added retrain and train model function to update the predictor model

src/main.ts

@@ -16,7 +16,7 @@ import {
 import { FileScoreMap } from "./types/FileScoreMap";
 import { isValidFilePath } from "./fetch/fetchFiles";
 import { connectMindsDB } from "./db/mindsdbConnection";
-import { retrainModel } from "./services/predictionService";
+import { trainPredictorModel } from "./services/predictionService";
 import {
   errorFallbackCommentForPRClosedEvent,
   errorFallbackCommentForPROpenEvent,
@@ -30,7 +30,7 @@ export async function main(app: Probot) {
   handleAppInstallationCreatedEvents(app);
   handlePullRequestOpenEvents(app);
   handlePullRequestClosedEvents(app);
-  retrainModel(app);
+  trainPredictorModel(app);
   debug(app);
 }
 

src/services/predictionService.ts

@@ -7,7 +7,6 @@ import MindsDB, {
 } from "mindsdb-js-sdk";
 
 const {
-  MINDSDB_HOST,
   MONGODB_USER,
   MONGODB_PASSWORD,
   MONGODB_PORT,
@@ -18,22 +17,45 @@ const {
 const databaseName = "mongo_datasource";
 const projectName = "mindsdb";
 const predictorName = "riskscore_predictor";
+const targetField = "riskScore";
 const aggregationQuery = `test.trainingfiles.find({})`;
 
-export async function retrainModel(app: Probot) {
-  trainModel(app);
-}
+const regressionTrainingOptions: TrainingOptions = {
+  select: aggregationQuery,
+  integration: databaseName,
+  orderBy: "createdAt",
+  groupBy: "installationId",
+  window: 100, // How many rows in the past to use when making a future prediction.
+  horizon: 10, // How many rows in the future to forecast.
+};
 
-async function connectToMindsDB() {
-  await MindsDB.connect({
-    user: "",
-    password: "",
-    host: MINDSDB_HOST,
-  });
+export async function retrainPredictorModel(app: Probot) {
+  await MindsDB.Models.retrainModel(
+    predictorName,
+    targetField,
+    projectName,
+    regressionTrainingOptions
+  )
+    .then(() => {
+      app.log.info(`[${predictorName}] model is retrained successfully`);
+    })
+    .catch((error: any) => {
+      app.log.error(
+        `Error occurred while retraining the model [${predictorName}]`
+      );
+      app.log.error(error);
+    });
 }
 
-async function trainModel(app: Probot) {
+export async function trainPredictorModel(app: Probot) {
   try {
+    const models: Model[] = await MindsDB.Models.getAllModels(projectName);
+    const modelNames = models.map((model: Model) => model.name);
+
+    if (modelNames.includes(predictorName)) {
+      app.log.info(`[${predictorName}] model is already present in mindsdb`);
+      return;
+    }
     app.log.info(`Started training the model: [${predictorName}]`);
     const dbList: Database[] = await MindsDB.Databases.getAllDatabases();
     const dbNames: string[] = dbList.map((db: Database) => db.name);
@@ -43,18 +65,9 @@ async function trainModel(app: Probot) {
       app.log.info(`Created database: ${db?.name} in mindsdb successfully`);
     }
 
-    const regressionTrainingOptions: TrainingOptions = {
-      select: aggregationQuery,
-      integration: databaseName,
-      orderBy: "createdAt",
-      groupBy: "installationId",
-      window: 100, // How many rows in the past to use when making a future prediction.
-      horizon: 10, // How many rows in the future to forecast.
-    };
-
     let predictionModel: Model | undefined = await MindsDB.Models.trainModel(
       predictorName,
-      "riskScore",
+      targetField,
       projectName,
       regressionTrainingOptions
     );
@@ -65,8 +78,8 @@ async function trainModel(app: Probot) {
         projectName
       );
 
-      if (predictionModel?.active) {
-        app.log.info("Prediction model is active");
+      if (predictionModel?.status.match("error")) {
+        app.log.info("Prediction model training is complete");
         clearInterval(intervalId);
       }
     }, 2000);

src/services/pullRequestService.ts

@@ -8,7 +8,7 @@ import { getAllCommits } from "../fetch/fetchCommits";
 import { calculateRiskScore } from "./riskScoreService";
 import { FileType } from "../types/FileType";
 import { FileStatus } from "../constants/GithubContants";
-import { retrainModel } from "./predictionService";
+import { retrainPredictorModel } from "./predictionService";
 import { TrainingFileType } from "../types/TrainingFileType";
 import { TrainingFile } from "../db/models/TrainingFile";
 
@@ -142,7 +142,7 @@ export async function updateFilesInDb(
       `Updated the files coming from pull request with ref: [${owner}/${repoName}/pulls/${pullNumber}] successfully for installation id: [${installationId}]`
     );
 
-    retrainModel(app);
+    retrainPredictorModel(app);
 
     return true;
   } catch (error: any) {

src/services/repositoryService.ts

@@ -10,6 +10,7 @@ import { fetchDetailsWithInstallationId } from "../fetch/fetch";
 import { FileType } from "../types/FileType";
 import { TrainingFile } from "../db/models/TrainingFile";
 import { TrainingFileType } from "../types/TrainingFileType";
+import { retrainPredictorModel } from "./predictionService";
 
 export async function processRepositories(
   app: Probot,
@@ -38,9 +39,9 @@ async function processRepositoryBatch(
   owner: string,
   repositories: any[]
 ): Promise<void> {
-  await Promise.all(
-    repositories.map(async (repo) => {
-      try {
+  try {
+    await Promise.all(
+      repositories.map(async (repo) => {
         app.log.info(`Started processing repository: [${repo.name}]`);
 
         const defaultBranch = await getDefaultBranch(
@@ -119,11 +120,14 @@ async function processRepositoryBatch(
         app.log.info(
           `Completed the processing of [${owner}/${repo.name}] repository successfully for installation id: [${installationId}]`
         );
-      } catch (error: any) {
-        app.log.error(error);
-      }
-    })
-  );
+
+        retrainPredictorModel(app);
+      })
+    );
+  } catch (error: any) {
+    app.log.error(`Error while processing the repository batch`);
+    app.log.error(error);
+  }
 }
 
 async function getDefaultBranch(