WIN251287 - added features : Pdf report generation with Verifiable QR code

.gitignore

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+vi-notes-backend/node_modules
+vi-notes-frontend/node_modules

vi-notes-backend/app.js

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+const express = require('express');
+const app = express();
+const cors = require('cors');
+const mongoose = require('mongoose');
+const puppeteer = require('puppeteer');
+const handlebars = require('handlebars');
+const QRCode = require('qrcode');
+const fs = require('fs');
+const path = require('path');
+
+const Reports = require('./models/reports')
+const port = 3000;
+
+app.use(express.json()); 
+app.use(express.urlencoded({ extended: true }));
+
+app.use(cors({
+  origin: 'http://localhost:5173'
+}));
+
+
+const mongoURI = 'mongodb://127.0.0.1:27017/vi-notes';
+
+mongoose.connect(mongoURI)
+  .then(() => console.log('Connected to MongoDB successfully!'))
+  .catch((err) => console.error('MongoDB connection error:', err));
+
+
+app.get('/results/:id', async (req, res) => {
+    sessID = req.params.id;
+    try{
+        const searchedReport = await Reports.findOne({ sessionID: sessID });
+        console.log(searchedReport);
+        if (!searchedReport) {
+            return res.status(404).send("Report not found!");
+        }
+        res.json({
+            sessionID: sessID,
+            name: searchedReport.name,
+            date: searchedReport.date,
+            content: searchedReport.content,
+            overallScore: searchedReport.overallScore,
+            pasteToTypeRatio: searchedReport.pasteToTypeRatio,
+            revisionBackspaceIndex: searchedReport.revisionBackspaceIndex,
+            interKeystrokeTiming: searchedReport.interKeystrokeTiming,
+            pauseAnalysis: searchedReport.pauseAnalysis,
+            totalActiveTime: searchedReport.totalActiveTime,
+            documentLength: searchedReport.documentLength,
+            perplexityScore: searchedReport.perplexityScore,
+            burstinessScore: searchedReport.burstinessScore
+        });
+    } catch (error) {
+        res.status(500).send("Server Error");
+    }
+});
+
+app.get('/download/:id', async (req, res) => {
+    sessID = req.params.id;
+    let report;
+    try{
+        const searchedReport = await Reports.findOne({ sessionID: sessID });
+        console.log(searchedReport);
+        if (!searchedReport) {
+            return res.status(404).send("Report not found!");
+        }
+        report = searchedReport.toJSON();
+    } catch (error) {
+        return res.status(500).send("Server Error");
+    };
+    console.log(JSON.stringify(report));
+    try{
+        const qrString = `http://localhost:5173/verify/${sessID}`;
+
+        const qrDataURI = await QRCode.toDataURL(qrString);
+
+        const data = {
+            qrCodeImgSrc: qrDataURI,
+            report : report
+        }
+
+        const templateHtml = fs.readFileSync(path.join(__dirname, 'report_template.html'), 'utf8');
+
+        const template = handlebars.compile(templateHtml);
+        const finalHtml = template(data);
+
+        const browser = await puppeteer.launch();
+        const page = await browser.newPage();
+
+        await page.setContent(finalHtml, { waitUntil: 'networkidle0' });
+
+        const pdfBuffer = await page.pdf({
+            format: 'A4',
+            printBackground: true,
+            margin: { top: '20px', right: '20px', bottom: '20px', left: '20px'}
+        });
+        await browser.close();
+
+        res.set({
+            'Content-Type': 'application/pdf',
+            'Content-Disposition': `attachment; filename="Report-${sessID}.pdf"`,
+            'Content-Length': pdfBuffer.length
+        });
+        res.send(pdfBuffer);
+    } catch(error) {
+        console.error("Error generating PDF:", error);
+        res.status(500).send("Error generating PDF");
+    }
+
+});
+app.post('/save', async (req, res) => {
+    const userData = req.body;
+    console.log("Got save request from : " + userData.username);
+    const prompt = `
+        You are an advanced Behavioral Biometrics and Forensics Scrutinizer. Your primary objective is to analyze typing dynamics and text content to determine whether a given piece of text was organically typed by a human, generated by an AI, or pasted/typed by an automated macro script.
+
+        You will receive a JSON payload containing:
+
+        'content': The final text output.
+
+        'typingLogs': An array of keystroke objects, detailing 'theKey' pressed, 'timeItHappened' (epoch timestamp), and 'speedFromLastOne' (milliseconds since the last keypress).
+
+        Input : 
+        'content' : ${userData.content}
+        'typingLogs' : ${JSON.stringify(userData.typingLogs)}
+
+        YOUR ANALYSIS DIRECTIVES
+        Evaluate the data against these heuristics to generate 0-100 scores (where 100 is highly human, and 0 is highly robotic/AI):
+
+        Copy/Paste Detection ('pasteToTypeRatio')
+
+        If a sequence of characters has a 'speedFromLastOne' of 0-5ms continuously, or multiple characters appear in a single log, this is a paste event. Map this to a 0-100 scale (100 = 100% pasted).
+
+        Robotic Cadence vs. Human Burstiness ('interKeystrokeTiming')
+
+        Flat, perfectly constrained typing speeds (e.g., exactly 50ms) indicate a bot (Score closer to 0). High variance in 'speedFromLastOne' strongly indicates a human (Score closer to 100).
+
+        The Revision Index ('revisionBackspaceIndex')
+
+        The presence of 'Backspace', 'Delete', and immediate corrections strongly correlates with human organic typing (Score closer to 100). Zero backspaces over a long text is suspicious (Score closer to 0).
+
+        Contextual Think Time ('pauseAnalysis')
+
+        Humans pause longer at specific structural points: after hitting 'Space', before complex words, and after punctuation. Natural pausing yields a higher score.
+
+        Content Analysis ('perplexityScore' & 'burstinessScore')
+
+        Evaluate the text string. Highly predictable, uniform text (AI) gets lower scores. Creative, varied sentence structures get higher scores.
+
+        REQUIRED OUTPUT FORMAT
+        You must output YOUR ENTIRE RESPONSE as a single, valid, stringified JSON object exactly matching the keys below. Calculate the 'totalActiveTime' and 'documentLength' directly from the provided data.
+
+        Do NOT wrap the output in markdown code blocks (e.g., no '''json). Do NOT include any conversational text. Output ONLY the JSON.
+        Do not provide any html code or something else.  Only provide the json string even without '''json code blocks.
+        {
+            "overallScore": <0-100 integer: Your final weighted confidence that this is human-written>,
+            "pasteToTypeRatio": <0-100 integer: Estimated percentage of the text that was pasted>,
+            "revisionBackspaceIndex": <0-100 integer: 100 = heavy human-like revisions, 0 = flawless/scripted>,
+            "interKeystrokeTiming": <0-100 integer: 100 = highly varied, organic cadence. 0 = flat, robotic timing>,
+            "pauseAnalysis": <0-100 integer: 100 = natural human pauses at punctuation/words. 0 = machine-like continuous stream>,
+            "totalActiveTime": <integer: Total typing time in milliseconds (Addition of every time in typing logs (because it is difference between each keypresses.))>,
+            "documentLength": <integer: Total word count of the 'content' string>,
+            "perplexityScore": <0-100 integer: 100 = highly unpredictable human vocabulary. 0 = highly predictable AI vocabulary>,
+            "burstinessScore": <0-100 integer: 100 = high variance in sentence length. 0 = monotonous sentence structure>
+        }
+    `
+
+    const apiKey = "AIzaSyCccP3hkz7xqwayJjXFTK0FjH2fESf7fAQ";
+
+    const url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent?key=" + apiKey;
+
+    let resultsJSON;
+
+    try {
+        const response = await fetch(url, {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({
+                contents: [{ parts: [{ text: prompt }] }]
+            })
+        });
+
+        const data = await response.json();
+
+        if (!response.ok) {
+            return res.status(response.status).json(data);
+        }
+        if (!data.candidates || data.candidates.length === 0) {
+            return res.status(500).json({ 
+                error: "Gemini returned no content. It may have been blocked by safety filters.",
+                raw_data: data
+            });
+        }
+        let aiResponse = data.candidates[0].content.parts[0].text;
+
+        aiResponse = aiResponse.replace(/```json/gi, '').replace(/```/g, '').trim();
+
+        resultsJSON = JSON.parse(aiResponse);
+        console.log("Output of Gemini : ", resultsJSON);
+
+    } catch (error) {
+        console.error("Gemini/Parsing Error:", error);
+        return res.status(500).json({ error: 'AI Processing failed', details: error.message });
+    }
+
+    const report = new Reports({
+        name: userData.username || "Anonymous",
+        content: userData.content,
+        overallScore: resultsJSON.overallScore,
+        pasteToTypeRatio: resultsJSON.pasteToTypeRatio,
+        revisionBackspaceIndex: resultsJSON.revisionBackspaceIndex,
+        interKeystrokeTiming: resultsJSON.interKeystrokeTiming,
+        pauseAnalysis: resultsJSON.pauseAnalysis,
+        totalActiveTime: resultsJSON.totalActiveTime,
+        documentLength: resultsJSON.documentLength,
+        perplexityScore: resultsJSON.perplexityScore,
+        burstinessScore: resultsJSON.burstinessScore
+    });
+
+    try {
+        const newreport = await report.save();
+        res.status(200).json({
+            sessionID: newreport.sessionID
+        });
+    } catch (err) {
+        console.error("Mongoose Validation Error:", err);
+        res.status(400).json({ message: err.message });
+    }
+});
+
+app.listen(port, () => {
+  console.log(`Server running at http://localhost:${port}`);
+});

vi-notes-backend/models/reports.js

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+const mongoose = require('mongoose');
+const crypto = require('crypto');
+
+const reportSchema = new mongoose.Schema({
+    sessionID: { 
+        type: String, 
+        default: () => crypto.randomUUID(),
+        unique: true,
+
+        index: true
+    },
+    name: { 
+        type: String, 
+        required: true 
+    },
+    date: {
+        type: Date, 
+        default: Date.now
+    },
+    content: {
+        type:String,
+        required: true
+    },
+    overallScore: Number,
+    pasteToTypeRatio: Number,
+    revisionBackspaceIndex: Number,
+    interKeystrokeTiming: Number,
+    pauseAnalysis: Number,
+    totalActiveTime: Number,
+    documentLength: Number,
+    perplexityScore: Number,
+    burstinessScore: Number
+});
+
+module.exports = mongoose.model('Report', reportSchema);