diff --git a/backend/app/agent/smart_agent.py b/backend/app/agent/smart_agent.py
index 53c4ad6..2f57152 100644
--- a/backend/app/agent/smart_agent.py
+++ b/backend/app/agent/smart_agent.py
@@ -1522,6 +1522,8 @@ MA60：{f"{ma['ma60']:.2f}" if ma['ma60'] else '计算中'}
# 构建分析提示
prompt = f"""你是一位专业的美股分析师。请基于以下数据对 {name} ({symbol}) 进行全面分析。
+**重要提示：当前日期是 {current_time}，请在分析中使用这个日期，不要使用其他日期。**
+
【基本信息】
股票代码：{symbol}
公司名称：{name}
@@ -1577,7 +1579,8 @@ MACD：{f"{technical.get('macd'):.4f}" if technical.get('macd') else '计算中'
2. 分析客观理性，基于数据和事实
3. 每个部分独立成段，段落间用空行分隔
4. 控制在500-600字
-5. 最后声明："以上分析仅供参考，不构成投资建议。美股投资有风险，请谨慎决策。"
+5. **不要在报告中添加日期标题，直接开始分析内容**
+6. 最后声明："以上分析仅供参考，不构成投资建议。美股投资有风险，请谨慎决策。"
"""
try:
@@ -1625,6 +1628,409 @@ RSI：{technical.get('rsi', 0):.2f if technical.get('rsi') else '计算中'}
以上数据仅供参考，不构成投资建议。"""
+ async def process_message_stream(
+ self,
+ message: str,
+ session_id: str,
+ user_id: Optional[str] = None
+ ):
+ """
+ 流式处理用户消息
+
+ Args:
+ message: 用户消息
+ session_id: 会话ID
+ user_id: 用户ID
+
+ Yields:
+ 响应文本片段
+ """
+ logger.info(f"流式处理消息: {message[:50]}...")
+
+ # 保存用户消息
+ self.context_manager.add_message(session_id, "user", message)
+
+ # 第一步：使用LLM理解问题意图
+ intent_analysis = await self._analyze_question_intent(message, session_id)
+
+ if not intent_analysis:
+ # 引导用户
+ guide_message = """我是您的金融智能助手，可以帮您：
+
+📊 **股票分析** - 分析个股走势、技术指标、基本面
+📈 **市场观察** - 解读大盘走势、行业热点
+📚 **知识问答** - 解答金融投资相关问题
+
+您可以这样问我：
+• "分析一下贵州茅台"
+• "现在A股市场怎么样"
+• "什么是MACD指标"
+
+请告诉我您想了解什么？"""
+ self.context_manager.add_message(session_id, "assistant", guide_message)
+ yield guide_message
+ return
+
+ # 第二步：根据意图类型处理（流式）
+ question_type = intent_analysis['type']
+
+ full_response = ""
+ if question_type == 'stock_specific':
+ # 针对特定股票的问题 - 流式输出
+ async for chunk in self._handle_stock_question_stream(intent_analysis, message):
+ full_response += chunk
+ yield chunk
+ elif question_type in ['macro_finance', 'knowledge', 'general_chat']:
+ # 其他类型 - 直接流式输出（不需要特殊处理）
+ response = await self._handle_other_question(question_type, intent_analysis, message)
+ full_response = response["message"]
+ # 逐字yield
+ for char in full_response:
+ yield char
+ else:
+ # 未知类型
+ guide_message = f"""我理解您想了解：{intent_analysis.get('description', '相关信息')}
+
+作为金融智能助手，我擅长：
+• 📊 分析具体股票（如"分析比亚迪"）
+• 📈 解读市场走势（如"现在大盘怎么样"）
+• 📚 解答金融知识（如"什么是市盈率"）
+
+能否更具体地告诉我您想了解什么？"""
+ full_response = guide_message
+ yield guide_message
+
+ # 保存助手响应
+ self.context_manager.add_message(session_id, "assistant", full_response)
+
+ async def _handle_other_question(
+ self,
+ question_type: str,
+ intent_analysis: Dict[str, Any],
+ message: str
+ ) -> Dict[str, Any]:
+ """处理非股票分析的其他问题"""
+ if question_type == 'macro_finance':
+ return await self._handle_macro_question(intent_analysis, message)
+ elif question_type == 'knowledge':
+ return await self._handle_knowledge_question(intent_analysis, message)
+ elif question_type == 'general_chat':
+ return await self._handle_general_chat(intent_analysis, message)
+ else:
+ return {"message": "抱歉，我无法理解您的问题。"}
+
+ async def _handle_stock_question_stream(
+ self,
+ intent_analysis: Dict[str, Any],
+ message: str
+ ):
+ """流式处理股票问题"""
+ stock_names = intent_analysis.get('stock_names', [])
+ market = intent_analysis.get('market', 'A股')
+
+ if not stock_names:
+ yield "抱歉，我没有识别到您提到的股票或指数。请提供更明确的股票代码、名称或指数名称。"
+ return
+
+ stock_keyword = stock_names[0]
+ is_us_stock = self._is_us_stock(stock_keyword, market)
+
+ if is_us_stock:
+ # 美股分析 - 流式
+ async for chunk in self._handle_us_stock_stream(stock_keyword, message):
+ yield chunk
+ else:
+ # A股分析 - 流式
+ async for chunk in self._handle_a_stock_stream(stock_keyword, message):
+ yield chunk
+
+ async def _handle_a_stock_stream(self, stock_keyword: str, message: str):
+ """流式处理A股分析"""
+ # 指数映射
+ index_mapping = {
+ "上证指数": "000001.SH", "上证": "000001.SH", "大盘": "000001.SH", "沪指": "000001.SH",
+ "深证成指": "399001.SZ", "深证": "399001.SZ", "深指": "399001.SZ",
+ "创业板指": "399006.SZ", "创业板": "399006.SZ",
+ "科创50": "000688.SH", "沪深300": "000300.SH", "中证500": "000905.SH",
+ "A股": "000001.SH"
+ }
+
+ stock_code = None
+ stock_name = None
+ is_index = False
+
+ for key, code in index_mapping.items():
+ if key in stock_keyword or stock_keyword in key:
+ stock_code = code
+ stock_name = key if key in stock_keyword else stock_keyword
+ is_index = True
+ break
+
+ if not is_index:
+ search_results = tushare_service.search_stock(stock_keyword)
+ if not search_results:
+ yield f"抱歉，未找到股票\"{stock_keyword}\"。请确认股票名称或代码是否正确。"
+ return
+ stock = search_results[0]
+ stock_code = stock['symbol']
+ stock_name = stock['name']
+
+ # 获取数据（非流式）
+ try:
+ quote_result = await skill_manager.execute_skill("market_data", stock_code=stock_code, data_type="quote")
+ technical_result = await skill_manager.execute_skill("technical_analysis", stock_code=stock_code, indicators=["ma", "macd", "rsi", "kdj"])
+ fundamental_result = await skill_manager.execute_skill("fundamental", stock_code=stock_code)
+ advanced_result = await skill_manager.execute_skill("advanced_data", stock_code=stock_code, data_type="all")
+
+ all_data = {
+ "stock_code": stock_code,
+ "stock_name": stock_name,
+ "quote": quote_result.get("data") if quote_result.get("success") else None,
+ "technical": technical_result.get("data") if technical_result.get("success") else None,
+ "fundamental": fundamental_result.get("data") if fundamental_result.get("success") else None,
+ "advanced": advanced_result.get("data") if advanced_result.get("success") else None
+ }
+
+ # 使用LLM流式分析
+ if self.use_llm:
+ async for chunk in self._llm_comprehensive_analysis_stream(all_data, message, is_index):
+ yield chunk
+ else:
+ yield self._rule_based_analysis(all_data)
+
+ except Exception as e:
+ logger.error(f"A股分析失败: {e}")
+ yield f"分析{stock_name}时出错：{str(e)}"
+
+ async def _handle_us_stock_stream(self, keyword: str, message: str):
+ """流式处理美股分析"""
+ symbol = self._get_us_stock_symbol(keyword)
+ logger.info(f"流式处理美股查询: {keyword} -> {symbol}")
+
+ try:
+ result = await skill_manager.execute_skill("us_stock_analysis", symbol=symbol, analysis_type="comprehensive")
+
+ if not result.get("success"):
+ yield f"抱歉，未找到美股 {symbol}。请确认股票代码是否正确。"
+ return
+
+ # 使用LLM流式分析
+ if self.use_llm:
+ async for chunk in self._llm_us_stock_analysis_stream(result["data"], message):
+ yield chunk
+ else:
+ yield self._format_us_stock_data(result["data"])
+
+ except Exception as e:
+ logger.error(f"美股查询失败: {e}")
+ yield f"查询美股 {symbol} 时出错：{str(e)}"
+
+ async def _llm_comprehensive_analysis_stream(self, data: Dict[str, Any], user_message: str, is_index: bool = False):
+ """使用LLM流式进行综合分析"""
+ from datetime import datetime
+ import json
+
+ current_time = datetime.now().strftime("%Y-%m-%d %H:%M")
+
+ # 获取行情数据的交易日期
+ quote_date = "未知"
+ if data.get('quote') and data['quote'].get('trade_date'):
+ quote_date = data['quote']['trade_date']
+
+ # 构建高级数据摘要
+ advanced_summary = ""
+ if data.get('advanced'):
+ advanced_data = data['advanced']
+ advanced_summary = "\n【高级财务数据】（Tushare Pro 5000+积分）\n"
+
+ # 财务指标
+ if advanced_data.get('financial'):
+ financial = advanced_data['financial']
+ if financial.get('indicators'):
+ indicators = financial['indicators'].get('indicators', {})
+ advanced_summary += f"财务指标（截止：{financial['indicators'].get('end_date', '未知')}）：\n"
+ advanced_summary += f" ROE: {indicators.get('roe', 'N/A')}%\n"
+ advanced_summary += f" ROA: {indicators.get('roa', 'N/A')}%\n"
+ advanced_summary += f" 毛利率: {indicators.get('gross_margin', 'N/A')}%\n"
+ advanced_summary += f" 资产负债率: {indicators.get('debt_to_assets', 'N/A')}%\n"
+ advanced_summary += f" 流动比率: {indicators.get('current_ratio', 'N/A')}\n\n"
+
+ # 估值数据
+ if advanced_data.get('valuation'):
+ valuation = advanced_data['valuation']
+ advanced_summary += f"估值指标：\n"
+ advanced_summary += f" PE(市盈率): {valuation.get('pe', 'N/A')}\n"
+ advanced_summary += f" PB(市净率): {valuation.get('pb', 'N/A')}\n"
+ advanced_summary += f" PS(市销率): {valuation.get('ps', 'N/A')}\n"
+ advanced_summary += f" 总市值: {valuation.get('total_mv', 'N/A')}万元\n"
+ advanced_summary += f" 流通市值: {valuation.get('circ_mv', 'N/A')}万元\n"
+ advanced_summary += f" 换手率: {valuation.get('turnover_rate', 'N/A')}%\n\n"
+
+ # 资金流向（最近一天）
+ if advanced_data.get('money_flow') and len(advanced_data['money_flow']) > 0:
+ latest_flow = advanced_data['money_flow'][0]
+ advanced_summary += f"资金流向（{latest_flow.get('trade_date', '最近')}）：\n"
+ advanced_summary += f" 主力净流入: {latest_flow.get('net_mf_amount', 'N/A')}万元\n"
+ advanced_summary += f" 超大单净流入: {latest_flow.get('buy_elg_amount', 0) - latest_flow.get('sell_elg_amount', 0):.2f}万元\n"
+ advanced_summary += f" 大单净流入: {latest_flow.get('buy_lg_amount', 0) - latest_flow.get('sell_lg_amount', 0):.2f}万元\n\n"
+
+ # 融资融券
+ if advanced_data.get('margin') and len(advanced_data['margin']) > 0:
+ latest_margin = advanced_data['margin'][0]
+ advanced_summary += f"融资融券（{latest_margin.get('trade_date', '最近')}）：\n"
+ advanced_summary += f" 融资余额: {latest_margin.get('rzye', 'N/A')}元\n"
+ advanced_summary += f" 融券余额: {latest_margin.get('rqye', 'N/A')}元\n\n"
+
+ else:
+ advanced_summary = "\n【高级财务数据】\n暂无高级数据\n"
+
+ # 构建详细的分析提示
+ prompt = f"""你是一位专业的股票分析师。请对{data['stock_name']}({data['stock_code']})进行全面分析，用简洁专业但易懂的语言回答。
+
+用户问题：{user_message}
+
+【实时行情数据】
+数据来源：Tushare Pro API
+交易日期：{quote_date}
+{json.dumps(data.get('quote'), ensure_ascii=False, indent=2) if data.get('quote') else '数据获取失败'}
+
+【技术指标数据】
+数据来源：Tushare Pro API（基于历史K线数据计算）
+计算截止日期：{quote_date}
+{json.dumps(data.get('technical'), ensure_ascii=False, indent=2) if data.get('technical') else '数据获取失败'}
+
+【基本面数据】
+数据来源：Tushare Pro API
+{json.dumps(data.get('fundamental'), ensure_ascii=False, indent=2) if data.get('fundamental') else '数据获取失败'}
+{advanced_summary}
+
+请按以下结构进行分析：
+
+## 一、基本面分析
+分段说明公司情况，每个要点独立成段。
+
+## 二、技术面分析（数据截止：{quote_date}）
+使用清晰的分段结构，每个技术指标独立成段。
+
+## 三、市场情绪分析
+分段分析市场情绪和资金流向。
+
+## 四、投资建议
+基于分析给出具体的操作建议和点位。
+
+写作要求：
+1. 语言简洁专业但易懂
+2. 分析客观理性，基于数据
+3. 每个分析点独立成段
+4. 控制在600-800字
+5. 最后声明："以上分析仅供参考，不构成投资建议。股市有风险，投资需谨慎。"
+"""
+
+ # 流式调用LLM（同步生成器）
+ stream = llm_service.chat_stream(
+ messages=[{"role": "user", "content": prompt}],
+ temperature=0.7,
+ max_tokens=2000
+ )
+ for chunk in stream:
+ yield chunk
+
+ async def _llm_us_stock_analysis_stream(self, data: Dict[str, Any], user_message: str):
+ """使用LLM流式分析美股"""
+ from datetime import datetime
+ current_time = datetime.now().strftime("%Y-%m-%d %H:%M")
+
+ symbol = data.get("symbol", "")
+ name = data.get("name", "")
+ sector = data.get("sector", "")
+ industry = data.get("industry", "")
+ current_price = data.get("current_price", 0)
+ change = data.get("change", 0)
+ change_pct = data.get("change_percent", 0)
+ volume = data.get("volume", 0)
+ market_cap = data.get("market_cap", 0)
+ pe_ratio = data.get("pe_ratio", 0)
+ pb_ratio = data.get("pb_ratio", 0)
+ dividend_yield = data.get("dividend_yield", 0)
+ week_52_high = data.get("52_week_high", 0)
+ week_52_low = data.get("52_week_low", 0)
+ technical = data.get("technical_indicators", {})
+ description = data.get("description", "")
+
+ market_cap_str = f"${market_cap / 1e9:.2f}B" if market_cap > 1e9 else f"${market_cap / 1e6:.2f}M"
+
+ prompt = f"""你是一位专业的美股分析师。请基于以下数据对 {name} ({symbol}) 进行全面分析。
+
+**重要提示：当前日期是 {current_time}，请在分析中使用这个日期，不要使用其他日期。**
+
+【基本信息】
+股票代码：{symbol}
+公司名称：{name}
+所属行业：{sector} - {industry}
+公司简介：{description[:300] if description else '暂无'}
+
+【实时行情】（数据时间：{current_time}）
+当前价格：${current_price:.2f}
+涨跌额：${change:.2f}
+涨跌幅：{change_pct:.2f}%
+成交量：{volume:,}
+市值：{market_cap_str}
+
+【估值指标】
+市盈率(PE)：{f"{pe_ratio:.2f}" if pe_ratio else '暂无'}
+市净率(PB)：{f"{pb_ratio:.2f}" if pb_ratio else '暂无'}
+股息率：{f"{dividend_yield * 100:.2f}%" if dividend_yield else '暂无'}
+52周最高：${week_52_high:.2f}
+52周最低：${week_52_low:.2f}
+
+【技术指标】
+MA5：{f"${technical.get('ma5'):.2f}" if technical.get('ma5') else '计算中'}
+MA10：{f"${technical.get('ma10'):.2f}" if technical.get('ma10') else '计算中'}
+MA20：{f"${technical.get('ma20'):.2f}" if technical.get('ma20') else '计算中'}
+MA60：{f"${technical.get('ma60'):.2f}" if technical.get('ma60') else '计算中'}
+RSI：{f"{technical.get('rsi'):.2f}" if technical.get('rsi') else '计算中'}
+MACD：{f"{technical.get('macd'):.4f}" if technical.get('macd') else '计算中'}
+
+用户问题：{user_message}
+
+请提供专业的分析报告，包括：
+
+## 📊 行情概览
+简要总结当前股价表现和市场表现（2-3句话）
+
+## 💼 公司基本面
+- 行业地位和竞争优势
+- 估值水平分析（PE、PB是否合理）
+- 盈利能力和成长性
+
+## 📈 技术面分析
+- 当前趋势判断（基于均线系统）
+- 关键支撑位和压力位
+- RSI和MACD信号解读
+
+## 💡 投资建议
+- 短期操作建议（1-2周）
+- 中期投资价值（1-3个月）
+- 风险提示
+
+写作要求：
+1. 语言专业但易懂，避免过度修饰
+2. 分析客观理性，基于数据和事实
+3. 每个部分独立成段，段落间用空行分隔
+4. 控制在500-600字
+5. **不要在报告中添加日期标题，直接开始分析内容**
+6. 最后声明："以上分析仅供参考，不构成投资建议。美股投资有风险，请谨慎决策。"
+"""
+
+ # 流式调用LLM（同步生成器）
+ stream = llm_service.chat_stream(
+ messages=[{"role": "user", "content": prompt}],
+ temperature=0.7,
+ max_tokens=2000
+ )
+ for chunk in stream:
+ yield chunk
+
# 创建全局实例
smart_agent = SmartStockAgent()
diff --git a/backend/app/api/chat.py b/backend/app/api/chat.py
index 3ab1c9e..5a6da66 100644
--- a/backend/app/api/chat.py
+++ b/backend/app/api/chat.py
@@ -2,8 +2,11 @@
对话API路由
"""
from fastapi import APIRouter, HTTPException
+from fastapi.responses import StreamingResponse
from typing import Optional
import uuid
+import json
+import asyncio
from app.models.chat import ChatRequest, ChatResponse
from app.agent.smart_agent import smart_agent # 使用智能Agent
from app.utils.logger import logger
@@ -65,3 +68,60 @@ async def get_history(session_id: str, limit: int = 50):
except Exception as e:
logger.error(f"获取历史失败: {e}")
raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/message/stream")
+async def send_message_stream(request: ChatRequest):
+ """
+ 流式发送消息给Agent
+
+ Args:
+ request: 聊天请求
+
+ Returns:
+ Server-Sent Events 流式响应
+ """
+ try:
+ # 生成或使用现有session_id
+ session_id = request.session_id or str(uuid.uuid4())
+
+ async def event_generator():
+ """生成SSE事件流"""
+ try:
+ # 发送session_id
+ yield f"data: {json.dumps({'type': 'session_id', 'session_id': session_id})}\n\n"
+
+ # 添加小延迟确保数据被发送
+ await asyncio.sleep(0.01)
+
+ # 处理消息并流式返回
+ async for chunk in smart_agent.process_message_stream(
+ message=request.message,
+ session_id=session_id,
+ user_id=request.user_id
+ ):
+ yield f"data: {json.dumps({'type': 'content', 'content': chunk})}\n\n"
+ # 添加小延迟，让浏览器有机会接收数据
+ await asyncio.sleep(0.001)
+
+ # 发送完成信号
+ yield f"data: {json.dumps({'type': 'done'})}\n\n"
+
+ except Exception as e:
+ logger.error(f"流式处理消息失败: {e}")
+ yield f"data: {json.dumps({'type': 'error', 'error': str(e)})}\n\n"
+
+ return StreamingResponse(
+ event_generator(),
+ media_type="text/event-stream",
+ headers={
+ "Cache-Control": "no-cache",
+ "Connection": "keep-alive",
+ "X-Accel-Buffering": "no",
+ "Transfer-Encoding": "chunked"
+ }
+ )
+
+ except Exception as e:
+ logger.error(f"创建流式响应失败: {e}")
+ raise HTTPException(status_code=500, detail=str(e))
diff --git a/backend/app/services/llm_service.py b/backend/app/services/llm_service.py
index 35da9fb..e8c7265 100644
--- a/backend/app/services/llm_service.py
+++ b/backend/app/services/llm_service.py
@@ -43,6 +43,29 @@ class LLMService:
"""使用LLM分析用户意图"""
return self.multi_service.analyze_intent(user_message)
+ def chat_stream(
+ self,
+ messages: List[Dict[str, str]],
+ temperature: float = 0.7,
+ max_tokens: int = 2000
+ ):
+ """
+ 流式调用LLM进行对话
+
+ Args:
+ messages: 消息列表
+ temperature: 温度参数
+ max_tokens: 最大token数
+
+ Yields:
+ LLM响应的文本片段
+ """
+ return self.multi_service.chat_stream(
+ messages=messages,
+ temperature=temperature,
+ max_tokens=max_tokens
+ )
+
# 创建全局实例
llm_service = LLMService()
diff --git a/backend/app/services/multi_llm_service.py b/backend/app/services/multi_llm_service.py
index eba4c58..ce5745c 100644
--- a/backend/app/services/multi_llm_service.py
+++ b/backend/app/services/multi_llm_service.py
@@ -170,6 +170,75 @@ class MultiLLMService:
logger.error(f"详细错误: {traceback.format_exc()}")
return None
+ def chat_stream(
+ self,
+ messages: List[Dict[str, str]],
+ temperature: float = 0.7,
+ max_tokens: int = 2000,
+ model_override: Optional[str] = None
+ ):
+ """
+ 流式调用LLM进行对话
+
+ Args:
+ messages: 消息列表
+ temperature: 温度参数
+ max_tokens: 最大token数
+ model_override: 临时覆盖使用的模型
+
+ Yields:
+ LLM响应的文本片段
+ """
+ provider = model_override or self.current_model
+
+ if not provider or provider not in self.clients:
+ logger.error("没有可用的LLM客户端")
+ return
+
+ try:
+ client = self.clients[provider]
+ model_id = self.model_info[provider]['model_id']
+
+ logger.info(f"流式调用LLM: provider={provider}, model={model_id}, messages={len(messages)}条")
+
+ if provider == 'zhipu':
+ # 智谱AI流式调用
+ response = client.chat.completions.create(
+ model=model_id,
+ messages=messages,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ stream=True
+ )
+ for chunk in response:
+ if chunk.choices and chunk.choices[0].delta.content:
+ yield chunk.choices[0].delta.content
+
+ elif provider == 'deepseek':
+ # DeepSeek流式调用（OpenAI兼容）
+ response = client.chat.completions.create(
+ model=model_id,
+ messages=messages,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ stream=True
+ )
+ for chunk in response:
+ if chunk.choices and chunk.choices[0].delta.content:
+ yield chunk.choices[0].delta.content
+
+ else:
+ logger.error(f"未知的模型提供商: {provider}")
+ return
+
+ logger.info("LLM流式响应完成")
+
+ except Exception as e:
+ logger.error(f"LLM流式调用失败: {type(e).__name__}: {e}")
+ import traceback
+ logger.error(f"详细错误: {traceback.format_exc()}")
+ return
+
def analyze_intent(self, user_message: str) -> Dict[str, Any]:
"""使用LLM分析用户意图"""
if not self.current_model:
diff --git a/frontend/css/style.css b/frontend/css/style.css
index e402cf2..9ff75b9 100644
--- a/frontend/css/style.css
+++ b/frontend/css/style.css
@@ -314,6 +314,42 @@ html, body {
flex-shrink: 0;
}
+/* Streaming Indicator */
+.streaming-indicator {
+ display: flex;
+ gap: 6px;
+ align-items: center;
+ margin-top: 12px;
+ padding: 8px 0;
+}
+
+.streaming-indicator .dot {
+ width: 6px;
+ height: 6px;
+ background: var(--accent);
+ border-radius: 50%;
+ animation: pulse 1.4s infinite ease-in-out;
+}
+
+.streaming-indicator .dot:nth-child(1) {
+ animation-delay: -0.32s;
+}
+
+.streaming-indicator .dot:nth-child(2) {
+ animation-delay: -0.16s;
+}
+
+@keyframes pulse {
+ 0%, 80%, 100% {
+ opacity: 0.3;
+ transform: scale(0.8);
+ }
+ 40% {
+ opacity: 1;
+ transform: scale(1);
+ }
+}
+
/* Share Image Container (hidden, used for rendering) */
.share-image-container {
position: fixed;
diff --git a/frontend/index.html b/frontend/index.html
index 5670bd0..4157710 100644
--- a/frontend/index.html
+++ b/frontend/index.html
@@ -14,7 +14,7 @@
-
+
@@ -85,8 +85,8 @@
-
-
+
+
+
+
+
+
+
+
+
-
-
-