modify: use deepseek api

CHANGELOG.md

@@ -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
 

Dockerfile

@@ -0,0 +1,36 @@
+# Use an official Python runtime as a parent image
+FROM python:3.12-slim
+
+# Prevents writing .pyc files and buffering stdout/stderr
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements first to leverage Docker cache
+COPY requirements.txt .
+
+# Install system dependencies (if needed) and Python deps
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app code
+COPY . .
+
+# Expose Streamlit default port
+EXPOSE 8501
+
+# Streamlit config: run headless and bind to 0.0.0.0
+ENV STREAMLIT_SERVER_HEADLESS=true
+ENV STREAMLIT_SERVER_ENABLE_CORS=false
+ENV STREAMLIT_SERVER_ENABLE_XSRF_PROTECTION=false
+ENV STREAMLIT_SERVER_PORT=8501
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
+
+# Run Streamlit
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

@@ -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-959e0b065c774b1db6e30bf7589680f9', 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://api.deepseek.com', 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="deepseek-chat",
+                                        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。")
 

docs/install.md

@@ -68,6 +68,25 @@ pip install streamlit-autorefresh openpyxl xlrd cryptography openai
 
 3. Open `http://localhost:8501` in your browser. The home page should load without import errors.
 
+## 5. Run with Docker (optional)
+
+If you prefer an isolated container build, use the included `Dockerfile`:
+
+```bash
+docker build -t trafficsafeanalyzer .
+docker run --rm -p 8501:8501 trafficsafeanalyzer
+```
+
+To work with local data, mount the host folder containing Excel files:
+
+```bash
+docker run --rm -p 8501:8501 \
+  -v "$(pwd)/sample:/app/sample" \
+  trafficsafeanalyzer
+```
+
+The container exposes Streamlit on port 8501 by default. Override configuration via environment variables when needed, for example `-e STREAMLIT_SERVER_PORT=8502`.
+
 ## Troubleshooting tips
 
 - **Missing package**: Re-run `pip install -r requirements.txt`.

docs/usage.md

@@ -4,7 +4,7 @@ TrafficSafeAnalyzer delivers accident analytics and decision support through a S
 
 ## Start the app
 
-1. Activate your virtual or conda environment.
+1. Activate your virtual or conda environment（或在容器中运行，见下）.
 2. From the project root, run:
 
    ```bash
@@ -13,6 +13,8 @@ TrafficSafeAnalyzer delivers accident analytics and decision support through a S
 
 3. Open `http://localhost:8501`. Keep the terminal running while you work in the browser.
 
+> 使用 Docker？运行 `docker build -t trafficsafeanalyzer .` 与 `docker run --rm -p 8501:8501 trafficsafeanalyzer` 后，同样访问 `http://localhost:8501`。
+
 ## Load input data
 
 Use the sidebar form labelled “数据与筛选”.
@@ -41,7 +43,7 @@ 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.

readme.md

@@ -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
 
@@ -19,7 +19,7 @@
 - Git
 - 可选：Docker（用于容器化部署）
 
-### 安装
+### 安装（本地环境）
 
 1. 克隆仓库：
 
@@ -63,6 +63,28 @@ streamlit run app.py
       streamlit run app.py
       ```
 
+### 使用 Docker 运行
+
+项目根目录已经包含 `Dockerfile`，无需额外配置即可容器化运行：
+
+```bash
+# 构建镜像
+docker build -t trafficsafeanalyzer .
+
+# 以临时容器方式启动
+docker run --rm -p 8501:8501 trafficsafeanalyzer
+```
+
+运行后访问 `http://localhost:8501` 即可。若需加载主机上的数据文件，可通过挂载方式注入：
+
+```bash
+docker run --rm -p 8501:8501 \
+  -v "$(pwd)/sample:/app/sample" \
+  trafficsafeanalyzer
+```
+
+容器内默认启用了示例 AI 凭据与 Streamlit Headless 模式，如需调整可在 `docker run` 时追加环境变量（例如 `-e STREAMLIT_SERVER_PORT=8502`）。
+
 ## 依赖项
 
 列于 `requirements.txt`：
@@ -91,6 +113,7 @@ 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
 

services/hotspot.py

@@ -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])
-

ui_sections/hotspot.py

@@ -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)
-