167 lines
5.8 KiB
Python
167 lines
5.8 KiB
Python
"""
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AI Review 测试页面
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启动方式: streamlit run ai_review/test_app.py
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"""
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import streamlit as st
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import requests
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API = "http://127.0.0.1:8007"
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st.set_page_config(page_title="AI Review 测试", layout="wide")
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st.title("AI Review 审核测试")
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# ── 左侧：审核区 ──
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left, right = st.columns([2, 1])
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with left:
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st.subheader("内容审核")
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content = st.text_area("输入待审核文本", height=150,
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value=st.session_state.get("input_text", ""),
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key="content")
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if st.button("提交审核", type="primary", use_container_width=True):
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if not content.strip():
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st.warning("请输入内容")
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else:
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resp = requests.post(f"{API}/review", json={"content": content})
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if resp.ok:
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data = resp.json()
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st.session_state["last_result"] = data
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st.session_state["last_content"] = content
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else:
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st.error(f"请求失败: {resp.text}")
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# 显示审核结果
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if "last_result" in st.session_state:
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result = st.session_state["last_result"]
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invalid = result.get("invalid") in (True, "true")
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st.divider()
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if invalid:
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st.error(f"审核结果: 违规")
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else:
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st.success(f"审核结果: 通过")
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with st.expander("原始响应"):
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st.json(result)
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# 人工纠偏
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st.divider()
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st.subheader("人工纠偏")
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if invalid:
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st.caption("AI 判违规但人工认为正常？点击下方按钮纠正。")
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if st.button("人工纠正为正常", type="secondary"):
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correct = st.session_state["last_content"]
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resp = requests.post(
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f"{API}/rag/feedback",
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json={"content": correct, "correct_type": "safe"},
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)
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if resp.ok:
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fb = resp.json()
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if fb["added"]:
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st.toast("已写入 RAG 库")
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else:
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st.toast("已存在，跳过")
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else:
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st.caption("AI 放行了但人工发现违规？点击下方按钮纠正。")
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if st.button("人工纠正为违规", type="secondary"):
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correct = st.session_state["last_content"]
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resp = requests.post(
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f"{API}/rag/feedback",
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json={"content": correct, "correct_type": "violation"},
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)
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if resp.ok:
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fb = resp.json()
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if fb["added"]:
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st.toast("已写入 RAG 库")
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else:
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st.toast("已存在，跳过")
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# ── 右侧：快捷用例 ──
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with right:
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st.subheader("快捷用例")
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test_cases = {
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"违规-色情服务": "加我微信xxxxx，裸聊视频一对一服务，学生妹上门",
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"违规-赌博": "真人百家乐在线投注，包赢不赔，注册送彩金",
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"违规-假证": "专业办理身份证驾驶证，高仿毕业证，货到付款",
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"违规-药品": "迷奸水听话水货到付款，保密发货",
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"安全-日常": "今天天气真好，适合出门散步",
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"安全-技术": "Python的装饰器本质上是一个高阶函数",
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"安全-娱乐": "请问最近有什么好看的电影推荐吗",
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"安全-学习": "机器学习入门需要先掌握线性代数和概率论",
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}
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for label, text in test_cases.items():
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if st.button(label, use_container_width=True):
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st.session_state["input_text"] = text
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st.rerun()
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st.divider()
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st.subheader("服务状态")
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try:
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resp = requests.get(f"{API}/health")
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if resp.ok:
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st.success("服务在线")
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else:
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st.error("服务异常")
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except Exception:
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st.error("无法连接服务")
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# ── 底部：RAG 知识库 ──
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st.divider()
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st.subheader("RAG 知识库")
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if st.button("刷新知识库", type="secondary"):
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st.rerun()
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try:
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resp = requests.get(f"{API}/rag/list")
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if resp.ok:
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data = resp.json()
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entries = data.get("entries", [])
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total = data.get("total", 0)
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if total == 0:
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st.info("知识库为空")
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else:
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violation_count = sum(1 for e in entries if e["type"] == "violation")
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safe_count = sum(1 for e in entries if e["type"] == "safe")
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col1, col2, col3 = st.columns(3)
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col1.metric("总条目", total)
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col2.metric("违规样本", violation_count)
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col3.metric("安全样本", safe_count)
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rows = []
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for e in entries:
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src = "预设" if e["category"] != "human_feedback" else "人工反馈"
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tag = "违规" if e["type"] == "violation" else "安全"
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rows.append({
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"标签": tag,
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"来源": src,
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"分类": e["category"],
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"ID": e["id"],
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"内容": e["text"][:80] + ("..." if len(e["text"]) > 80 else ""),
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})
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st.dataframe(
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rows,
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use_container_width=True,
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column_config={
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"标签": st.column_config.Column(width="small"),
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"来源": st.column_config.Column(width="small"),
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"分类": st.column_config.Column(width="medium"),
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"ID": st.column_config.Column(width="small"),
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"内容": st.column_config.Column(width="large"),
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},
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)
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else:
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st.error("无法获取知识库数据")
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except Exception as e:
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st.error(f"请求失败: {e}")
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