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modify: update apps

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ntnt
2025-11-02 21:56:35 +08:00
parent 5825cf81b7
commit d8eea8e3a9
6 changed files with 111 additions and 86 deletions
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6
CHANGELOG.md
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@@ -3,7 +3,7 @@
## [1.1.0] - 2025-08-28
### Added
- Integrated GPT-based analysis for comprehensive traffic safety insights
- Integrated AI-based analysis for comprehensive traffic safety insights
- Added automated report generation with AI-powered recommendations
- Implemented natural language query processing for data exploration
- Added export functionality for analysis reports (PDF/CSV formats)
@@ -22,7 +22,7 @@
- Addressed memory leaks in large dataset processing
### Documentation
- Updated README with new GPT analysis features and usage examples
- Updated README with new AI analysis features and usage examples
- Added API documentation for extended functionality
- Included sample datasets and tutorial guides
@@ -44,4 +44,4 @@
### Fixed
- Resolved session state KeyError.
- Resolved session state KeyError.

158
app.py
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@@ -294,9 +294,9 @@ def run_streamlit_app():
# Add OpenAI API key input in sidebar
st.sidebar.markdown("---")
st.sidebar.subheader("GPT API 配置")
openai_api_key = st.sidebar.text_input("GPT API Key", value='sk-sXY934yPqjh7YKKC08380b198fEb47308cDa09BeE23d9c8a', type="password", help="用于GPT分析结果的API密钥")
open_ai_base_url = st.sidebar.text_input("GPT Base Url", value='https://aihubmix.com/v1', type='default')
st.sidebar.subheader("AI API 配置")
openai_api_key = st.sidebar.text_input("AI API Key", value='sk-sXY934yPqjh7YKKC08380b198fEb47308cDa09BeE23d9c8a', type="password", help="用于 AI 分析结果的 API 密钥")
open_ai_base_url = st.sidebar.text_input("AI Base Url", value='https://aihubmix.com/v1', type='default')
# Process data only when Apply button is clicked
if apply_button and accident_file and strategy_file:
@@ -404,14 +404,14 @@ def run_streamlit_app():
tab_labels = [
"🏠 总览",
"📍 事故热点",
"🔍 AI 分析",
"📈 预测模型",
"📊 模型评估",
"⚠️ 异常检测",
"📝 策略评估",
"⚖️ 策略对比",
"🧪 情景模拟",
"🔍 GPT 分析",
"📍 事故热点",
]
default_tab = st.session_state.get("active_tab", tab_labels[0])
if default_tab not in tab_labels:
@@ -426,17 +426,94 @@ def run_streamlit_app():
st.session_state["active_tab"] = selected_tab
if selected_tab == "📍 事故热点":
if selected_tab == "🏠 总览":
if render_overview is not None:
render_overview(base, region_sel, start_dt, end_dt, strat_filter)
else:
st.warning("概览模块未能加载，请检查 `ui_sections/overview.py`。")
elif selected_tab == "📍 事故热点":
if render_hotspot is not None:
render_hotspot(accident_records, accident_source_name)
else:
st.warning("事故热点模块未能加载，请检查 `ui_sections/hotspot.py`。")
elif selected_tab == "🏠 总览":
if render_overview is not None:
render_overview(base, region_sel, start_dt, end_dt, strat_filter)
elif selected_tab == "🔍 AI 分析":
from openai import OpenAI
st.subheader("AI 数据分析与改进建议")
if not HAS_OPENAI:
st.warning("未安装 `openai` 库。请安装后重试。")
elif not openai_api_key:
st.info("请在左侧边栏输入 OpenAI API Key 以启用 AI 分析。")
else:
st.warning("概览模块未能加载，请检查 `ui_sections/overview.py`。")
if all_strategy_types:
# Generate results if not already
results, recommendation = generate_output_and_recommendations(base, all_strategy_types,
region=region_sel if region_sel != '全市' else '全市')
df_res = pd.DataFrame(results).T
kpi_json = json.dumps(kpi, ensure_ascii=False, indent=2)
results_json = df_res.to_json(orient="records", force_ascii=False)
recommendation_text = recommendation
# Prepare data to send
data_to_analyze = {
"kpis": kpi_json,
"strategy_results": results_json,
"recommendation": recommendation_text
}
data_str = json.dumps(data_to_analyze, ensure_ascii=False)
prompt = (
"你是一名资深交通安全数据分析顾问。请基于以下结构化数据输出一份专业报告，需包含：\n"
"1. 核心指标洞察：按要点总结事故趋势、显著波动及可能原因。\n"
"2. 策略绩效评估：对比主要策略的优势、短板与适用场景。\n"
"3. 优化建议：为短期（0-3个月）、中期（3-12个月）与长期（12个月以上）分别给出2-3条可操作措施。\n"
"请保持正式语气，引用关键数值支撑结论，并用清晰的小节或列表呈现。\n"
f"数据摘要：{data_str}\n"
)
if st.button("上传数据至 AI 并获取分析"):
if not openai_api_key.strip():
st.info("请提供有效的 AI API Key。")
elif not open_ai_base_url.strip():
st.info("请提供可访问的 AI Base Url。")
else:
try:
client = OpenAI(
base_url=open_ai_base_url,
# sk-xxx替换为自己的key
api_key=openai_api_key
)
st.markdown("### AI 分析结果与改进思路")
placeholder = st.empty()
accumulated_response: list[str] = []
with st.spinner("AI 正在生成专业报告，请稍候…"):
stream = client.chat.completions.create(
model="gpt-5-mini",
messages=[
{
"role": "system",
"content": "You are a professional traffic safety analyst who writes concise, well-structured Chinese reports."
},
{"role": "user", "content": prompt},
],
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta if chunk.choices else None
piece = getattr(delta, "content", None) if delta else None
if piece:
accumulated_response.append(piece)
placeholder.markdown("".join(accumulated_response), unsafe_allow_html=True)
final_text = "".join(accumulated_response)
if not final_text:
placeholder.info("AI 未返回可用内容，请稍后重试或检查凭据配置。")
except Exception as e:
st.error(f"调用 OpenAI API 失败：{str(e)}")
else:
st.warning("没有策略数据可供分析。")
# Update refresh time
st.session_state['last_refresh'] = datetime.now()
elif selected_tab == "📈 预测模型":
if render_forecast is not None:
@@ -652,67 +729,6 @@ def run_streamlit_app():
else:
st.info("请设置模拟参数并点击“应用模拟参数”按钮。")
# --- New Tab 8: GPT 分析
elif selected_tab == "🔍 GPT 分析":
from openai import OpenAI
st.subheader("GPT 数据分析与改进建议")
# open_ai_key = f"sk-dQhKOOG48iVEfgJfAb14458dA4474fB09aBbE8153d4aB3Fc"
if not HAS_OPENAI:
st.warning("未安装 `openai` 库。请安装后重试。")
elif not openai_api_key:
st.info("请在左侧边栏输入 OpenAI API Key 以启用 GPT 分析。")
else:
if all_strategy_types:
# Generate results if not already
results, recommendation = generate_output_and_recommendations(base, all_strategy_types,
region=region_sel if region_sel != '全市' else '全市')
df_res = pd.DataFrame(results).T
kpi_json = json.dumps(kpi, ensure_ascii=False, indent=2)
results_json = df_res.to_json(orient="records", force_ascii=False)
recommendation_text = recommendation
# Prepare data to send
data_to_analyze = {
"kpis": kpi_json,
"strategy_results": results_json,
"recommendation": recommendation_text
}
data_str = json.dumps(data_to_analyze, ensure_ascii=False)
prompt = str(f"""
请分析以下交通安全分析结果，包括KPI指标、策略评估结果和推荐。
提供数据结果的详细分析，以及改进思路和建议。
数据：{str(data_str)}
""")
if st.button("上传数据至 GPT 并获取分析"):
if False:
st.info("请将 GPT Base Url 更新为实际可访问的接口地址。")
else:
try:
client = OpenAI(
base_url=open_ai_base_url,
# sk-xxx替换为自己的key
api_key=openai_api_key
)
response = client.chat.completions.create(
model="gpt-5-mini",
messages=[
{"role": "system", "content": "You are a helpful assistant that analyzes traffic safety data."},
{"role": "user", "content": prompt}
],
stream=False
)
gpt_response = response.choices[0].message.content
st.markdown("### GPT 分析结果与改进思路")
st.markdown(gpt_response, unsafe_allow_html=True)
except Exception as e:
st.error(f"调用 OpenAI API 失败：{str(e)}")
else:
st.warning("没有策略数据可供分析。")
# Update refresh time
st.session_state['last_refresh'] = datetime.now()
else:
st.info("请先在左侧上传事故数据与策略数据，并点击“应用数据与筛选”按钮。")

2
docs/usage.md
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@@ -41,7 +41,7 @@ Use the sidebar form labelled “数据与筛选”.
- **📝 策略评估 (Strategy evaluation)** — Aggregates metrics per strategy type, recommends the best option, writes `strategy_evaluation_results.csv`, and updates `recommendation.txt`.
- **⚖️ 策略对比 (Strategy comparison)** — side-by-side metrics for selected strategies, useful for “what worked best last month” reviews.
- **🧪 情景模拟 (Scenario simulation)** — apply intervention models (persistent/decay, lagged effects) to test potential roll-outs.
- **🔍 GPT 分析** — enter your own OpenAI-compatible API key and base URL in the sidebar to generate narrative insights. Keys are read at runtime only.
- **🔍 AI 分析** — 默认示例 API Key/Base URL 已预填，可直接体验；如需切换自有凭据，可在侧边栏更新后生成洞察（运行时读取，不会写入磁盘）。
- **📍 事故热点 (Hotspot)** — reuse the already uploaded accident data to identify high-risk intersections and produce targeted mitigation ideas; no separate hotspot upload is required.
Each tab remembers the active filters from the sidebar so results stay consistent.

3
readme.md
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@@ -9,7 +9,7 @@
- 检测异常事故点
- 评估交通策略效果并提供推荐
- 识别事故热点路口并生成风险分级与整治建议
- 支持 GPT 分析生成自然语言洞察
- 支持 AI 分析生成自然语言洞察
## 安装步骤
@@ -91,6 +91,7 @@ openai>=2.0.0
- **环境变量**（可选）：
- `LOG_LEVEL=DEBUG`：启用详细日志
- 示例：`export LOG_LEVEL=DEBUG`（Linux/macOS）或 `set LOG_LEVEL=DEBUG`（Windows）
- **AI 分析凭据**：应用内已预填可用的示例 API Key 与 Base URL，可直接体验；如需使用自有服务，可在侧边栏替换后即时生效。
## 示例数据

22
services/hotspot.py
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@@ -1,7 +1,7 @@
from __future__ import annotations
from datetime import datetime
from typing import Iterable
from typing import Iterable, Optional
import numpy as np
import pandas as pd
@@ -211,11 +211,24 @@ def generate_hotspot_strategies(
return strategies
def serialise_datetime_columns(df: pd.DataFrame, columns: Iterable[str]) -> pd.DataFrame:
def serialise_datetime_columns(df: pd.DataFrame, columns: Optional[Iterable[str]] = None) -> pd.DataFrame:
result = df.copy()
if columns is None:
columns = result.columns
for column in columns:
if column in result.columns and pd.api.types.is_datetime64_any_dtype(result[column]):
result[column] = result[column].dt.strftime("%Y-%m-%d %H:%M:%S")
if column not in result.columns:
continue
series = result[column]
if pd.api.types.is_datetime64_any_dtype(series):
result[column] = series.dt.strftime("%Y-%m-%d %H:%M:%S")
else:
has_timestamp = series.map(lambda value: isinstance(value, (datetime, pd.Timestamp))).any()
if has_timestamp:
result[column] = series.map(
lambda value: value.strftime("%Y-%m-%d %H:%M:%S")
if isinstance(value, (datetime, pd.Timestamp))
else value
)
return result
@@ -224,4 +237,3 @@ def _mode_fallback(series: pd.Series) -> str:
return ""
mode = series.mode()
return str(mode.iloc[0]) if not mode.empty else str(series.iloc[0])

6
ui_sections/hotspot.py
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@@ -154,10 +154,7 @@ def render_hotspot(accident_records, accident_source_name: str | None) -> None:
)
with download_cols[1]:
serializable = serialise_datetime_columns(
top_hotspots.reset_index(),
columns=[col for col in top_hotspots.columns if "time" in col or "date" in col],
)
serializable = serialise_datetime_columns(top_hotspots.reset_index())
report_payload = {
"analysis_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"time_window": time_window,
@@ -186,4 +183,3 @@ def render_hotspot(accident_records, accident_source_name: str | None) -> None:
preview_cols = ["事故时间", "所在街道", "事故类型", "事故具体地点", "道路类型"]
preview_df = hotspot_data[preview_cols].copy()
st.dataframe(preview_df.head(10), use_container_width=True)