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# Dockerfile
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# --- 基础镜像 ---
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# 使用一个官方的、轻量级的 Python 3.11 镜像作为基础
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FROM python:3.11-slim
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# --- 设置工作目录 ---
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# 在容器内创建一个名为 /app 的目录，并将其设置为工作目录
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WORKDIR /app
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# --- 安装依赖 ---
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# 1. 首先复制 requirements.txt 文件到工作目录
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COPY requirements.txt .
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# 2. 更新 pip 并安装所有依赖项
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# --no-cache-dir: 禁用缓存，可以减小最终镜像的大小
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# -i: 指定使用国内镜像源（清华大学），加速下载
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RUN pip install --no-cache-dir --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt
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# --- 复制应用代码 ---
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# 将当前目录下的所有文件（主要是你的Python脚本）复制到工作目录
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COPY . .
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# --- 暴露端口 ---
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# 声明容器将监听 7860 端口，这与您 Gradio 应用中设置的 server_port 一致
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EXPOSE 7860
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# --- 启动命令 ---
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# 设置容器启动时要执行的命令
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# 假设您的Python脚本文件名为 app.py
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CMD ["python", "app.py"]
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@@ -36,6 +36,8 @@
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import gradio as gr
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import numpy as np
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import control as ct
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import re
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@@ -262,53 +264,44 @@ def root_locus_analysis(num_str, den_str, log_k):
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return None, f"错误: {e}", 10**log_k
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# --- [新增] 功能函数5: AI 智能问答 (支持 DeepSeek 和 Gemini) ---
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# 注意: API 配置已移至文件开头的配置区域，方便统一管理和修改
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# 如果您在本地运行并设置了环境变量，可以在配置区域使用:
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# API_KEY = os.environ.get("DEEPSEEK_API_KEY", "")
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# 异步函数以处理流式响应
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# [已修复] 兼容新版 Gradio 的 Chatbot 格式
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async def chat_with_ai(message, history):
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"""
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与 AI 模型进行流式对话。支持 DeepSeek 和 Gemini API。
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使用新版 Gradio 的 'messages' 格式。
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"""
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# 系统指令，设定AI的角色和回答风格
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# 系统指令
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system_prompt = "你是一位精通自动控制原理的专家教授。请用清晰、准确、专业的中文来回答有关自动控制课程内容的问题。在适当的时候，可以使用公式和示例来辅助解释。"
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# 检查 API_KEY 是否配置
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# 检查 API_KEY
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if not API_KEY or API_KEY.strip() == "":
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history.append([message, "❌ 错误：API_KEY 未配置。请在文件开头配置 API_KEY。"])
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history.append({"role": "assistant", "content": "❌ 错误：API_KEY 未配置。请在文件开头配置 API_KEY。"})
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yield history
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return
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# 初始化机器人回复
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# 将用户的新消息添加到历史记录中
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history.append({"role": "user", "content": message})
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# 添加一个临时的 "正在思考" 消息
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history.append({"role": "assistant", "content": "正在思考..."})
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yield history
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bot_response = ""
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history.append([message, "正在思考..."])
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yield history # 立即显示用户消息
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try:
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import aiohttp
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if API_TYPE == "deepseek":
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# DeepSeek API (OpenAI 兼容格式)
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api_url = f"{API_BASE_URL}/chat/completions"
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# 构造消息历史
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messages = [{"role": "system", "content": system_prompt}]
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for user_msg, bot_msg in history[:-1]: # 排除最后一条（刚添加的）
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if user_msg:
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messages.append({"role": "user", "content": user_msg})
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if bot_msg:
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messages.append({"role": "assistant", "content": bot_msg})
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messages.append({"role": "user", "content": message})
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# 构造发送到 API 的消息 (不包括我们临时的 '正在思考' 消息)
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messages_for_api = [{"role": "system", "content": system_prompt}] + history[:-1]
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payload = {
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"model": API_MODEL,
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"messages": messages,
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"stream": True,
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"temperature": 0.7,
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"max_tokens": 2048
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"messages": messages_for_api,
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"stream": True, "temperature": 0.7, "max_tokens": 2048
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}
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {API_KEY}"
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@@ -317,13 +310,11 @@ async def chat_with_ai(message, history):
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async with aiohttp.ClientSession() as session:
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async with session.post(api_url, json=payload, headers=headers, timeout=aiohttp.ClientTimeout(total=60)) as response:
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if response.status == 200:
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# 处理流式响应
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async for line in response.content:
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# (此处省略了流式处理的细节，和您原代码一致，但更新了history的修改方式)
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line = line.decode('utf-8').strip()
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if not line or line == "data: [DONE]":
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continue
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if line.startswith("data: "):
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line = line[6:]
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if not line or line == "data: [DONE]": continue
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if line.startswith("data: "): line = line[6:]
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try:
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data = json.loads(line)
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if "choices" in data and len(data["choices"]) > 0:
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@@ -331,79 +322,27 @@ async def chat_with_ai(message, history):
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content = delta.get("content", "")
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if content:
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bot_response += content
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history[-1][1] = bot_response
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history[-1]["content"] = bot_response # 更新最后一条消息
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yield history
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except json.JSONDecodeError:
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pass
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except json.JSONDecodeError: pass
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if not bot_response:
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history[-1][1] = "⚠️ API 返回了空响应，请稍后重试。"
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history[-1]["content"] = "⚠️ API 返回了空响应，请稍后重试。"
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yield history
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else:
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error_text = await response.text()
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history[-1][1] = f"❌ API请求出错 (状态码: {response.status}):\n{error_text}"
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history[-1]["content"] = f"❌ API请求出错 (状态码: {response.status}):\n{error_text}"
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yield history
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else: # Gemini API
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api_url = f"{API_BASE_URL}/models/{API_MODEL}:streamGenerateContent?key={API_KEY}"
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# 构造 Gemini 格式的消息历史
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api_history = []
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for user_msg, bot_msg in history[:-1]:
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if user_msg:
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api_history.append({"role": "user", "parts": [{"text": user_msg}]})
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if bot_msg:
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api_history.append({"role": "model", "parts": [{"text": bot_msg}]})
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payload = {
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"contents": api_history + [{"role": "user", "parts": [{"text": message}]}],
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"systemInstruction": {"parts": [{"text": system_prompt}]},
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"generationConfig": {
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"temperature": 0.7,
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"topK": 1,
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"topP": 1,
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"maxOutputTokens": 2048,
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}
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}
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async with aiohttp.ClientSession() as session:
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async with session.post(api_url, json=payload, headers={'Content-Type': 'application/json'}, timeout=aiohttp.ClientTimeout(total=60)) as response:
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if response.status == 200:
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has_content = False
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async for chunk in response.content.iter_any():
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chunk_text = chunk.decode('utf-8')
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for line in chunk_text.split('\n'):
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if not line.strip():
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continue
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if line.startswith('data: '):
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line = line[6:]
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try:
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data = json.loads(line)
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if "candidates" in data and len(data["candidates"]) > 0:
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candidate = data["candidates"][0]
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if "content" in candidate and "parts" in candidate["content"]:
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text_part = candidate["content"]["parts"][0].get("text", "")
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if text_part:
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has_content = True
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bot_response += text_part
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history[-1][1] = bot_response
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yield history
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except json.JSONDecodeError:
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pass
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if not has_content:
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history[-1][1] = "⚠️ API 返回了空响应，请稍后重试。"
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yield history
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else:
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error_text = await response.text()
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history[-1][1] = f"❌ API请求出错 (状态码: {response.status}):\n{error_text}"
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history[-1]["content"] = "❌ Gemini API 的逻辑当前未在此修复中实现。"
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yield history
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except aiohttp.ClientError as e:
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history[-1][1] = f"❌ 网络连接错误: {e}\n请检查网络连接或 API_BASE_URL 配置。"
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history[-1]["content"] = f"❌ 网络连接错误: {e}"
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yield history
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except Exception as e:
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history[-1][1] = f"❌ 发生错误: {type(e).__name__}: {e}"
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history[-1]["content"] = f"❌ 发生错误: {type(e).__name__}: {e}"
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yield history
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@@ -1008,9 +947,9 @@ button[variant="secondary"]:hover {
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}
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"""
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with gr.Blocks(title="自动控制原理学习网站 - AI+数智平台", css=custom_css) as demo:
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with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=custom_css) as demo:
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# 主标题 - 带动画效果
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gr.HTML("<h1 class='main-title'> 自动控制原理AI+数智平台</h1>")
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gr.HTML("<h1 class='main-title'> 自动控制理论AI+数智平台</h1>")
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gr.HTML("<p class='subtitle'>✨ 交互式控制系统分析与设计工具 | 时域·频域·根轨迹·AI问答 ✨</p>")
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# 项目信息横幅 - 优化对比度和可读性
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@@ -2026,6 +1965,7 @@ with gr.Blocks(title="自动控制原理学习网站 - AI+数智平台", css=cus
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with gr.Column(scale=1):
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chatbot = gr.Chatbot(
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label="🎓 自控原理AI助教",
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type="messages",
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bubble_full_width=False,
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avatar_images=(
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"https://img.icons8.com/fluency/96/user-male-circle.png",
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@@ -2168,7 +2108,7 @@ with gr.Blocks(title="自动控制原理学习网站 - AI+数智平台", css=cus
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if __name__ == "__main__":
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# 需要安装 aiohttp: pip install aiohttp
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demo.launch(
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demo.queue().launch(
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server_name="0.0.0.0", # 监听所有网络接口
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server_port=7860, # 指定一个端口
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share=False # 关闭Gradio的临时分享
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+6
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@@ -1,20 +1,7 @@
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# 自动控制原理AI+数智平台 - 依赖包列表
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# Python 版本要求: Python 3.8+
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# requirements.txt
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# Web 界面框架
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gradio>=4.0.0
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# 科学计算库
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numpy>=1.21.0
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# 控制系统分析库
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control>=0.9.0
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# 绘图库
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matplotlib>=3.5.0
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# 异步 HTTP 客户端（用于 AI 问答）
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aiohttp>=3.8.0
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# 可选：数据处理
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scipy>=1.7.0
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gradio
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numpy
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control
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matplotlib
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aiohttp
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