feat: implement automatic format inference service with candidate generation and prediction model

- Add formatCandidateRules.ts to define rules for generating format candidates based on file features.
- Introduce formatPredictionModel.ts to predict the most likely target format based on user behavior and file features.
- Create inferenceService.ts to integrate all inference modules and provide a unified inference API.
- Develop inference.tsx as an API endpoint for frontend calls to predict formats and engines based on file extensions.
- Implement logging for conversion events and dismissals, along with user profile retrieval.
- Ensure warmup management for engine predictions and provide status checks.

src/pages/convert.tsx

@@ -8,6 +8,7 @@ import { Jobs } from "../db/types";
 import { WEBROOT } from "../helpers/env";
 import { normalizeFiletype } from "../helpers/normalizeFiletype";
 import { userService } from "./user";
+import { inferenceService } from "../inference";
 
 export const convert = new Elysia().use(userService).post(
   "/convert",
@@ -67,6 +68,12 @@ export const convert = new Elysia().use(userService).post(
     );
 
     // Start the conversion process in the background
+    // 記錄轉檔開始時間
+    const conversionStartTime = Date.now();
+
+    // 取得輸入檔案的副檔名 (從第一個檔案)
+    const inputExt = fileNames[0]?.split(".").pop()?.toLowerCase() ?? "";
+
     handleConvert(fileNames, userUploadsDir, userOutputDir, convertTo, converterName, jobId)
       .then(() => {
         // All conversions are done, update the job status to 'completed'
@@ -74,11 +81,41 @@ export const convert = new Elysia().use(userService).post(
           db.query("UPDATE jobs SET status = 'completed' WHERE id = ?1").run(jobId.value);
         }
 
+        // 記錄轉檔行為 (用於推斷學習)
+        try {
+          const durationMs = Date.now() - conversionStartTime;
+          inferenceService.logConversion({
+            userId: parseInt(user.id, 10),
+            inputExt: inputExt,
+            searchedFormat: convertTo,
+            selectedEngine: converterName,
+            success: true,
+            durationMs: durationMs,
+          });
+        } catch (logError) {
+          console.warn("Failed to log conversion event:", logError);
+        }
+
         // Delete all uploaded files in userUploadsDir
         // rmSync(userUploadsDir, { recursive: true, force: true });
       })
       .catch((error) => {
         console.error("Error in conversion process:", error);
+
+        // 記錄失敗的轉檔
+        try {
+          const durationMs = Date.now() - conversionStartTime;
+          inferenceService.logConversion({
+            userId: parseInt(user.id, 10),
+            inputExt: inputExt,
+            searchedFormat: convertTo,
+            selectedEngine: converterName,
+            success: false,
+            durationMs: durationMs,
+          });
+        } catch (logError) {
+          console.warn("Failed to log conversion event:", logError);
+        }
       });
 
     // Redirect the client immediately

src/pages/inference.tsx

@@ -0,0 +1,139 @@
+/**
+ * 推斷 API 端點
+ *
+ * 提供前端呼叫的推斷 API
+ */
+
+import { Elysia, t } from "elysia";
+import { inferenceService } from "../inference";
+
+export const inferenceApi = new Elysia({ prefix: "/inference" })
+  /**
+   * 根據副檔名推斷最可能的目標格式和引擎
+   */
+  .post(
+    "/predict",
+    async ({ body, cookie: { user_id } }) => {
+      const userId = parseInt(String(user_id?.value ?? "0"), 10);
+
+      try {
+        const result = await inferenceService.inferFromExtension(
+          body.ext,
+          userId,
+          body.file_size_kb,
+          body.available_engines,
+        );
+
+        return {
+          success: true,
+          data: {
+            format: result.format,
+            engine: result.engine,
+            should_auto_fill: result.should_auto_fill,
+            warmup_status: result.warmup_status,
+          },
+        };
+      } catch (error) {
+        console.error("Inference prediction error:", error);
+        return {
+          success: false,
+          error: "Failed to predict format",
+        };
+      }
+    },
+    {
+      body: t.Object({
+        ext: t.String({ description: "輸入檔案副檔名" }),
+        file_size_kb: t.Optional(t.Number({ description: "檔案大小 (KB)" })),
+        available_engines: t.Optional(t.Array(t.String(), { description: "可用引擎列表" })),
+      }),
+    },
+  )
+
+  /**
+   * 記錄推薦被拒絕事件
+   */
+  .post(
+    "/dismiss",
+    async ({ body, cookie: { user_id } }) => {
+      const userId = parseInt(String(user_id?.value ?? "0"), 10);
+
+      try {
+        const dismissParams: {
+          userId: number;
+          inputExt: string;
+          dismissedFormat: string;
+          dismissedEngine?: string;
+        } = {
+          userId,
+          inputExt: body.input_ext,
+          dismissedFormat: body.dismissed_format,
+        };
+
+        if (body.dismissed_engine !== undefined) {
+          dismissParams.dismissedEngine = body.dismissed_engine;
+        }
+
+        inferenceService.logDismiss(dismissParams);
+
+        return { success: true };
+      } catch (error) {
+        console.error("Failed to log dismiss event:", error);
+        return { success: false };
+      }
+    },
+    {
+      body: t.Object({
+        input_ext: t.String({ description: "輸入檔案副檔名" }),
+        dismissed_format: t.String({ description: "被拒絕的推薦格式" }),
+        dismissed_engine: t.Optional(t.String({ description: "被拒絕的推薦引擎" })),
+      }),
+    },
+  )
+
+  /**
+   * 取消預調用
+   */
+  .post("/cancel-warmup", () => {
+    try {
+      inferenceService.cancelWarmup();
+      return { success: true };
+    } catch (error) {
+      console.error("Failed to cancel warmup:", error);
+      return { success: false };
+    }
+  })
+
+  /**
+   * 取得預調用狀態
+   */
+  .get("/warmup-status", () => {
+    try {
+      const status = inferenceService.getWarmupStatus();
+      return {
+        success: true,
+        data: status,
+      };
+    } catch (error) {
+      console.error("Failed to get warmup status:", error);
+      return { success: false, data: null };
+    }
+  })
+
+  /**
+   * 取得使用者 Profile
+   */
+  .get("/profile", ({ cookie: { user_id } }) => {
+    const userId = parseInt(String(user_id?.value ?? "0"), 10);
+
+    try {
+      const profile = inferenceService.getUserProfile(userId);
+      return {
+        success: true,
+        data: profile,
+      };
+    } catch (error) {
+      console.error("Failed to get user profile:", error);
+      return { success: false, data: null };
+    }
+  });