fix(flow-monitor): 修复 Kiro 请求 Token 显示为 0 的问题 (#44)

- 在 CWParsedResponse 中添加 estimate_tokens() 方法
- 基于 context_usage_percentage 估算 input_tokens (100% = 200k tokens)
- 基于响应内容长度估算 output_tokens (约 4 字符 = 1 token)
- 修复 api.rs 中所有调用 build_llm_response 时传入 None 的位置
- 版本更新至 0.17.12

package.json

@@ -1,7 +1,7 @@
 {
   "name": "proxycast",
   "private": true,
-  "version": "0.17.11",
+  "version": "0.17.12",
   "type": "module",
   "repository": {
     "type": "git",

src-tauri/Cargo.lock

@@ -3377,7 +3377,7 @@ dependencies = [
 
 [[package]]
 name = "proxycast"
-version = "0.17.11"
+version = "0.17.12"
 dependencies = [
  "anyhow",
  "async-stream",

src-tauri/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "proxycast"
-version = "0.17.11"
+version = "0.17.12"
 description = "AI API Proxy Desktop App"
 authors = ["you"]
 edition = "2021"

src-tauri/src/server/handlers/api.rs

@@ -1232,8 +1232,13 @@ pub async fn chat_completions(
                                             // 完成 Flow 捕获并检查响应拦截（重试成功）
                                             // **Validates: Requirements 2.1, 2.5**
                                             if let Some(fid) = &flow_id {
-                                                let llm_response =
-                                                    build_llm_response(200, &parsed.content, None);
+                                                let (est_input, est_output) =
+                                                    parsed.estimate_tokens();
+                                                let llm_response = build_llm_response(
+                                                    200,
+                                                    &parsed.content,
+                                                    Some((est_input, est_output)),
+                                                );
 
                                                 // 检查是否需要拦截响应
                                                 if let Some(modified_response) =
@@ -1896,7 +1901,12 @@ pub async fn anthropic_messages(
                             // 完成 Flow 捕获并检查响应拦截（流式）
                             // **Validates: Requirements 2.1, 2.5**
                             if let Some(fid) = &flow_id {
-                                let llm_response = build_llm_response(200, &parsed.content, None);
+                                let (est_input, est_output) = parsed.estimate_tokens();
+                                let llm_response = build_llm_response(
+                                    200,
+                                    &parsed.content,
+                                    Some((est_input, est_output)),
+                                );
 
                                 // 检查是否需要拦截响应
                                 if let Some(modified_response) = check_response_intercept(
@@ -1955,7 +1965,12 @@ pub async fn anthropic_messages(
                         // 完成 Flow 捕获并检查响应拦截（非流式）
                         // **Validates: Requirements 2.1, 2.5**
                         if let Some(fid) = &flow_id {
-                            let llm_response = build_llm_response(200, &parsed.content, None);
+                            let (est_input, est_output) = parsed.estimate_tokens();
+                            let llm_response = build_llm_response(
+                                200,
+                                &parsed.content,
+                                Some((est_input, est_output)),
+                            );
 
                             // 检查是否需要拦截响应
                             if let Some(modified_response) = check_response_intercept(
@@ -2080,8 +2095,13 @@ pub async fn anthropic_messages(
                                             // 完成 Flow 捕获并检查响应拦截（重试成功）
                                             // **Validates: Requirements 2.1, 2.5**
                                             if let Some(fid) = &flow_id {
-                                                let llm_response =
-                                                    build_llm_response(200, &parsed.content, None);
+                                                let (est_input, est_output) =
+                                                    parsed.estimate_tokens();
+                                                let llm_response = build_llm_response(
+                                                    200,
+                                                    &parsed.content,
+                                                    Some((est_input, est_output)),
+                                                );
 
                                                 // 检查是否需要拦截响应
                                                 if let Some(modified_response) =

src-tauri/src/server_utils.rs

@@ -21,6 +21,30 @@ pub struct CWParsedResponse {
     pub context_usage_percentage: f64,
 }
 
+impl CWParsedResponse {
+    /// 估算 Token 使用量
+    ///
+    /// 基于响应内容长度和上下文使用百分比估算 Token 数量：
+    /// - output_tokens: 基于响应内容长度（约 4 字符 = 1 token）
+    /// - input_tokens: 基于 context_usage_percentage（假设 100% = 200k tokens）
+    ///
+    /// # 返回
+    /// (input_tokens, output_tokens) 元组
+    pub fn estimate_tokens(&self) -> (u32, u32) {
+        // 估算 output tokens: 基于响应内容长度 (约 4 字符 = 1 token)
+        let mut output_tokens: u32 = (self.content.len() / 4) as u32;
+        for tc in &self.tool_calls {
+            output_tokens += (tc.function.arguments.len() / 4) as u32;
+        }
+
+        // 从 context_usage_percentage 估算 input tokens
+        // 假设 100% = 200k tokens (Claude 的上下文窗口)
+        let input_tokens = ((self.context_usage_percentage / 100.0) * 200000.0) as u32;
+
+        (input_tokens, output_tokens)
+    }
+}
+
 /// 安全截断字符串到指定字符数，避免 UTF-8 边界问题
 pub fn safe_truncate(s: &str, max_chars: usize) -> String {
     let chars: Vec<char> = s.chars().collect();

src-tauri/tauri.conf.json

@@ -1,7 +1,7 @@
 {
   "$schema": "https://schema.tauri.app/config/2",
   "productName": "ProxyCast",
-"version": "0.17.11",
+"version": "0.17.12",
   "identifier": "com.proxycast.app",
   "build": {
     "beforeDevCommand": "npm run dev",