Raheel-glicth · Raheel-glicth · Feb 23, 2026
diff --git a/README.md b/README.md
@@ -1,2 +1,29 @@
-# PathFinder
-help to find path for new students 
+# PathFinder AI
+
+PathFinder AI helps students choose the stream/career path that best fits them: Frontend, Backend, DevOps, Data Science, UI/UX, and Mobile Development.
+
+## What you get
+- 18 meaningful questions (10 core + 8 adaptive) instead of random quiz items
+- Adaptive logic: after core answers, follow-up questions are selected from your top 2 fields
+- ML-style percentage prediction for each stream
+- Personalized roadmap for top matches
+- Resource manager where you can add/remove your own links per stream (saved in localStorage)
+- Daily study-time aware roadmap pacing estimate
+
+## Train model on internet data
+You can train/update model bias weights from a public internet dataset (Stack Overflow survey) and feed them into the website.
+
+```bash
+python3 scripts/train_model.py
+```
+
+This creates/updates:
+- `data/model_weights.json`
+
+The app auto-loads this file at startup.
+
+## Run locally
+```bash
+python3 -m http.server 8000
+```
+Then open `http://localhost:8000`.
diff --git a/app.js b/app.js
@@ -0,0 +1,326 @@
+const fields = ["Frontend", "Backend", "DevOps", "Data Science", "UI/UX", "Mobile Development"];
+
+const likertOptions = [
+  { label: "Strongly Disagree", value: 1 },
+  { label: "Disagree", value: 2 },
+  { label: "Neutral", value: 3 },
+  { label: "Agree", value: 4 },
+  { label: "Strongly Agree", value: 5 }
+];
+
+const coreQuestions = [
+  { text: "I enjoy building visual interfaces users can directly interact with.", weights: { "Frontend": 1.2, "UI/UX": 1.1 } },
+  { text: "I like designing APIs and backend logic.", weights: { "Backend": 1.3, "DevOps": 0.4 } },
+  { text: "I am curious about data analysis and prediction models.", weights: { "Data Science": 1.4, "Backend": 0.4 } },
+  { text: "I enjoy automation, deployment and infrastructure work.", weights: { "DevOps": 1.4, "Backend": 0.5 } },
+  { text: "I care deeply about user behavior and product usability.", weights: { "UI/UX": 1.4, "Frontend": 0.5 } },
+  { text: "I want to build apps that run smoothly on mobile devices.", weights: { "Mobile Development": 1.5, "Frontend": 0.5 } },
+  { text: "I like solving complex bugs in layered systems.", weights: { "Backend": 0.8, "DevOps": 0.8, "Data Science": 0.4 } },
+  { text: "I prefer work that gives quick visible output.", weights: { "Frontend": 1.0, "Mobile Development": 0.8, "UI/UX": 0.7 } },
+  { text: "I am comfortable with statistics and mathematical reasoning.", weights: { "Data Science": 1.4, "Backend": 0.3 } },
+  { text: "I often think about scalability and reliability.", weights: { "DevOps": 1.1, "Backend": 1.0 } }
+];
+
+const adaptiveQuestions = {
+  "Frontend": [
+    { text: "I enjoy optimizing accessibility and browser compatibility.", weights: { "Frontend": 1.3, "UI/UX": 0.5 } },
+    { text: "I like component-based frameworks like React/Vue.", weights: { "Frontend": 1.2, "Mobile Development": 0.3 } },
+    { text: "I care about web performance metrics and Lighthouse scores.", weights: { "Frontend": 1.2, "DevOps": 0.3 } },
+    { text: "I enjoy translating design mockups into responsive pages.", weights: { "Frontend": 1.3, "UI/UX": 0.4 } }
+  ],
+  "Backend": [
+    { text: "I enjoy database schema design and data modeling.", weights: { "Backend": 1.3, "Data Science": 0.4 } },
+    { text: "I am interested in authentication and API security.", weights: { "Backend": 1.3, "DevOps": 0.4 } },
+    { text: "I like optimizing queries and reducing response latency.", weights: { "Backend": 1.2, "DevOps": 0.4 } },
+    { text: "I enjoy writing services consumed by web and mobile clients.", weights: { "Backend": 1.2, "Mobile Development": 0.4 } }
+  ],
+  "DevOps": [
+    { text: "I am excited by CI/CD pipelines and release automation.", weights: { "DevOps": 1.4, "Backend": 0.4 } },
+    { text: "I enjoy cloud infrastructure setup and monitoring.", weights: { "DevOps": 1.4 } },
+    { text: "I like containers and orchestration (Docker/Kubernetes).", weights: { "DevOps": 1.5 } },
+    { text: "I prefer improving developer productivity and deployment speed.", weights: { "DevOps": 1.3, "Frontend": 0.2 } }
+  ],
+  "Data Science": [
+    { text: "I like cleaning noisy datasets before modeling.", weights: { "Data Science": 1.2, "Backend": 0.3 } },
+    { text: "I enjoy feature engineering and model evaluation.", weights: { "Data Science": 1.4 } },
+    { text: "I am interested in data visualization and storytelling.", weights: { "Data Science": 1.2, "UI/UX": 0.4 } },
+    { text: "I want to build prediction systems from real-world behavior data.", weights: { "Data Science": 1.5 } }
+  ],
+  "UI/UX": [
+    { text: "I enjoy mapping user journeys and pain points.", weights: { "UI/UX": 1.4, "Frontend": 0.4 } },
+    { text: "I like creating design systems and reusable UI patterns.", weights: { "UI/UX": 1.3, "Frontend": 0.5 } },
+    { text: "I value usability testing and iterative feedback loops.", weights: { "UI/UX": 1.4 } },
+    { text: "I can explain design decisions with user evidence.", weights: { "UI/UX": 1.3, "Data Science": 0.3 } }
+  ],
+  "Mobile Development": [
+    { text: "I like designing interactions for smaller touch screens.", weights: { "Mobile Development": 1.4, "UI/UX": 0.4 } },
+    { text: "I enjoy working with app lifecycle and performance constraints.", weights: { "Mobile Development": 1.3, "Backend": 0.3 } },
+    { text: "I am interested in integrating device features like camera/GPS.", weights: { "Mobile Development": 1.4 } },
+    { text: "I want to publish apps and improve them using user reviews.", weights: { "Mobile Development": 1.3, "Frontend": 0.2 } }
+  ]
+};
+
+const roadmaps = {
+  "Frontend": ["HTML/CSS basics", "JavaScript + TypeScript", "React/Vue", "State/API integration", "Testing + accessibility"],
+  "Backend": ["Language fundamentals", "DB + SQL", "API architecture", "Security", "Scaling"],
+  "DevOps": ["Linux + networking", "Git + CI/CD", "Containers", "Cloud", "Observability"],
+  "Data Science": ["Python + SQL", "Statistics", "Data preprocessing", "ML modeling", "Deployment/MLOps"],
+  "UI/UX": ["UX principles", "Wireframing", "Research methods", "Design systems", "Developer handoff"],
+  "Mobile Development": ["Flutter/React Native or native stack", "Architecture", "State/API", "Performance", "Release + analytics"]
+};
+
+const defaultResources = {
+  "Frontend": ["https://developer.mozilla.org/en-US/docs/Web", "https://roadmap.sh/frontend"],
+  "Backend": ["https://roadmap.sh/backend", "https://www.postman.com/api-platform/api-testing/"],
+  "DevOps": ["https://roadmap.sh/devops", "https://docs.docker.com/get-started/"],
+  "Data Science": ["https://www.kaggle.com/learn", "https://scikit-learn.org/stable/"],
+  "UI/UX": ["https://www.nngroup.com/articles/", "https://www.figma.com/resource-library/"],
+  "Mobile Development": ["https://docs.flutter.dev/", "https://developer.android.com/courses"]
+};
+
+const defaultBias = {
+  "Frontend": 1,
+  "Backend": 1,
+  "DevOps": 1,
+  "Data Science": 1,
+  "UI/UX": 1,
+  "Mobile Development": 1
+};
+
+let modelBias = { ...defaultBias };
+let studentName = "";
+let dailyTime = 1;
+let questions = [];
+let answers = [];
+let idx = 0;
+
+const setupForm = document.getElementById("setupForm");
+const setupCard = document.getElementById("setupCard");
+const quizCard = document.getElementById("quizCard");
+const resultCard = document.getElementById("resultCard");
+const modelStatus = document.getElementById("modelStatus");
+const questionType = document.getElementById("questionType");
+const questionCounter = document.getElementById("questionCounter");
+const questionText = document.getElementById("questionText");
+const optionsWrap = document.getElementById("options");
+const prevBtn = document.getElementById("prevBtn");
+const nextBtn = document.getElementById("nextBtn");
+const resultTitle = document.getElementById("resultTitle");
+const resultMeta = document.getElementById("resultMeta");
+const scoreGrid = document.getElementById("scoreGrid");
+const roadmapWrap = document.getElementById("roadmapWrap");
+const restartBtn = document.getElementById("restartBtn");
+const resourceManager = document.getElementById("resourceManager");
+
+bootstrap();
+
+async function bootstrap() {
+  await loadModelWeights();
+  renderResourceManager();
+}
+
+async function loadModelWeights() {
+  try {
+    const response = await fetch("./data/model_weights.json", { cache: "no-store" });
+    if (!response.ok) throw new Error("weights file missing");
+    const data = await response.json();
+    if (data.field_bias && typeof data.field_bias === "object") {
+      modelBias = { ...defaultBias, ...data.field_bias };
+      modelStatus.textContent = `ML weights loaded (${data.source || "trained model"})`;
+      modelStatus.classList.add("ready");
+      return;
+    }
+    throw new Error("invalid weights structure");
+  } catch (_error) {
+    modelStatus.textContent = "Using default weights. Run scripts/train_model.py to train on internet dataset.";
+  }
+}
+
+setupForm.addEventListener("submit", (event) => {
+  event.preventDefault();
+  studentName = document.getElementById("studentName").value.trim();
+  dailyTime = Number(document.getElementById("dailyTime").value);
+
+  questions = [...coreQuestions];
+  answers = Array(18).fill(null);
+  idx = 0;
+
+  setupCard.classList.add("hidden");
+  quizCard.classList.remove("hidden");
+  renderQuestion();
+});
+
+prevBtn.addEventListener("click", () => {
+  if (idx === 0) return;
+  idx -= 1;
+  renderQuestion();
+});
+
+nextBtn.addEventListener("click", () => {
+  if (answers[idx] == null) return;
+
+  if (idx === coreQuestions.length - 1 && questions.length === coreQuestions.length) {
+    const top2 = getTopFields(calculateRawScores(answers.slice(0, 10), coreQuestions), 2);
+    questions = [...coreQuestions, ...top2.flatMap((f) => adaptiveQuestions[f].slice(0, 4))];
+  }
+
+  if (idx < questions.length - 1) {
+    idx += 1;
+    renderQuestion();
+  } else {
+    renderResults();
+  }
+});
+
+restartBtn.addEventListener("click", () => window.location.reload());
+
+function renderQuestion() {
+  const q = questions[idx];
+  questionType.textContent = idx < 10 ? "Core Question" : "Adaptive Question";
+  questionCounter.textContent = `${idx + 1}/${questions.length}`;
+  questionText.textContent = q.text;
+  prevBtn.disabled = idx === 0;
+
+  optionsWrap.innerHTML = "";
+  likertOptions.forEach((opt) => {
+    const button = document.createElement("button");
+    button.type = "button";
+    button.textContent = opt.label;
+    if (answers[idx] === opt.value) button.classList.add("active");
+    button.addEventListener("click", () => {
+      answers[idx] = opt.value;
+      renderQuestion();
+    });
+    optionsWrap.appendChild(button);
+  });
+}
+
+function calculateRawScores(answerList, questionList) {
+  const scores = Object.fromEntries(fields.map((f) => [f, 0]));
+  questionList.forEach((q, i) => {
+    const val = Number(answerList[i] ?? 3);
+    Object.entries(q.weights).forEach(([field, w]) => {
+      scores[field] += val * w;
+    });
+  });
+  return scores;
+}
+
+function getTopFields(scoreMap, take = 2) {
+  return Object.entries(scoreMap)
+    .sort((a, b) => b[1] - a[1])
+    .slice(0, take)
+    .map(([field]) => field);
+}
+
+function normalizeWithModel(rawScores) {
+  const logits = fields.map((f) => (rawScores[f] || 0) + (modelBias[f] || 0));
+  const expVals = logits.map((v) => Math.exp(v / 16));
+  const sum = expVals.reduce((a, b) => a + b, 0);
+  return fields
+    .map((field, i) => ({ field, percent: Math.round((expVals[i] / sum) * 100) }))
+    .sort((a, b) => b.percent - a.percent);
+}
+
+function estimateWeeksPerStep() {
+  const baseHoursPerStep = 70;
+  return Math.max(1, Math.round(baseHoursPerStep / (dailyTime * 7)));
+}
+
+function renderResults() {
+  quizCard.classList.add("hidden");
+  resultCard.classList.remove("hidden");
+
+  const raw = calculateRawScores(answers.slice(0, questions.length), questions);
+  const ranked = normalizeWithModel(raw);
+  const top3 = ranked.slice(0, 3);
+
+  resultTitle.textContent = `${studentName}, your stream match report`;
+  resultMeta.textContent = `Predicted with weighted assessment + trained model bias. Suggested pace: ~${estimateWeeksPerStep()} week(s) per roadmap step.`;
+
+  scoreGrid.innerHTML = ranked
+    .map(({ field, percent }) => `
+      <article class="score-card">
+        <strong>${field}</strong>
+        <div>${percent}% interest</div>
+        <div class="bar"><div class="fill" style="width:${percent}%"></div></div>
+      </article>
+    `)
+    .join("");
+
+  roadmapWrap.innerHTML = top3
+    .map(({ field }) => {
+      const steps = roadmaps[field].map((s) => `<li>${s}</li>`).join("");
+      const links = [...defaultResources[field], ...getCustomResources(field)]
+        .map((url) => `<li><a href="${url}" target="_blank" rel="noopener noreferrer">${url}</a></li>`)
+        .join("");
+      return `
+        <article class="roadmap">
+          <h4>${field}</h4>
+          <ol>${steps}</ol>
+          <strong>Resources</strong>
+          <ul>${links}</ul>
+        </article>
+      `;
+    })
+    .join("");
+}
+
+function keyFor(field) {
+  return `pathfinder_resources_${field}`;
+}
+
+function getCustomResources(field) {
+  return JSON.parse(localStorage.getItem(keyFor(field)) || "[]");
+}
+
+function addCustomResource(field, url) {
+  const items = getCustomResources(field);
+  items.push(url);
+  localStorage.setItem(keyFor(field), JSON.stringify(items));
+}
+
+function removeCustomResource(field, index) {
+  const items = getCustomResources(field);
+  items.splice(index, 1);
+  localStorage.setItem(keyFor(field), JSON.stringify(items));
+}
+
+function renderResourceManager() {
+  resourceManager.innerHTML = "";
+  fields.forEach((field) => {
+    const node = document.getElementById("resourceTemplate").content.cloneNode(true);
+    const box = node.querySelector(".resource-box");
+    const heading = box.querySelector("h4");
+    const list = box.querySelector(".resource-list");
+    const form = box.querySelector(".resource-form");
+
+    heading.textContent = field;
+
+    const paint = () => {
+      const custom = getCustomResources(field);
+      const defaults = defaultResources[field].map((url) => `<li><a href="${url}" target="_blank" rel="noopener noreferrer">${url}</a> <em>(default)</em></li>`);
+      const userLinks = custom.map((url, i) => `<li><a href="${url}" target="_blank" rel="noopener noreferrer">${url}</a> <button data-remove="${i}" type="button" class="secondary">x</button></li>`);
+      list.innerHTML = [...defaults, ...userLinks].join("");
+
+      list.querySelectorAll("button[data-remove]").forEach((btn) => {
+        btn.addEventListener("click", () => {
+          removeCustomResource(field, Number(btn.getAttribute("data-remove")));
+          paint();
+        });
+      });
+    };
+
+    form.addEventListener("submit", (event) => {
+      event.preventDefault();
+      const url = new FormData(form).get("url").toString().trim();
+      if (!url) return;
+      addCustomResource(field, url);
+      form.reset();
+      paint();
+    });
+
+    paint();
+    resourceManager.appendChild(node);
+  });
+}
diff --git a/data/model_weights.json b/data/model_weights.json
@@ -0,0 +1,13 @@
+{
+  "source": "fallback-sample (network blocked)",
+  "trained_at": "2026-02-23T16:05:25.705445+00:00",
+  "rows": 14,
+  "field_bias": {
+    "Frontend": 2.316,
+    "Backend": 2.316,
+    "DevOps": 2.316,
+    "Data Science": 2.316,
+    "UI/UX": 2.036,
+    "Mobile Development": 2.316
+  }
+}