gallery 2 api

.dockerignore

@@ -1,3 +1,6 @@
 node_modules
 .git
 .gitignore
+**/docker-compose*
+**/compose.y*ml
+**/Dockerfile*

.gitignore

@@ -3,7 +3,7 @@ node_modules
 
 # yarn error logs
 yarn-error.log
-
+yarn.lock
 # Environment varibales
 .env*
 !.env*.example

Dockerfile

@@ -1,8 +1,12 @@
-FROM node:alpine
+ARG NODE_VERSION=18
 
-RUN mkdir -p /usr/src/node-app && chown -R node:node /usr/src/node-app
+FROM node:${NODE_VERSION}-alpine
 
-WORKDIR /usr/src/node-app
+ENV NODE_ENV production
+
+RUN mkdir -p /usr/src/iimtt_api && chown -R node:node /usr/src/iimtt_api
+
+WORKDIR /usr/src/iimtt_api
 
 COPY package.json yarn.lock ./
 
@@ -12,4 +16,6 @@ RUN yarn install --pure-lockfile
 
 COPY --chown=node:node . .
 
-EXPOSE 3000
+EXPOSE 5174
+
+CMD yarn start

docker-compose.yml

@@ -1,32 +1,31 @@
-version: '3'
-
 services:
-  node-app:
+  iimtt_api:
     build: .
-    image: node-app
-    environment:
-      - MONGODB_URL=mongodb://mongodb:27017/node-boilerplate
+    image: iimtt_api
+    # environment:
+    #   - MONGODB_URL=mongodb://mongodb:27017/node-boilerplate
     ports:
-      - '3000:3000'
-    depends_on:
-      - mongodb
+      - '5174:5174'
+    # depends_on:
+    #   - mongodb
     volumes:
-      - .:/usr/src/node-app
-    networks:
-      - node-network
+      - .:/usr/src/iimtt_api
+    # networks:
+    #   - host
+#       - node-network
 
-  mongodb:
-    image: mongo:4.2.1-bionic
-    ports:
-      - '27017:27017'
-    volumes:
-      - dbdata:/data/db
-    networks:
-      - node-network
+#   mongodb:
+#     image: mongo:4.2.1-bionic
+#     ports:
+#       - '27017:27017'
+#     volumes:
+#       - dbdata:/data/db
+#     networks:
+#       - node-network
 
-volumes:
-  dbdata:
+# volumes:
+#   dbdata:
 
-networks:
-  node-network:
-    driver: bridge
+# networks:
+#   node-network:
+#     driver: bridge

package.json

@@ -1,6 +1,6 @@
 {
   "name": "iimttapi",
-  "version": "0.0.1",
+  "version": "1.0.0",
   "description": "Create a Node.js app for building production-ready RESTful APIs using Express, by running one command",
   "bin": "bin/createNodejsApp.js",
   "main": "src/index.js",
@@ -9,7 +9,8 @@
     "node": ">=12.0.0"
   },
   "scripts": {
-    "start": "pm2 start ecosystem.config.json --no-daemon",
+    "start": "NODE_ENV=production node src/index.js",
+    "start:pm2": "pm2 start ecosystem.config.json --no-daemon",
     "dev": "cross-env NODE_ENV=development nodemon src/index.js",
     "test": "jest -i --colors --verbose --detectOpenHandles",
     "test:watch": "jest -i --watchAll",
@@ -48,6 +49,7 @@
   ],
   "dependencies": {
     "aws-sdk": "^2.1669.0",
+    "axios": "^1.7.7",
     "bcryptjs": "^2.4.3",
     "body-parser": "^1.20.2",
     "compression": "^1.7.4",
@@ -57,15 +59,20 @@
     "express": "^4.17.1",
     "express-mongo-sanitize": "^2.0.0",
     "express-rate-limit": "^5.0.0",
+    "form-data": "^4.0.1",
+    "formdata-node": "^6.0.3",
     "helmet": "^4.1.0",
     "http-status": "^1.4.0",
+    "install": "^0.13.0",
     "joi": "^17.3.0",
     "jsonwebtoken": "^8.5.1",
     "moment": "^2.24.0",
-    "mongoose": "^5.7.7",
+    "mongoose": "^8.7.1",
     "morgan": "^1.9.1",
     "mysql2": "^3.11.0",
+    "node-fetch": "^3.3.2",
     "nodemailer": "^6.3.1",
+    "npm": "^11.0.0",
     "passport": "^0.4.0",
     "passport-jwt": "^4.0.0",
     "pm2": "^5.1.0",

src/config/config.js

@@ -38,7 +38,7 @@ module.exports = {
   mongoose: {
     url: envVars.MONGODB_URL + (envVars.NODE_ENV === 'test' ? '-test' : ''),
     options: {
-      useCreateIndex: true,
+      // useCreateIndex: true,
       useNewUrlParser: true,
       useUnifiedTopology: true,
     },

src/models/gameModel.js

@@ -0,0 +1,13 @@
+const mongoose = require("mongoose");
+
+const gameSchema = new mongoose.Schema({
+  gameName: { type: String, required: true },
+  userId: { type: String, required: true },
+  gameID: { type: String, required: true },
+  gameTime: { type: String, required: false },
+  score: { type: String},
+  gameStar: { type: Number},
+  screenshotUrl: { type: String }, // Store the S3 URL of the screenshot
+});
+
+module.exports = mongoose.model("Game", gameSchema);

src/models/getGalleyImage.js

@@ -0,0 +1,15 @@
+const mongoose = require('mongoose');
+
+// Define the GalleryImage schema
+const galleryImageSchema = new mongoose.Schema({
+    userId: { type: mongoose.Schema.Types.ObjectId, ref: 'User', required: true },  // Assuming you have a User model
+    gameName: { type: String, required: true },
+    gameID: { type: String, required: true },
+    screenshotUrl: { type: String, required: true },
+    message: { type: String, default: 'Image received' },
+}, { timestamps: true });
+
+// Check if the model already exists in mongoose.models
+const GalleryImage = mongoose.models.GalleryImage || mongoose.model('GalleryImage', galleryImageSchema);
+
+module.exports = GalleryImage;

src/models/imageGallery.js

@@ -0,0 +1,10 @@
+const mongoose = require("mongoose");
+
+const galleryImageScema = new mongoose.Schema({
+  gameName: { type: String, required: true },
+  userId: { type: String, required: true },
+  gameID: { type: String, required: true },
+  screenshotUrl: { type: String }, 
+});
+
+module.exports = mongoose.model("GalleryImage", galleryImageScema);

src/module/base64ToImageFile.js

@@ -0,0 +1,21 @@
+const fs = require('fs');
+const path = require('path');
+
+function base64ToImageFile(base64, fileName) {
+    const matches = base64.match(/^data:(.+);base64,(.+)$/);
+    if (!matches) {
+        throw new Error("Invalid Base64 string");
+    }
+
+    const mimeType = matches[1];  // e.g., image/png
+    const base64Data = matches[2];  // Actual base64 string
+
+    const buffer = Buffer.from(base64Data, 'base64');
+    const filePath = path.join(__dirname, fileName);
+
+    fs.writeFileSync(filePath, buffer);
+
+    return filePath;
+}
+
+module.exports = { base64ToImageFile };

src/routes/api/aiEvaluateImageToScore.js

@@ -0,0 +1,103 @@
+const AWS = require('aws-sdk');
+const axios = require('axios');
+const mongoose = require('mongoose');
+const Game = require('../../models/gameModel'); // Ensure this path matches your project structure
+
+const aiEvaluateImageToScore = async (req, res) => {
+  try {
+    // Destructure data from req.body
+    const { userId, gameName, gameID, gameTime, gameStar, screenShot } = req.body;
+
+    if (!screenShot) {
+      return res.status(400).json({ error: 'Screenshot is required' });
+    }
+
+    // Decode the base64 image
+    const buffer = Buffer.from(screenShot, 'base64');
+    const fileName = `${Date.now()}-${gameID}.png`; // Unique filename
+    const bucketName = process.env.S3_BUCKET_NAME;
+
+    // Initialize S3
+    AWS.config.update({
+      region: 'ap-south-1',
+      accessKeyId: process.env.AWS_ACCESS_KEY_ID,
+      secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,
+    });
+    const s3 = new AWS.S3();
+
+    // Upload the image to S3
+    const s3Params = {
+      Bucket: bucketName,
+      Key: `screenshots/${fileName}`,
+      Body: buffer,
+      ContentType: 'image/png',
+      ACL: 'public-read', // Make the file publicly accessible
+    };
+
+    const s3UploadResponse = await s3.upload(s3Params).promise();
+    const screenshotUrl = s3UploadResponse.Location;
+
+    // Prepare the OpenAI API request
+    const openAiPayload = {
+      model: 'gpt-4o-mini',
+      messages: [
+        {
+          role: 'user',
+          content: [
+            {
+              type: 'text',
+              text: 'The coloring was done by a 5-year-old kid. Give a score between 1 to 10 in JSON format.',
+            },
+            {
+              type: 'image_url',
+              image_url: {
+                url: screenshotUrl,
+              },
+            },
+          ],
+        },
+      ],
+      max_tokens: 300,
+    };
+
+    // Call the OpenAI API
+    const openAiResponse = await axios.post(
+      'https://api.openai.com/v1/chat/completions',
+      openAiPayload,
+      {
+        headers: {
+          'Content-Type': 'application/json',
+          Authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
+        },
+      }
+    );
+
+    // Extract the score from OpenAI's response
+    const scoreResponse = openAiResponse.data.choices[0].message.content;
+    const parsedScore = JSON.parse(scoreResponse);
+
+    // Save to MongoDB
+    const gameData = new Game({
+      gameName,
+      userId,
+      gameID,
+      gameTime,
+      score: parsedScore.score || 'N/A', // Adjust key to match OpenAI response
+      gameStar,
+      screenshotUrl,
+    });
+
+    await gameData.save();
+
+    // Respond with the saved data
+    return res.status(201).json({
+      message: 'Game data saved successfully',
+      data: gameData,
+    });
+  } catch (error) {
+    console.error('Error:', error.message);
+    return res.status(500).json({ error: 'Something went wrong' });
+  }
+};
+
+module.exports = aiEvaluateImageToScore;

src/routes/api/aiEvaluateImageToStar.js

@@ -0,0 +1,88 @@
+const AWS = require('aws-sdk');
+const axios = require('axios');
+const mongoose = require('mongoose');
+const Game = require('../../models/gameModel'); // Ensure this path matches your project structure
+
+const aiEvaluateImageToStar = async (req, res) => {
+  try {
+    const { userId, gameName, gameID, gameTime, gameStar, screenShot } = req.body;
+    if (!screenShot) {
+      return res.status(400).json({ error: 'Screenshot is required' });
+    }
+
+      const formData = new FormData();
+      formData.append('image', screenShot);
+      const screenshotUrl = await axios.post('https://teachertrainingchennai.in/api/uploadBase64/', formData, {
+        headers: {
+          'Content-Type': 'multipart/form-data',
+        },
+      })
+        // .then(response => {
+        //   console.log('Response:', response.data);
+        // })
+        // .catch(error => {
+        //   console.error('Error:', error);
+        // });
+        console.log(screenshotUrl.data.filePath)
+    const openAiPayload = {
+      model: 'gpt-4o-mini',
+      messages: [
+        {
+          role: 'user',
+          content: [
+            {
+              type: 'text',
+              text: 'The coloring was done by a 5-year-old kid. Give a score between 1 to 10 in JSON format.',
+            },
+            {
+              type: 'image_url',
+              image_url: {
+                url: screenshotUrl.data.filePath,
+              },
+            },
+          ],
+        },
+      ],
+      max_tokens: 300,
+    };
+
+    console.log('AAAAAA')
+    const openAiResponse = await axios.post(
+      'https://api.openai.com/v1/chat/completions',
+      openAiPayload,
+      {
+        headers: {
+          'Content-Type': 'application/json',
+          Authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
+        },
+      }
+    );
+
+    // Extract the score from OpenAI's response
+    const scoreResponse = openAiResponse.data.choices[0].message.content;
+    const parsedScore = JSON.parse(scoreResponse);
+
+console.log(openAiResponse)
+    const gameData = new Game({
+      gameName,
+      userId,
+      gameID,
+      gameTime,
+      score: parsedScore.score || 'N/A', // Adjust key to match OpenAI response
+      gameStar,
+      screenshotUrl,
+    });
+
+    await gameData.save();
+
+    return res.status(200).json({
+      message: 'Game data saved successfully',
+      data: gameData,
+    });
+  } catch (error) {
+    console.error('Error:', error.message);
+    return res.status(500).json({ error: 'Something went wrong' });
+  }
+};
+
+module.exports = aiEvaluateImageToStar;

src/routes/api/aiFeedbackActionParent.js

@@ -0,0 +1,35 @@
+const aiFeedbackOnReport = async (req, res) => {
+  const fetch = require('node-fetch');
+  const url = process.env.AI_API_ENDOINT;
+  const api_key = process.env.AI_API_KEY;
+  const payload = {
+    "model": "gpt-3.5-turbo-16k",
+    "messages": [{ "role": "user", 
+      "content": JSON.stringify(req.body.grades) + "   -  first dataset(grades) " + JSON.stringify(req.body.suggested) + "   -  second dataset(suggested actions) " + req.body.instruction }]
+  }
+  try {
+    const apiResponse = await fetch(url, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+        'Authorization': `Bearer ${api_key}`,
+        // 'Authorization': api_key,
+      },
+      body: JSON.stringify(payload)
+    });
+
+    if (!apiResponse.ok) {
+      return res.status(apiResponse.status).send({ error: 'API response was not ok'+res });
+    }
+
+    const data = await apiResponse.json();
+
+    res.send(data);
+
+  } catch (error) {
+    console.error('Error fetching data:', error);
+    res.status(500).send({ error: 'Something went wrong with the fetch operation' });
+  }
+}
+
+module.exports = aiFeedbackOnReport;

src/routes/api/aiFeedbackOnReport.js

@@ -0,0 +1,35 @@
+const aiFeedbackOnReport = async (req, res) => {
+  // const fetch = require('node-fetch');
+  const url = process.env.AI_API_ENDOINT;
+  const api_key = process.env.AI_API_KEY;
+  const payload = {
+    "model": "gpt-4o-mini",
+    "messages": [{ "role": "user", 
+      "content": JSON.stringify(req.body.score) + "   -  is the students report card. " + req.body.instruction }]
+  }
+  try {
+    const apiResponse = await fetch(url, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+        'Authorization': `Bearer ${api_key}`,
+        // 'Authorization': api_key,
+      },
+      body: JSON.stringify(payload)
+    });
+
+    if (!apiResponse.ok) {
+      return res.status(apiResponse.status).send({ error: 'API response was not ok'+res });
+    }
+
+    const data = await apiResponse.json();
+
+    res.send(data);
+
+  } catch (error) {
+    console.error('Error fetching data:', error);
+    res.status(500).send({ error: 'Something went wrong with the fetch operation' });
+  }
+}
+
+module.exports = aiFeedbackOnReport;

src/routes/api/aiFeedbackOnReportWithFollowup.js

@@ -0,0 +1,95 @@
+const aiFollowupQuestion = async (req, res) => {
+
+  const { MongoClient } = require('mongodb');
+  const fetch = require('node-fetch');
+  const { v4: uuidv4 } = require('uuid');
+  const url = process.env.MONGODB_URL;
+  const dbName = process.env.MONGO_DB_NAME;
+  const client = new MongoClient(url);
+  
+  await client.connect();
+  const database = client.db(dbName); // Replace with your database name
+  const conversationsCollection = database.collection('conversations'); // Replace with your collection name
+  
+  async function fetchOpenAICompletion(prompt, messages, model = "gpt-3.5-turbo-16k", max_tokens = 200) {
+    const response = await fetch('https://api.openai.com/v1/chat/completions', {
+      method: 'POST',
+      headers: {
+        'Authorization': `Bearer ${process.env.OPENAI_KEY}`,
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({
+        model: model,
+        messages: messages,
+        max_tokens: max_tokens,
+      }),
+    });
+  
+    const data = await response.json();
+    return data.choices[0].message.content;
+  }
+  
+  
+    try {
+      const { prompt, sessionId, model = "gpt-4o-mini", max_tokens = 200 } = req.body;
+      if (!conversationsCollection) {
+        return res.status(500).json({
+          success: false,
+          error: 'MongoDB is not connected yet. Please try again later.',
+        });
+      }
+      let conversation;
+  
+      if (!sessionId) {
+        const newSessionId = uuidv4();
+  
+        conversation = {
+          sessionId: newSessionId,
+          conversationHistory: [
+            { role: 'system', content: 'You are a helpful assistant.' },
+            // { role: 'user', content: prompt },
+            { role: 'user', content: req.body.score + "   -  is the students report card. " + req.body.instruction },
+          ],
+        };
+        await conversationsCollection.insertOne(conversation);
+      } else {
+        conversation = await conversationsCollection.findOne({ sessionId: sessionId });
+        if (!conversation) {
+          return res.status(400).json({
+            success: false,
+            error: 'Invalid session ID.',
+          });
+        }
+        conversation.conversationHistory.push({ role: 'user', content: prompt });
+      }
+  
+      // Call OpenAI API with the conversation history
+      const aiResponse = await fetchOpenAICompletion(prompt, conversation.conversationHistory, model, max_tokens);
+  
+      // Add the AI's response to the conversation history
+      conversation.conversationHistory.push({ role: 'assistant', content: aiResponse });
+  
+      // Update the conversation in MongoDB
+      await conversationsCollection.updateOne(
+        { sessionId: conversation.sessionId },
+        { $set: { conversationHistory: conversation.conversationHistory } }
+      );
+  
+      // Send the AI's response and session ID back to the frontend
+      res.json({
+        success: true,
+        data: aiResponse,
+        sessionId: conversation.sessionId,  // Return the session ID for subsequent queries
+      });
+    } catch (error) {
+      console.error('Error generating response:', error.message);
+      res.status(500).json({
+        success: false,
+        error: 'Something went wrong. Please try again later.',
+      });
+    }
+  
+  
+  }
+  
+  module.exports = aiFollowupQuestion;

src/routes/api/aiFollowupQuestion.js

@@ -0,0 +1,91 @@
+const aiFollowupQuestion = async (req, res) => {
+
+  const { MongoClient } = require('mongodb');
+  const fetch = require('node-fetch');
+  const { v4: uuidv4 } = require('uuid');
+  const url = process.env.MONGODB_URL;
+  const dbName = process.env.MONGO_DB_NAME;
+  const client = new MongoClient(url);
+  
+  await client.connect();
+  const database = client.db(dbName);
+  const conversationsCollection = database.collection('conversations');
+  
+  async function fetchOpenAICompletion(prompt, messages, model = "gpt-3.5-turbo-16k", max_tokens = 200) {
+    const response = await fetch('https://api.openai.com/v1/chat/completions', {
+      method: 'POST',
+      headers: {
+        'Authorization': `Bearer ${process.env.OPENAI_KEY}`,
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({
+        model: model,
+        messages: messages,
+        max_tokens: max_tokens,
+      }),
+    });
+  
+    const data = await response.json();
+    return data;
+  }
+  
+  
+    try {
+      const { prompt, sessionId, model = "gpt-3.5-turbo-16k", max_tokens = 200 } = req.body;
+      if (!conversationsCollection) {
+        return res.status(500).json({
+          success: false,
+          error: 'MongoDB is not connected yet. Please try again later.',
+        });
+      }
+      let conversation;
+  
+      if (!sessionId) {
+        const newSessionId = uuidv4();
+  
+        conversation = {
+          sessionId: newSessionId,
+          conversationHistory: [
+            { role: 'system', content: 'answer within 50 words' },
+            { role: 'user', content: prompt },
+          ],
+        };
+        await conversationsCollection.insertOne(conversation);
+      } else {
+        conversation = await conversationsCollection.findOne({ sessionId: sessionId });
+        if (!conversation) {
+          return res.status(400).json({
+            success: false,
+            error: 'Invalid session ID.',
+          });
+        }
+        conversation.conversationHistory.push({ role: 'user', content: prompt });
+      }
+  
+      const aiResponse = await fetchOpenAICompletion(prompt, conversation.conversationHistory, model, max_tokens);
+  
+      conversation.conversationHistory.push({ role: 'assistant', content: aiResponse.choices[0].message.content });
+  
+      await conversationsCollection.updateOne(
+        { sessionId: conversation.sessionId },
+        { $set: { conversationHistory: conversation.conversationHistory } }
+      );
+  
+      res.json({
+        success: true,
+        data: aiResponse.choices[0].message.content,
+        total_tokens: aiResponse.usage.total_tokens,
+        sessionId: conversation.sessionId,
+      });
+    } catch (error) {
+      console.error('Error generating response:', error.message);
+      res.status(500).json({
+        success: false,
+        error: 'Something went wrong. Please try again later.',
+      });
+    }
+  
+  
+  }
+  
+  module.exports = aiFollowupQuestion;

src/routes/api/aiMarkDrawing.js

@@ -0,0 +1,116 @@
+const aiMarkDrawing = async (req, res) => {
+
+  
+  const express = require('express');
+const fetch = require('node-fetch'); // For making HTTP requests
+const path = require('path');
+const fs = require('fs');
+
+// Create an Express app
+const app = express();
+const port = 3000;
+
+// Middleware to handle JSON and URL-encoded form data
+app.use(express.json());
+app.use(express.urlencoded({ extended: true }));
+
+// Replace with your OpenAI API key
+const OPENAI_API_KEY = 'your-openai-api-key';
+
+// Route to handle image upload and forward it to OpenAI API
+app.post('/upload-image', async (req, res) => {
+    try {
+        // Check if the file is included in the request
+        if (!req.headers['content-type']?.includes('multipart/form-data')) {
+            return res.status(400).send({ message: 'No file uploaded' });
+        }
+
+        // Extract the raw body data (binary) from the request
+        let imageBuffer = Buffer.alloc(0);
+
+        // Collect the image data from the stream (raw body)
+        req.on('data', chunk => {
+            imageBuffer = Buffer.concat([imageBuffer, chunk]);
+        });
+
+        req.on('end', async () => {
+            // Forward the image to OpenAI's API for scoring
+            const score = await sendImageToOpenAI(imageBuffer);
+
+            // Respond with the score
+            res.json({ score: score });
+        });
+
+    } catch (error) {
+        console.error('Error processing the image:', error);
+        res.status(500).send({ message: 'Internal Server Error' });
+    }
+});
+
+// Function to send image to OpenAI API and get the response
+async function sendImageToOpenAI(imageBuffer) {
+    try {
+        // Convert binary image data to base64 format
+        const imageBase64 = imageBuffer.toString('base64');
+
+        // Make a request to the OpenAI API using fetch
+        const response = await fetch('https://api.openai.com/v1/chat/completions', {
+            method: 'POST',
+            headers: {
+                'Authorization': `Bearer ${OPENAI_API_KEY}`,
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                model: "gpt-4-vision", // Use the appropriate model
+                messages: [
+                    { role: "system", content: "You are a grading assistant for 2nd grade students." },
+                    { role: "user", content: "Evaluate this student's drawing or coloring." }
+                ],
+                functions: {
+                    function_call: {
+                        name: "vision_function",
+                        args: {
+                            image: imageBase64 // Send the image in base64 format
+                        }
+                    }
+                }
+            })
+        });
+
+        // Parse the response
+        const data = await response.json();
+
+        // Extract the score or other relevant information from the response
+        const aiResponse = data.choices[0].message.content;
+
+        // You can parse this response and extract the score, for example:
+        const score = extractScoreFromResponse(aiResponse);
+        return score;
+
+    } catch (error) {
+        console.error('Error interacting with OpenAI API:', error);
+        throw error;
+    }
+}
+
+// Example function to extract a score from OpenAI's response (you need to define this based on the actual response structure)
+function extractScoreFromResponse(responseText) {
+    // You can use a regex or logic to extract the score from OpenAI's response
+    const scoreMatch = responseText.match(/score:\s*(\d+)/i);
+    if (scoreMatch) {
+        return parseInt(scoreMatch[1], 10);
+    } else {
+        // If no score is found, return a default value
+        return 0;
+    }
+}
+
+// Start the server
+app.listen(port, () => {
+    console.log(`Server running at http://localhost:${port}`);
+});
+
+
+}
+
+module.exports = aiMarkDrawing;

src/routes/api/aiTextToSpeech.js

@@ -0,0 +1,85 @@
+const axios = require('axios');
+const AWS = require('aws-sdk');
+const FormData = require('form-data');
+
+const aiTextToSpeech = async (req, res) => {
+  AWS.config.update({
+    region: 'ap-south-1',
+    accessKeyId: process.env.AWS_polly2_ACCESS_KEY_ID,
+    secretAccessKey: process.env.AWS_polly2_SECRET_ACCESS_KEY,
+  });
+
+  const polly = new AWS.Polly();
+  const voiceIdMap = {
+    "Ava": "Ivy",
+    "Monstero": "Justin",
+    "Dax": "Kevin",
+    "Kai": "Danielle",
+    "Arlo": "Ruth",
+    "Yara": "Salli"
+  };
+
+  const { text, voiceId: frontendVoiceId } = req.body;
+
+  if (!text || !frontendVoiceId) {
+    return res.status(400).json({ error: 'Text and voiceId are required' });
+  }
+
+  const pollyVoiceId = voiceIdMap[frontendVoiceId];
+  if (!pollyVoiceId) {
+    return res.status(400).json({ error: 'Invalid voiceId provided' });
+  }
+
+  const params = {
+    Text: text,
+    OutputFormat: 'mp3',
+    Engine: 'neural',
+    VoiceId: pollyVoiceId,
+  };
+
+  try {
+    function generateTimestampWithRandomDigits() {
+      const now = new Date();
+      const formattedDate = now.toISOString().replace('T', '_').replace(/\..+/, '').replace(/:/g, '-');
+      const randomDigits = Math.floor(Math.random() * 10000).toString().padStart(4, '0');
+      return `${formattedDate}_${randomDigits}`;
+    }
+
+    const folder = generateTimestampWithRandomDigits();
+    const folderName = `audio_files/${folder}`;
+
+    const data = await polly.synthesizeSpeech(params).promise();
+    const audioBuffer = data.AudioStream;
+
+    const textBuffer = Buffer.from(text, 'utf-8');
+
+    const formData = new FormData();
+    formData.append('file1', audioBuffer, 'speech.mp3');
+    formData.append('file2', textBuffer, 'text.txt');
+    // formData.append('folder', folderName); // Send folder name
+    formData.append('bucket', 'polly-bs'); // Adjust bucket
+
+    const response = await axios.post(
+      'https://preschool-curriculum.in/api/one/v1/file/upload',
+      formData,
+      {
+        headers: {
+          // 'Authorization': `Bearer ${process.env.BEARER_TOKEN}`,
+          ...formData.getHeaders() // Ensure FormData headers are included
+        }
+      }
+    );
+
+    res.json({
+      message: 'Speech generated and saved successfully',
+      fileUrls: response.data.urls,
+      fileKeys: response.data.data
+    });
+
+  } catch (error) {
+    console.error('Error generating speech or uploading files:', error);
+    res.status(500).json({ error: 'Error generating speech or uploading files' });
+  }
+};
+
+module.exports = aiTextToSpeech;

src/routes/api/generateQuestions copy.js

@@ -0,0 +1,35 @@
+const mysql = require("mysql2");
+
+const generateQuestions = (req, res) => {
+    const connection = mysql.createConnection({
+    host: process.env.MARIA_HOST,
+    user: process.env.MARIA_USER,
+    password: process.env.MARIA_PASS,
+    database: process.env.MARIA_DBNM
+    });
+
+    connection.connect((err) => {
+    if (err) {
+        console.error('Error connecting to the database:', err);
+        return;
+    }
+    console.log('Connected to the MariaDB database.');
+    });
+
+    const { questions } = req.body;
+    res.send( questions );
+    questions.forEach(question => {
+        const { questionText, options, correctAnswer } = question;
+        const sql = 'INSERT INTO quiz_questions (questionText, option1, option2, option3, option4, correctAnswer) VALUES (?, ?, ?, ?, ?, ?)';
+        db.query(sql, [questionText, options[0], options[1], options[2], options[3], correctAnswer], (err, result) => {
+            if (err) {
+                console.error('Error inserting question:', err);
+                res.status(500).send('Error inserting question');
+                return;
+            }
+        });
+    });
+    
+    res.status(200).send('Questions added successfully');
+}
+module.exports = generateQuestions;

src/routes/api/generateQuestions.js

@@ -1,35 +1,48 @@
-const mysql = require("mysql2");
+var MongoClient = require('mongodb').MongoClient;
+// const AWS = require('aws-sdk');
 
-const generateQuestions = (req, res) => {
-    const connection = mysql.createConnection({
-    host: process.env.MARIA_HOST,
-    user: process.env.MARIA_USER,
-    password: process.env.MARIA_PASS,
-    database: process.env.MARIA_DBNM
-    });
+const saveGameScore = (req, res) => {
+    const url = process.env.MONGODB_URL;
+    const dbName = process.env.MONGO_DB_NAME;
+    const client = new MongoClient(url, { useUnifiedTopology: true });
+    client.connect((err) => {
+        if (err) {
+            console.error('Failed to connect to the server', err);
+            return;
+        }
+        // console.log('Connected successfully to server');
+        const db = client.db(dbName);
+        const collection = db.collection('questions');
+        // const data = req.body;
+        const { question, option1, option2, option3, option4, correctAnswer, moduleId } = req.body;
+        const data = {
+            question: question,
+            option1: option1,
+            option2: option2,
+            option3: option3,
+            option4: option4,
+            correctAnswer: correctAnswer,
+            moduleId: moduleId
+        };
 
-    connection.connect((err) => {
-    if (err) {
-        console.error('Error connecting to the database:', err);
-        return;
-    }
-    console.log('Connected to the MariaDB database.');
-    });
-
-    const { questions } = req.body;
-    res.send( questions );
-    questions.forEach(question => {
-        const { questionText, options, correctAnswer } = question;
-        const sql = 'INSERT INTO quiz_questions (questionText, option1, option2, option3, option4, correctAnswer) VALUES (?, ?, ?, ?, ?, ?)';
-        db.query(sql, [questionText, options[0], options[1], options[2], options[3], correctAnswer], (err, result) => {
+        collection.insertOne(data, (err, result) => {
             if (err) {
-                console.error('Error inserting question:', err);
-                res.status(500).send('Error inserting question');
-                return;
+                console.error('Failed to insert document', err);
+            } else {
+                // console.log('Document inserted with _id: ', result.insertedId);
             }
+            client.close((err) => {
+                if (err) {
+                    console.error('Failed to close connection', err);
+                } else {
+                    console.log('Connection closed');
+                    
+                    res.send(result.insertedId);
+                }
+            });
         });
     });
+  };
+  
+  module.exports = saveGameScore;
   
-    res.status(200).send('Questions added successfully');
-}
-module.exports = generateQuestions;

src/routes/api/getGalleryImage.js

@@ -0,0 +1,36 @@
+const GalleryImage = require('../../models/getGalleyImage');
+
+const getGalleryImage = async (req, res) => {
+    try {
+        // Extract query parameters for filtering (if provided)
+        const { gameName, userId, gameID} = req.query;
+
+        // Build a filter object based on the query parameters
+        const filter = {};
+
+        if (gameName) {
+            filter.gameName = gameName;
+        }
+
+        if (userId) {
+            filter.userId = userId;
+        }
+        if (gameID) {
+            filter.gameID = gameID;
+        }
+
+        // Fetch gallery images with filters and selected fields
+        const galleryImages = await GalleryImage.find(filter).select('gameName userId gameID screenshotUrl');   // Select only these fields
+
+        if (!galleryImages.length) {
+            return res.status(404).json({ message: 'No images found' });
+        }
+
+        return res.status(200).json(galleryImages);  // Return the filtered gallery images with selected fields
+    } catch (error) {
+        console.error('Error fetching images:', error.message);
+        return res.status(500).json({ error: 'Something went wrong' });
+    }
+};
+
+module.exports = getGalleryImage;

src/routes/api/getGameScore.js

@@ -1,32 +1,52 @@
-const mysql = require("mysql2");
 const getGameScore = (req, res) => {
-    const pool = mysql.createPool({
-        host: process.env.MARIA_HOST,
-        user: process.env.MARIA_USER,
-        password: process.env.MARIA_PASS,
-        database: 'beanstalk_game',
-        waitForConnections: true,
-        connectionLimit: 10,
-        queueLimit: 0
-    });
-    const promisePool = pool.promise();
-    const { userId, gameName, gameID } = req.body;
+    const { MongoClient } = require('mongodb');
 
-    const query = 'SELECT score, gameTime FROM gameData WHERE userId = ? AND gameName = ? AND gameID = ?';
+async function main() {
+    const url = process.env.MONGODB_URL;
+    const dbName = process.env.MONGO_DB_NAME;
+    const client = new MongoClient(url, { useNewUrlParser: true, useUnifiedTopology: true });
 
-    pool.query(query, [userId, gameName, gameID], (error, results) => {
-        if (error) {
-            return res.status(500).json({ message: 'Database query failed', error });
-        }
+    try {
+        // Connect to the MongoDB client
+        await client.connect();
 
-        if (results.length > 0) {
-            const game = results[0];
-            res.json({ score: game.score, gameTime: game.gameTime });
-        } else {
-            res.status(404).json({ message: 'Game data not found' });
-        }
-    });
-  };
+        // Select the database and collection
+        const database = client.db(dbName); // Replace with your database name
+        const collection = database.collection('gameData'); // Replace with your collection name
 
-  module.exports = getGameScore;
+        // Find a single document
+        // const user = await collection.findOne({ userId: 'dsfdfgffgfgeg' });
+
+        const { userId, gameName, gameID } = req.body;
+            const gameData = await collection.findOne({
+                userId: userId,
+                gameName: gameName,
+                gameID: gameID
+            });
+    
+            if (gameData) {
+                res.json({gameData});
+            } else {
+                res.status(404).json({ message: 'Game data not found' });
+            }
+
+        // Output the result
+        // if (user) {
+        //     console.log('User found:', user);
+        // } else {
+        //     console.log('No user found with the username "john_doe"');
+        // }
+    } catch (error) {
+        console.error('Error connecting to MongoDB or performing query:', error);
+    } finally {
+        // Close the connection
+        await client.close();
+    }
+}
+
+main().catch(console.error);
+
+};
+
+module.exports = getGameScore;
   

src/routes/api/saveGalleryImage.js

@@ -0,0 +1,78 @@
+const GalleryImage = require('../../models/imageGallery');
+const axios = require('axios');
+const FormData = require('form-data'); // Import form-data for Node.js
+
+const saveGalleryImage = async (req, res) => {
+    try {
+        const { userId, gameName, gameID, screenShot } = req.body;
+
+        // Validate base64 format
+        const isValidBase64 = validateBase64(screenShot);
+        if (!isValidBase64) {
+            return res.status(400).json({ error: 'Invalid base64 image data' });
+        }
+
+        // Extract image details
+        const imageData = extractImageDataFromBase64(screenShot);
+        const contentType = imageData.contentType || 'image/jpeg'; // Default to JPEG
+
+        // Convert base64 to buffer
+        const buffer = Buffer.from(screenShot.replace(/^data:image\/(png|jpg|jpeg);base64,/, ''), 'base64');
+
+        // Create FormData
+        const formData = new FormData();
+        formData.append('file1', buffer, { filename: 'screenshot.jpg', contentType });
+        formData.append('folder', 'gameGallery');
+        formData.append('bucket', 'polly-bs');
+
+        // Upload image to external API using axios
+        const uploadResponse = await axios.post(
+            'https://preschool-curriculum.in/api/one/v1/file/upload',
+            formData,
+            {
+                headers: {
+                    ...formData.getHeaders(), // Add form-data headers like Content-Type boundary
+                    // Add any necessary authentication headers if required
+                },
+            }
+        );
+
+        if (uploadResponse.status !== 200) {
+            throw new Error('Image upload failed: ' + uploadResponse.statusText);
+        }
+
+        // Extract and store the uploaded image URL from the response
+        const screenshotUrl = uploadResponse.data.urls[0];
+
+        // Create and save gallery image data
+        const galleryImageData = new GalleryImage({
+            userId,
+            gameName,
+            gameID,
+            screenshotUrl,
+            message: 'Image received',
+        });
+        await galleryImageData.save();
+
+        return res.status(200).json(galleryImageData);
+    } catch (error) {
+        console.error('Error:', error.message);
+        return res.status(500).json({ error: 'Something went wrong' });
+    }
+};
+
+// Validate base64 image
+function validateBase64(base64String) {
+    const regex = /^data:image\/(png|jpg|jpeg);base64,/;
+    return regex.test(base64String);
+}
+
+// Extract image data (like content type) from base64 string
+function extractImageDataFromBase64(base64String) {
+    const matches = base64String.match(/^data:image\/(png|jpg|jpeg);base64,/);
+    return {
+        contentType: matches ? `image/${matches[1]}` : 'image/jpeg',
+    };
+}
+
+module.exports = saveGalleryImage;

src/routes/api/saveGameScore.js

@@ -14,13 +14,14 @@ const saveGameScore = (req, res) => {
         const db = client.db(dbName);
         const collection = db.collection('gameData');
         // const data = req.body;
-        const { userId, gameName, gameID, gameTime, score, screenShot } = req.body;
+        const { userId, gameName, gameID, gameTime, score,gameStar, screenShot } = req.body;
         const data = {
             userId: userId,
             gameName: gameName,
             gameID: gameID,
             gameTime: gameTime,
-            score: score
+            score: score,
+            gameStar:gameStar
         };
 
         collection.insertOne(data, (err, result) => {

src/routes/api/saveGameScore2.js

@@ -0,0 +1,49 @@
+const AWS = require("aws-sdk");
+const Game = require("../../models/gameModel");
+
+const s3 = new AWS.S3({
+  accessKeyId: process.env.AWS_ACCESS_KEY_ID,
+  secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,
+  region: process.env.AWS_REGION,
+});
+
+const saveGameScore2 = async (req, res) => {
+  const { userId, gameName, gameID, gameTime, score, gameStar, screenShot } = req.body;
+  try {
+    if (!gameName || !userId || !gameID) {
+      return res.status(400).json({ error: "Missing required fields" });
+    }
+    const newGame = new Game({
+      gameName,
+      userId,
+      gameID,
+      gameTime,
+      score,
+      gameStar,
+    });
+    const result = await newGame.save();
+    if (screenShot) {
+    let base64Image = screenShot.split(";base64,").pop();   
+    const buffer = Buffer.from(base64Image, 'base64');
+    //   const buffer = Buffer.from(screenShot, "base64");
+      const uploadParams = {
+        Bucket: process.env.S3_BUCKET_NAME,
+        Key: `images/${result._id}.png`,
+        Body: buffer,
+        ContentType: "image/png",
+      };
+      const s3Response = await s3.upload(uploadParams).promise();
+      newGame.screenshotUrl = s3Response.Location;
+      await newGame.save();
+    }
+    res.status(200).json({
+      message: "Game data saved successfully",
+      data: newGame,
+    });
+  } catch (error) {
+    console.error("Error:", error);
+    res.status(500).json({ error: "Internal Server Error" });
+  }
+};
+
+module.exports = saveGameScore2;

src/routes/v1/api.route.js

@@ -23,6 +23,17 @@ const getGameScore = require("../api/getGameScore");
 const resultAfterQuizSubmit = require("../api/resultAfterQuizSubmit");
 const generateQuestions = require("../api/generateQuestions");
 const saveGameScore = require("../api/saveGameScore");
+const saveGameScore2 = require("../api/saveGameScore2");
+const aiFeedbackOnReport = require("../api/aiFeedbackOnReport");
+const aiFeedbackActionParent = require("../api/aiFeedbackActionParent");
+const aiFollowupQuestion = require("../api/aiFollowupQuestion");
+const aiMarkDrawing = require("../api/aiMarkDrawing");
+const aiFeedbackOnReportWithFollowup = require("../api/aiFeedbackOnReportWithFollowup");
+const aiTextToSpeech = require("../api/aiTextToSpeech");
+const aiEvaluateImageToStar = require("../api/aiEvaluateImageToStar");
+const saveGalleryImage = require("../api/saveGalleryImage");
+const getGalleryImage = require("../api/getGalleryImage");
+
 
 
 const router = express.Router();
@@ -142,6 +153,58 @@ router.get("/ping", (req, res) => {
     saveGameScore(req, res);
   });
   
+  // For Quiz Result After Submit Quiz
+  router.post("/saveGameScore2", (req, res) => {
+    saveGameScore2(req, res);
+  });
+  
+    
+  // AI feedback for teacher on children's report card
+  router.post("/aiFeedbackOnReport", (req, res) => {
+    aiFeedbackOnReport(req, res);
+  });
+  // AI feedback for teacher on children's report card
+  router.post("/aiFeedbackOnReportWithFollowup", (req, res) => {
+    aiFeedbackOnReportWithFollowup(req, res);
+  });
+  
+  // AI top ten action items for a parent of this child.
+  router.post("/aiFeedbackActionParent", (req, res) => {
+    aiFeedbackActionParent(req, res);
+  });
+
+  // AI to respond on initial and followup questions.
+  router.post("/aiFollowupQuestion", (req, res) => {
+    aiFollowupQuestion(req, res);
+  });
+
+  // AI to provide star for drawing.
+  router.post("/aiMarkDrawing", (req, res) => {
+    aiMarkDrawing(req, res);
+  });
+
+  // AI to provide star for drawing.
+  router.post("/aiTextToSpeech", (req, res) => {
+    aiTextToSpeech(req, res);
+  });
+
+  // AI to provide star for drawing.
+  router.post("/aiEvaluateImageToStar", (req, res) => {
+    aiEvaluateImageToStar(req, res);
+  });
+
+
+// Save Drawing Game Gallery Image
+  router.post("/saveGalleryImage", (req, res) => {
+    saveGalleryImage(req, res);
+  });
+
+  // Get Drawing Game Gallery Image
+  router.get("/getGalleryImage", (req, res) => {
+    getGalleryImage(req, res);
+  });
+  
+  
 
 module.exports = router;