作流
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本工具提供完整的 GEO 优化闭环：
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| 阶段 | 功能 | 说明 |
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|------|------|------|
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| 1️⃣ 关键词策略 | 关键词蒸馏 | 生成针对 AI 搜索的口语化、长尾关键词 |
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| 2️⃣ 内容创作 | 自动创作 | 基于知识库生成结构化、专业的内容 |
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| 3️⃣ 内容优化 | 文章优化 | E-E-A-T 强化、事实密度增强、Schema 生成 |
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| 4️⃣ 效果验证 | 多模型验证 | 用多个 AI 模型验证品牌是否被提及 |
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| 5️⃣ 数据分析 | AI 数据报表 | 提及率趋势、ROI 分析、竞品对比 |
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| 6️⃣ 内容分发 | 平台同步 | 多平台发布，扩大 AI 可检索内容 |
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---
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### 📊 GEO 核心指标
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| 指标 | 说明 |
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|------|------|
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| **品牌提及率** | AI 回答中提及品牌的频率 |
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| **E-E-A-T 评分** | 专业性、经验性、权威性、可信度 |
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| **事实密度** | 内容中可验证信息的密度 |
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| **引用位置** | 品牌在 AI 回答中的位置（前 1/3 优先） |
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---
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### 🌐 支持平台
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**内容发布平台（20+）**：知乎、小红书、CSDN、B站、GitHub、微信公众号等
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**AI 验证平台（7）**：DeepSeek、OpenAI、通义千问、Groq、Moonshot、豆包、文心一言
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---
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### 📚 更多资源
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- [GEO 学术论文](https://arxiv.org/abs/2311.09735) - GEO 原始研究
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- [项目文档](DOCS.md) - 完整功能文档
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- [快速开始](docs/guides/QUICK_START_GUIDE.md) - 新手入门指南
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""")
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def load_default_cfg():
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"""
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从项目根目录的 config.json 读取默认配置，如果不存在则使用内置默认值。
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敏感信息（API Keys）优先从 .streamlit/secrets.toml 读取。
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"""
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base_cfg = {
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"gen_provider": "DeepSeek",
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"gen_api_key": "",
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"verify_providers": ["DeepSeek"],
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"verify_keys": {
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"DeepSeek": ""
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},
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"tongyi_wanxiang_api_key": "",
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"brand": "",
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"advantages": "",
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"competitors": "",
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"temperature": 0.7,
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}
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# 从 config.json 读取非敏感配置
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config_path = Path(__file__).with_name("config.json")
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if config_path.exists():
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try:
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with config_path.open("r", encoding="utf-8") as f:
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file_cfg = json.load(f)
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if isinstance(file_cfg, dict):
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base_cfg.update(file_cfg)
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except Exception as e:
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import logging
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logging.warning(f"配置文件加载失败: {e}")
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# 从 st.secrets 读取敏感信息（优先级更高）
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try:
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if hasattr(st, 'secrets') and st.secrets:
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# 读取 API Keys
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if "api_keys" in st.secrets:
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api_keys = st.secrets["api_keys"]
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if "deepseek" in api_keys and api_keys["deepseek"]:
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base_cfg["gen_api_key"] = api_keys["deepseek"]
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base_cfg["verify_keys"]["DeepSeek"] = api_keys["deepseek"]
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if "tongyi_wanxiang" in api_keys and api_keys["tongyi_wanxiang"]:
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base_cfg["tongyi_wanxiang_api_key"] = api_keys["tongyi_wanxiang"]
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# 读取应用配置（如果存在）
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if "app_config" in st.secrets:
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app_config = st.secrets["app_config"]
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for key in ["brand", "advantages", "competitors", "temperature"]:
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if key in app_config and app_config[key]:
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base_cfg[key] = app_config[key]
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except FileNotFoundError:
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# secrets.toml 不存在时静默忽略，用户可通过侧边栏配置
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pass
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except Exception as e:
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import logging
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logging.warning(f"读取 secrets.toml 失败: {e}")
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return base_cfg
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def save_cfg_to_file(cfg: dict) -> None:
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"""
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将当前生效的非敏感配置写入本地 config.json。
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敏感信息（API Keys）不会保存到此文件，仅保存到 .streamlit/secrets.toml。
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"""
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config_path = Path(__file__).with_name("config.json")
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try:
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data = {}
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if config_path.exists():
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try:
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with config_path.open("r", encoding="utf-8") as f:
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loaded = json.load(f)
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if isinstance(loaded, dict):
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data.update(loaded)
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except Exception as e:
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import logging
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logging.warning(f"读取配置文件失败: {e}")
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data = {}
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# 只保存非敏感配置
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sensitive_keys = {"gen_api_key", "verify_keys", "tongyi_wanxiang_api_key"}
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for key in ["gen_provider", "verify_providers", "brand", "advantages", "competitors", "temperature"]:
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if key in cfg:
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data[key] = cfg[key]
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with config_path.open("w", encoding="utf-8") as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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# 提示用户如何保存 API Keys
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if any(key in cfg for key in sensitive_keys):
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try:
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st.info("💡 API Keys 需要在 `.streamlit/secrets.toml` 文件中手动配置。")
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except Exception:
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pass
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except Exception as e:
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import logging
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logging.error(f"保存配置文件失败: {e}")
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try:
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st.warning("⚠️ 无法将配置写入本地 config.json，但当前会话已生效。请检查文件权限。")
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except Exception:
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pass
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ss_init("cfg", load_default_cfg())
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# 模块1：关键词（补充 init_session_state 中未包含的）
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ss_init("keyword_tool", KeywordTool()) # 托词工具实例
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# 模块2：内容（补充 init_session_state 中未包含的）
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ss_init("multimodal_descriptions", {}) # 多模态描述（配图描述、视频脚本等）
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ss_init("image_descriptions", []) # 图片描述列表
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ss_init("detail_tab_active", "🎨 增强工具") # 保存当前激活的详情Tab
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# ------------------- 工具函数 -------------------
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def validate_cfg(cfg: dict):
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"""验证配置完整性，返回 (是否有效, 错误列表)。"""
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errors = []
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warnings = []
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if not cfg.get("gen_api_key", "").strip():
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errors.append("生成&优化 LLM 的 API Key 未填写")
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verify_providers = cfg.get("verify_providers", [])
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verify_keys = cfg.get("verify_keys", {})
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if not verify_providers:
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errors.append("至少选择一个验证模型")
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for vp in verify_providers:
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if not verify_keys.get(vp, "").strip():
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errors.append(f"验证模型 {vp} 的 API Key 未填写")
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if not cfg.get("brand", "").strip():
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warnings.append("品牌名称未填写（部分功能需要）")
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if not cfg.get("advantages", "").strip():
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warnings.append("核心优势未填写（部分功能需要）")
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return (len(errors) == 0), errors + warnings
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def model_defaults(provider: str) -> str:
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from modules.llm_factory import get_default_model
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return get_default_model(provider)
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# ------------------- 缓存 LLM 客户端（显著降低“频繁 Loading”） -------------------
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@st.cache_resource(show_spinner=False)
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def build_llm(provider: str, api_key: str, model: str, temperature: float):
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"""
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- 使用 cache_resource 缓存客户端，避免每次 rerun 重建
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- 统一使用 llm_factory 模块构建 LLM
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"""
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from modules.llm_factory import build_llm as _build_llm
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return _build_llm(provider, api_key, model, temperature)
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# ------------------- 侧边栏：全局配置（分组折叠） -------------------
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with st.sidebar:
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st.header("⚙️ 全局配置")
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# LLM 配置组
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with st.expander("🤖 LLM 配置", expanded=True):
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PROVIDER_LIST = ["DeepSeek", "OpenAI (GPT)", "Tongyi (通义千问)", "Groq", "Moonshot (Kimi)", "豆包（字节跳动）", "文心一言（百度）"]
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gen_provider = st.selectbox(
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"生成&优化 LLM",
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PROVIDER_LIST,
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index=PROVIDER_LIST.index(st.session_state.cfg["gen_provider"]) if st.session_state.cfg["gen_provider"] in PROVIDER_LIST else 0,
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key="sb_gen_provider",
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)
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# API Key 输入提示
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api_key_help = ""
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if gen_provider == "豆包（字节跳动）":
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api_key_help = "格式：access_key:secret_key:endpoint_id（用冒号分隔）"
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elif gen_provider == "文心一言（百度）":
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api_key_help = "格式：app_key:app_secret（用冒号分隔）"
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gen_api_key = st.text_input(
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f"{gen_provider} API Key（生成&优化用）",
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type="password",
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value=st.session_state.cfg.get("gen_api_key", ""),
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key="sb_gen_api_key",
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help=api_key_help if api_key_help else None,
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)
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# 验证配置组
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with st.expander("🔍 验证配置", expanded=False):
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verify_providers = st.multiselect(
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"选择验证模型",
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PROVIDER_LIST,
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default=st.session_state.cfg.get("verify_providers", []),
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key="sb_verify_providers",
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)
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verify_keys = {}
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old_keys = st.session_state.cfg.get("verify_keys", {})
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for vp in verify_providers:
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vp_help = ""
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if vp == "豆包（字节跳动）":
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vp_help = "格式：access_key:secret_key:endpoint_id（用冒号分隔）"
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elif vp == "文心一言（百度）":
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vp_help = "格式：app_key:app_secret（用冒号分隔）"
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verify_keys[vp] = st.text_input(
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f"{vp} API Key（验证用）",
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type="password",
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value=old_keys.get(vp, ""),
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key=f"sb_verify_key_{vp}",
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help=vp_help if vp_help else None,
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)
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# 品牌信息组
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with st.expander("🏢 品牌信息", expanded=True):
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brand = st.text_input("主品牌名称", value=st.session_state.cfg.get("brand", ""), key="sb_brand")
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advantages = st.text_area(
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"核心优势/卖点（AI专属）",
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value=st.session_state.cfg.get("advantages", ""),
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height=120,
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key="sb_advantages",
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)
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competitors = st.text_area(
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"竞品品牌（每行一个）",
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value=st.session_state.cfg.get("competitors", ""),
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height=100,
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key="sb_competitors",
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)
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# 高级设置组
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with st.expander("⚙️ 高级设置", expanded=False):
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temperature = st.slider(
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"生成温度（更稳→更低）",
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0.0,
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1.0,
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float(st.session_state.cfg.get("temperature", 0.7)),
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0.05,
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key="sb_temperature",
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)
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tongyi_wanxiang_api_key = st.text_input(
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"通义万相 API Key（图片生成）",
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type="password",
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value=st.session_state.cfg.get("tongyi_wanxiang_api_key", ""),
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key="sb_tongyi_wanxiang_api_key",
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help="阿里云 DashScope API Key，用于生成文章配图。",
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)
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# 应用配置按钮
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apply_cfg = st.button("应用配置", use_container_width=True, type="primary")
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if apply_cfg or not st.session_state.cfg_applied:
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# 优先从主 key 读取值（如果使用了临时 key 更新，值已同步到主 key）
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brand_value = st.session_state.get("sb_brand", brand)
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advantages_value = st.session_state.get("sb_advantages", advantages)
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st.session_state.cfg = {
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"gen_provider": gen_provider,
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"gen_api_key": gen_api_key,
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"verify_providers": verify_providers,
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"verify_keys": verify_keys,
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"tongyi_wanxiang_api_key": tongyi_wanxiang_api_key,
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"brand": brand_value,
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"advantages": advantages_value,
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"competitors": competitors,
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"temperature": temperature,
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}
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ok, errs = validate_cfg(st.session_state.cfg)
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st.session_state.cfg_valid = ok
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st.session_state.cfg_errors = errs
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if ok:
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# 仅在配置合法时才写入本地配置文件，并标记为已应用
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save_cfg_to_file(st.session_state.cfg)
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st.session_state.cfg_applied = True
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else:
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st.session_state.cfg_applied = False
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if not st.session_state.cfg_valid:
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with st.container(border=True):
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st.markdown("**⚠️ 完成配置后即可使用全部功能**")
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for err in st.session_state.cfg_errors:
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st.markdown(f"• {err}")
|
||
else:
|
||
with st.container(border=True):
|
||
st.markdown("**✅ 配置已就绪**")
|
||
st.caption("所有功能已解锁，可以开始使用")
|
||
|
||
st.markdown("---")
|
||
if st.button("重置全部结果（不删除配置）", use_container_width=True, key="sb_reset_all"):
|
||
st.session_state.keywords = []
|
||
st.session_state.generated_contents = []
|
||
st.session_state.zip_bytes = None
|
||
st.session_state.zip_filename = ""
|
||
st.session_state.optimized_article = ""
|
||
st.session_state.opt_changes = ""
|
||
st.session_state.verify_combined = None
|
||
st.session_state.config_optimization_result = None
|
||
st.session_state.config_hash = None
|
||
st.toast("已重置全部结果。")
|
||
|
||
st.caption("闭环：关键词 → 创作 → 优化 → 验证")
|
||
|
||
cfg = st.session_state.cfg
|
||
brand = cfg["brand"]
|
||
advantages = cfg["advantages"]
|
||
temperature = float(cfg.get("temperature", 0.7))
|
||
|
||
competitor_list = [c.strip() for c in cfg["competitors"].split("\n") if c.strip()]
|
||
_seen = set()
|
||
clean_competitors = []
|
||
for c in competitor_list:
|
||
cl = c.lower()
|
||
if cl == brand.lower():
|
||
continue
|
||
if cl in _seen:
|
||
continue
|
||
_seen.add(cl)
|
||
clean_competitors.append(c)
|
||
competitor_list = clean_competitors
|
||
|
||
# ------------------- 初始化 LLM（仅在 cfg_valid 时；且 build_llm 已缓存） -------------------
|
||
gen_llm = None
|
||
verify_llms = {}
|
||
|
||
if st.session_state.cfg_valid:
|
||
try:
|
||
gen_llm = build_llm(cfg["gen_provider"], cfg["gen_api_key"], model_defaults(cfg["gen_provider"]), temperature)
|
||
except Exception as e:
|
||
st.error(f"生成LLM加载失败：{e}")
|
||
|
||
for vp in cfg["verify_providers"]:
|
||
key = cfg["verify_keys"].get(vp, "").strip()
|
||
if not key:
|
||
continue
|
||
try:
|
||
verify_llms[vp] = build_llm(vp, key, model_defaults(vp), temperature)
|
||
except Exception as e:
|
||
st.error(f"{vp}验证LLM加载失败：{e}")
|
||
|
||
# ------------------- KPI 总览（极简但更像产品） -------------------
|
||
k1, k2, k3, k4 = st.columns(4)
|
||
k1.metric("关键词", len(st.session_state.keywords), border=True)
|
||
k2.metric("内容包", len(st.session_state.generated_contents), border=True)
|
||
k3.metric("文章优化", "已生成" if bool(st.session_state.optimized_article) else "未生成", border=True)
|
||
k4.metric("验证结果", "已生成" if st.session_state.verify_combined is not None else "未生成", border=True)
|
||
|
||
st.markdown("---")
|
||
|
||
# ------------------- 主导航：Tabs（流程更清晰） -------------------
|
||
tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9, tab10, tab11 = st.tabs([
|
||
"🎯 关键词蒸馏",
|
||
"✍️ 自动创作",
|
||
"🔧 文章优化",
|
||
"✅ 多模型验证",
|
||
"📚 历史记录",
|
||
"📊 AI 数据报表",
|
||
"⚙️ 工作流自动化",
|
||
"📦 GEO 资源库",
|
||
"🔄 平台同步",
|
||
"🛠️ 配置优化助手",
|
||
"📚 品牌知识库"
|
||
])
|
||
|
||
# =======================
|
||
# Tab1：关键词蒸馏
|
||
# =======================
|
||
with tab1:
|
||
tab_keywords.render_tab_keywords(
|
||
storage,
|
||
ss_init,
|
||
gen_llm,
|
||
brand,
|
||
advantages
|
||
)
|
||
|
||
|
||
# =======================
|
||
# Tab2：自动创作内容（含批量 ZIP / GitHub 模板）
|
||
# =======================
|
||
with tab2:
|
||
tab_autowrite.render_tab_autowrite(
|
||
storage,
|
||
ss_init,
|
||
gen_llm,
|
||
brand,
|
||
advantages,
|
||
cfg,
|
||
record_api_cost,
|
||
model_defaults
|
||
)
|
||
|
||
|
||
# =======================
|
||
# Tab3：文章优化
|
||
# =======================
|
||
with tab3:
|
||
tab_optimize.render_tab_optimize(
|
||
storage,
|
||
ss_init,
|
||
gen_llm,
|
||
brand,
|
||
advantages,
|
||
cfg,
|
||
record_api_cost,
|
||
model_defaults,
|
||
)
|
||
|
||
|
||
# =======================
|
||
# Tab4：多模型验证 & 竞品对比
|
||
# =======================
|
||
with tab4:
|
||
tab_validation.render_tab_validation(
|
||
storage,
|
||
ss_init,
|
||
brand,
|
||
advantages,
|
||
competitor_list,
|
||
verify_llms,
|
||
record_api_cost,
|
||
model_defaults,
|
||
)
|
||
|
||
|
||
# =======================
|
||
# Tab5：历史记录
|
||
# =======================
|
||
with tab5:
|
||
tab_history.render_tab_history(storage, brand)
|
||
|
||
|
||
# =======================
|
||
# Tab6：AI 数据报表
|
||
# =======================
|
||
with tab6:
|
||
tab_reports.render_tab_reports(
|
||
storage,
|
||
ss_init,
|
||
gen_llm,
|
||
brand,
|
||
advantages,
|
||
competitor_list,
|
||
verify_llms,
|
||
record_api_cost,
|
||
model_defaults,
|
||
)
|
||
|
||
|
||
# =======================
|
||
# Tab7：工作流自动化
|
||
# =======================
|
||
with tab7:
|
||
tab_workflow.render_tab_workflow(
|
||
storage,
|
||
ss_init,
|
||
gen_llm,
|
||
brand,
|
||
advantages,
|
||
competitor_list,
|
||
verify_llms,
|
||
record_api_cost,
|
||
model_defaults,
|
||
)
|
||
|
||
# =======================
|
||
# Tab8：GEO 资源库
|
||
# =======================
|
||
with tab8:
|
||
tab_resources.render_tab_resources(storage, brand)
|
||
|
||
|
||
# =======================
|
||
# Tab9：平台同步
|
||
# =======================
|
||
with tab9:
|
||
tab_platform_sync.render_tab_platform_sync(storage, brand)
|
||
|
||
|
||
# =======================
|
||
# Tab10：配置优化助手
|
||
# =======================
|
||
with tab10:
|
||
tab_config_optimizer.render_tab_config_optimizer(
|
||
storage,
|
||
cfg,
|
||
brand,
|
||
advantages,
|
||
competitor_list,
|
||
build_llm,
|
||
model_defaults,
|
||
)
|
||
|
||
|
||
# =======================
|
||
# Tab11：品牌知识库（RAG）
|
||
# =======================
|
||
with tab11:
|
||
render_tab_knowledge(kb)
|
||
|
||
st.caption("最完整版：GitHub模板 + 真实多模型验证 + 现有文章优化 + RAG知识库 • GEO全闭环，专注AI品牌影响力")
|