fixed

README.md

@@ -4,6 +4,24 @@ FastAPI service that rates a public GitHub user profile out of 100.
 
 The API accepts a GitHub username and returns a `rating_score`, developer level, language breakdown, contribution signals, streak data, and model metadata.
 
+## Folder Structure
+
+```text
+app/
+  main.py              FastAPI app entrypoint and static frontend serving
+  api/                 HTTP routes for health checks and profile analysis
+  schemas.py           Request and response models
+  service.py           Application service layer
+  ai/                  Embedding and scoring logic
+  clients/             GitHub API clients and queries
+  core/                Settings and logging
+  graph/               Active analysis workflow
+  static/index.html    Browser API console
+backend/               Legacy LangGraph prototype pipeline
+experiments/           Standalone prototype files
+scripts/               Developer utilities and CLI helpers
+```
+
 ## Token Behavior
 
 `GITHUB_TOKEN` is optional.
@@ -34,6 +52,12 @@ pip install -r requirements.txt
 uvicorn app.main:app --host 0.0.0.0 --port 8000
 ```
 
+Open the browser console UI:
+
+```text
+http://localhost:8000/
+```
+
 Request a rating:
 
 ```bash
@@ -48,7 +72,50 @@ curl -X POST http://localhost:8000/analyze \
 docker compose up --build -d
 ```
 
-The API will be available on `http://localhost:${APP_PORT:-8000}`.
+The API and `index.html` console will be available on `http://localhost:${APP_PORT:-8000}`.
+
+Useful deployment commands:
+
+```bash
+docker compose ps
+docker compose logs -f api
+docker compose down
+```
+
+For a cloud VM, install Docker, clone the repo, create `.env`, then run the same `docker compose up --build -d` command. If you deploy behind a domain or reverse proxy, forward public traffic to container port `8000`.
+
+## Use The API In `index.html`
+
+The frontend at `app/static/index.html` calls the API with `fetch`:
+
+```html
+<script>
+async function analyze(username) {
+  const response = await fetch("/analyze", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({ username })
+  });
+
+  if (!response.ok) {
+    const error = await response.json();
+    throw new Error(error.detail || "Analyze request failed");
+  }
+
+  return response.json();
+}
+</script>
+```
+
+Use a full base URL only when the HTML is hosted somewhere else:
+
+```js
+fetch("https://your-domain.com/analyze", {
+  method: "POST",
+  headers: { "Content-Type": "application/json" },
+  body: JSON.stringify({ username: "octocat" })
+});
+```
 
 ## Response Fields
 

app/api/__init__.py

@@ -0,0 +1 @@
+"""API route package."""

app/api/routes.py

@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+from fastapi import APIRouter, HTTPException
+
+from app.clients.github_graphql import GitHubAPIError
+from app.schemas import AnalyzeRequest, AnalyzeResponse
+from app.service import AnalyzerService
+
+router = APIRouter()
+service: AnalyzerService | None = None
+
+
+def get_service() -> AnalyzerService:
+    global service
+    if service is None:
+        service = AnalyzerService()
+    return service
+
+
+@router.get("/health")
+async def health() -> dict[str, str]:
+    return {"status": "ok"}
+
+
+@router.post("/analyze", response_model=AnalyzeResponse)
+async def analyze(payload: AnalyzeRequest) -> AnalyzeResponse:
+    try:
+        return await get_service().analyze(payload.username)
+    except GitHubAPIError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=str(exc)) from exc

app/main.py

@@ -1,36 +1,24 @@
 from __future__ import annotations
 
-from fastapi import FastAPI, HTTPException
+from pathlib import Path
 
-from app.clients.github_graphql import GitHubAPIError
+from fastapi import FastAPI
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+
+from app.api.routes import router
 from app.core.config import settings
 from app.core.logging import setup_logging
-from app.schemas import AnalyzeRequest, AnalyzeResponse
-from app.service import AnalyzerService
 
 setup_logging()
 
 app = FastAPI(title=settings.app_name, version=settings.app_version)
-service: AnalyzerService | None = None
-
-
-def get_service() -> AnalyzerService:
-    global service
-    if service is None:
-        service = AnalyzerService()
-    return service
-
+STATIC_DIR = Path(__file__).resolve().parent / "static"
 
-@app.get("/health")
-async def health() -> dict[str, str]:
-    return {"status": "ok"}
+app.include_router(router)
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
 
 
-@app.post("/analyze", response_model=AnalyzeResponse)
-async def analyze(payload: AnalyzeRequest) -> AnalyzeResponse:
-    try:
-        return await get_service().analyze(payload.username)
-    except GitHubAPIError as exc:
-        raise HTTPException(status_code=400, detail=str(exc)) from exc
-    except Exception as exc:
-        raise HTTPException(status_code=500, detail=str(exc)) from exc
+@app.get("/")
+async def index() -> FileResponse:
+    return FileResponse(STATIC_DIR / "index.html")

app/static/index.html

@@ -0,0 +1,486 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>GitHub Profile AI Reviewer</title>
+  <style>
+    :root {
+      color-scheme: dark;
+      --bg: #0b0f13;
+      --panel: #121820;
+      --panel-soft: #18212b;
+      --line: #263241;
+      --text: #eef3f8;
+      --muted: #93a4b7;
+      --accent: #3fbf8f;
+      --accent-strong: #54d6a5;
+      --warn: #f7c56b;
+      --danger: #ff7b7b;
+    }
+
+    * {
+      box-sizing: border-box;
+    }
+
+    body {
+      margin: 0;
+      min-height: 100vh;
+      background: var(--bg);
+      color: var(--text);
+      font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+    }
+
+    header {
+      border-bottom: 1px solid var(--line);
+      background: rgba(11, 15, 19, 0.94);
+      position: sticky;
+      top: 0;
+      z-index: 10;
+      backdrop-filter: blur(12px);
+    }
+
+    .nav {
+      align-items: center;
+      display: flex;
+      gap: 18px;
+      justify-content: space-between;
+      margin: 0 auto;
+      max-width: 1120px;
+      padding: 16px 20px;
+    }
+
+    .brand {
+      align-items: center;
+      display: flex;
+      gap: 10px;
+      font-weight: 700;
+    }
+
+    .mark {
+      align-items: center;
+      aspect-ratio: 1;
+      background: var(--accent);
+      border-radius: 6px;
+      color: #06110d;
+      display: inline-flex;
+      font-weight: 900;
+      justify-content: center;
+      width: 32px;
+    }
+
+    .pill {
+      border: 1px solid var(--line);
+      border-radius: 999px;
+      color: var(--muted);
+      font-size: 13px;
+      padding: 7px 12px;
+      white-space: nowrap;
+    }
+
+    main {
+      margin: 0 auto;
+      max-width: 1120px;
+      padding: 32px 20px 56px;
+    }
+
+    .workspace {
+      display: grid;
+      gap: 20px;
+      grid-template-columns: minmax(0, 1fr) 360px;
+      align-items: start;
+    }
+
+    .hero {
+      padding: 14px 0 22px;
+    }
+
+    h1 {
+      font-size: clamp(34px, 6vw, 62px);
+      letter-spacing: 0;
+      line-height: 1.02;
+      margin: 0 0 16px;
+      max-width: 780px;
+    }
+
+    .lead {
+      color: var(--muted);
+      font-size: 18px;
+      line-height: 1.6;
+      margin: 0;
+      max-width: 720px;
+    }
+
+    .panel {
+      background: var(--panel);
+      border: 1px solid var(--line);
+      border-radius: 8px;
+      padding: 18px;
+    }
+
+    .analyzer {
+      display: grid;
+      gap: 16px;
+    }
+
+    label {
+      color: var(--muted);
+      display: block;
+      font-size: 13px;
+      font-weight: 700;
+      margin-bottom: 8px;
+      text-transform: uppercase;
+    }
+
+    .input-row {
+      display: grid;
+      gap: 10px;
+      grid-template-columns: minmax(0, 1fr) auto;
+    }
+
+    input {
+      background: #0e141a;
+      border: 1px solid var(--line);
+      border-radius: 6px;
+      color: var(--text);
+      font: inherit;
+      min-height: 46px;
+      outline: none;
+      padding: 0 13px;
+      width: 100%;
+    }
+
+    input:focus {
+      border-color: var(--accent);
+      box-shadow: 0 0 0 3px rgba(63, 191, 143, 0.16);
+    }
+
+    button {
+      background: var(--accent);
+      border: 0;
+      border-radius: 6px;
+      color: #05110d;
+      cursor: pointer;
+      font: inherit;
+      font-weight: 800;
+      min-height: 46px;
+      padding: 0 18px;
+      white-space: nowrap;
+    }
+
+    button:disabled {
+      cursor: wait;
+      opacity: 0.62;
+    }
+
+    .result-grid {
+      display: grid;
+      gap: 12px;
+      grid-template-columns: repeat(3, minmax(0, 1fr));
+    }
+
+    .metric {
+      background: var(--panel-soft);
+      border: 1px solid var(--line);
+      border-radius: 8px;
+      min-height: 92px;
+      padding: 14px;
+    }
+
+    .metric span {
+      color: var(--muted);
+      display: block;
+      font-size: 12px;
+      font-weight: 700;
+      text-transform: uppercase;
+    }
+
+    .metric strong {
+      display: block;
+      font-size: 30px;
+      line-height: 1;
+      margin-top: 10px;
+      overflow-wrap: anywhere;
+    }
+
+    .score strong {
+      color: var(--accent-strong);
+      font-size: 48px;
+    }
+
+    .status {
+      color: var(--muted);
+      min-height: 22px;
+    }
+
+    .error {
+      color: var(--danger);
+    }
+
+    .language-list {
+      display: flex;
+      flex-wrap: wrap;
+      gap: 8px;
+      margin-top: 4px;
+    }
+
+    .language-list span {
+      background: #0e141a;
+      border: 1px solid var(--line);
+      border-radius: 999px;
+      color: var(--muted);
+      font-size: 13px;
+      padding: 7px 10px;
+    }
+
+    pre {
+      background: #080c10;
+      border: 1px solid var(--line);
+      border-radius: 8px;
+      color: #b7c7d8;
+      font-size: 13px;
+      line-height: 1.55;
+      margin: 0;
+      overflow-x: auto;
+      padding: 14px;
+    }
+
+    .side {
+      display: grid;
+      gap: 20px;
+    }
+
+    .side h2,
+    .panel h2 {
+      font-size: 16px;
+      margin: 0 0 12px;
+    }
+
+    .api-base {
+      margin-bottom: 14px;
+    }
+
+    .small {
+      color: var(--muted);
+      font-size: 13px;
+      line-height: 1.5;
+      margin: 10px 0 0;
+    }
+
+    @media (max-width: 860px) {
+      .workspace {
+        grid-template-columns: 1fr;
+      }
+
+      .result-grid {
+        grid-template-columns: 1fr;
+      }
+    }
+
+    @media (max-width: 560px) {
+      .nav {
+        align-items: flex-start;
+        flex-direction: column;
+      }
+
+      .input-row {
+        grid-template-columns: 1fr;
+      }
+
+      button {
+        width: 100%;
+      }
+    }
+  </style>
+</head>
+<body>
+  <header>
+    <div class="nav">
+      <div class="brand"><span class="mark">G</span> GitHub Profile AI Reviewer</div>
+      <div class="pill" id="health-pill">API status: checking</div>
+    </div>
+  </header>
+
+  <main>
+    <section class="hero">
+      <h1>Rate any public GitHub profile out of 100.</h1>
+      <p class="lead">Submit a username and the API returns a developer rating, level, contribution signals, language mix, and scoring metadata. A server token is optional.</p>
+    </section>
+
+    <section class="workspace">
+      <div class="panel analyzer">
+        <form id="analyze-form">
+          <label for="username">GitHub username</label>
+          <div class="input-row">
+            <input id="username" name="username" value="octocat" autocomplete="off" maxlength="39" pattern="[A-Za-z0-9-]+" required>
+            <button id="submit-button" type="submit">Analyze</button>
+          </div>
+        </form>
+
+        <div class="status" id="status">Ready.</div>
+
+        <div class="result-grid">
+          <div class="metric score">
+            <span>Rating</span>
+            <strong id="rating">--</strong>
+          </div>
+          <div class="metric">
+            <span>Level</span>
+            <strong id="level">--</strong>
+          </div>
+          <div class="metric">
+            <span>Language</span>
+            <strong id="language">--</strong>
+          </div>
+        </div>
+
+        <div class="panel">
+          <h2>Language Breakdown</h2>
+          <div class="language-list" id="language-breakdown"></div>
+        </div>
+
+        <div class="panel">
+          <h2>Raw API Response</h2>
+          <pre id="response">{}</pre>
+        </div>
+      </div>
+
+      <aside class="side">
+        <div class="panel">
+          <h2>API Base</h2>
+          <input class="api-base" id="api-base" value="">
+          <p class="small">Leave this as the current origin when this page is served by FastAPI. Use `http://localhost:8000` if opening the HTML file directly.</p>
+        </div>
+
+        <div class="panel">
+          <h2>Fetch Example</h2>
+          <pre id="fetch-example"></pre>
+        </div>
+
+        <div class="panel">
+          <h2>Endpoint</h2>
+          <pre>POST /analyze
+Content-Type: application/json
+
+{
+  "username": "octocat"
+}</pre>
+        </div>
+      </aside>
+    </section>
+  </main>
+
+  <script>
+    const form = document.getElementById("analyze-form");
+    const usernameInput = document.getElementById("username");
+    const apiBaseInput = document.getElementById("api-base");
+    const submitButton = document.getElementById("submit-button");
+    const statusText = document.getElementById("status");
+    const healthPill = document.getElementById("health-pill");
+    const responseBox = document.getElementById("response");
+    const fetchExample = document.getElementById("fetch-example");
+
+    const fields = {
+      rating: document.getElementById("rating"),
+      level: document.getElementById("level"),
+      language: document.getElementById("language"),
+      breakdown: document.getElementById("language-breakdown")
+    };
+
+    apiBaseInput.value = window.location.protocol === "file:" ? "http://localhost:8000" : window.location.origin;
+
+    function endpoint(path) {
+      return `${apiBaseInput.value.replace(/\/$/, "")}${path}`;
+    }
+
+    function setStatus(message, isError = false) {
+      statusText.textContent = message;
+      statusText.classList.toggle("error", isError);
+    }
+
+    function updateFetchExample() {
+      const username = usernameInput.value || "octocat";
+      fetchExample.textContent = `const response = await fetch("${endpoint("/analyze")}", {
+  method: "POST",
+  headers: { "Content-Type": "application/json" },
+  body: JSON.stringify({ username: "${username}" })
+});
+
+const profileRating = await response.json();`;
+    }
+
+    function renderResult(data) {
+      fields.rating.textContent = Number.isFinite(data.rating_score) ? `${data.rating_score}` : "--";
+      fields.level.textContent = data.developer_level || "--";
+      fields.language.textContent = data.strongest_language || "--";
+      fields.breakdown.innerHTML = "";
+
+      const entries = Object.entries(data.language_breakdown || {});
+      if (!entries.length) {
+        const empty = document.createElement("span");
+        empty.textContent = "No language data";
+        fields.breakdown.appendChild(empty);
+      }
+
+      for (const [language, percent] of entries) {
+        const item = document.createElement("span");
+        item.textContent = `${language}: ${percent}%`;
+        fields.breakdown.appendChild(item);
+      }
+
+      responseBox.textContent = JSON.stringify(data, null, 2);
+    }
+
+    async function checkHealth() {
+      try {
+        const response = await fetch(endpoint("/health"));
+        healthPill.textContent = response.ok ? "API status: online" : "API status: unavailable";
+      } catch {
+        healthPill.textContent = "API status: offline";
+      }
+    }
+
+    form.addEventListener("submit", async (event) => {
+      event.preventDefault();
+      const username = usernameInput.value.trim();
+      if (!username) {
+        setStatus("Enter a GitHub username.", true);
+        return;
+      }
+
+      submitButton.disabled = true;
+      setStatus(`Analyzing ${username}...`);
+      updateFetchExample();
+
+      try {
+        const response = await fetch(endpoint("/analyze"), {
+          method: "POST",
+          headers: { "Content-Type": "application/json" },
+          body: JSON.stringify({ username })
+        });
+
+        const data = await response.json();
+        if (!response.ok) {
+          throw new Error(data.detail || "Request failed.");
+        }
+
+        renderResult(data);
+        setStatus(`Finished ${username}.`);
+      } catch (error) {
+        setStatus(error.message, true);
+      } finally {
+        submitButton.disabled = false;
+      }
+    });
+
+    usernameInput.addEventListener("input", updateFetchExample);
+    apiBaseInput.addEventListener("input", () => {
+      updateFetchExample();
+      checkHealth();
+    });
+
+    updateFetchExample();
+    checkHealth();
+  </script>
+</body>
+</html>

prototype.py → experiments/prototype.py

@@ -1,35 +1,35 @@
-import torch
-from transformers import AutoTokenizer, AutoModel
-
-print("Loading CodeBERT model... Please wait.")
-
-model_name = "microsoft/codebert-base"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModel.from_pretrained(model_name)
-
-print("Model loaded successfully!")
-
-
-sample_code = """
-def calculate_experience(repo_data):
-    stars = repo_data.get('stars', 0)
-    commits = repo_data.get('commits', 0)
-    return (stars * 10) + commits
-"""
-
-print(f"\nAnalyzing Sample Code Snippet:\n{sample_code}")
-
-
-inputs = tokenizer(sample_code, return_tensors="pt", padding=True, truncation=True)
-
-# 4. Generating Embeddings
-with torch.no_grad():
-    outputs = model(**inputs)
-
-# 5. Extracting Vector (Last hidden state)
-embeddings = outputs.last_hidden_state
-
-print("\nAI Pipeline Success!")
-print(f"Vector Shape: {embeddings.shape}")
-print("Meaning: CodeBERT successfully converted your code into heavy AI numbers!")
-
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+print("Loading CodeBERT model... Please wait.")
+
+model_name = "microsoft/codebert-base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+print("Model loaded successfully!")
+
+
+sample_code = """
+def calculate_experience(repo_data):
+    stars = repo_data.get('stars', 0)
+    commits = repo_data.get('commits', 0)
+    return (stars * 10) + commits
+"""
+
+print(f"\nAnalyzing Sample Code Snippet:\n{sample_code}")
+
+
+inputs = tokenizer(sample_code, return_tensors="pt", padding=True, truncation=True)
+
+# 4. Generating Embeddings
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# 5. Extracting Vector (Last hidden state)
+embeddings = outputs.last_hidden_state
+
+print("\nAI Pipeline Success!")
+print(f"Vector Shape: {embeddings.shape}")
+print("Meaning: CodeBERT successfully converted your code into heavy AI numbers!")
+

index.html

@@ -1,360 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>GitHub Profile AI-Reviewer Docs</title>
-    <style>
-        body {
-            font-family: 'Segoe UI', -apple-system, BlinkMacSystemFont, Roboto, sans-serif;
-            background-color: #0b0e14;
-            color: #8b949e;
-            margin: 0;
-            padding: 0;
-            overflow-x: hidden;
-        }
-
-        /* Top Navigation Bar */
-        .navbar {
-            display: flex;
-            justify-content: space-between;
-            align-items: center;
-            padding: 20px 40px;
-            background-color: #0b0e14;
-            border-bottom: 1px solid #161b22;
-            position: sticky;
-            top: 0;
-            z-index: 100;
-        }
-        .logo {
-            font-weight: 700;
-            color: #58a6ff;
-            letter-spacing: 1px;
-            font-size: 1.1rem;
-            cursor: pointer;
-        }
-        .nav-links {
-            display: flex;
-            gap: 25px;
-        }
-        .nav-links a {
-            color: #8b949e;
-            text-decoration: none;
-            font-size: 0.85rem;
-            text-transform: uppercase;
-            font-weight: 600;
-            letter-spacing: 0.5px;
-            padding: 6px 12px;
-            border-radius: 4px;
-            transition: all 0.2s ease;
-        }
-        .nav-links a:hover, .nav-links a.active {
-            color: #f0f6fc;
-            background-color: #21262d;
-        }
-        .status-badge {
-            background-color: rgba(56, 139, 253, 0.1);
-            color: #58a6ff;
-            border: 1px solid rgba(56, 139, 253, 0.3);
-            padding: 4px 12px;
-            border-radius: 20px;
-            font-size: 0.8rem;
-            font-weight: bold;
-        }
-
-        /* Multi-Page Visibility Logic */
-        .page {
-            display: none; /* Sub pages hidden by default */
-            animation: fadeIn 0.4s ease forwards;
-        }
-        .page.active-page {
-            display: block; /* Only active page shows up */
-        }
-
-        @keyframes fadeIn {
-            from { opacity: 0; transform: translateY(10px); }
-            to { opacity: 1; transform: translateY(0); }
-        }
-
-        /* PAGE 1: HERO HOME STYLE */
-        .hero {
-            text-align: center;
-            padding: 120px 20px 80px 20px;
-            background: radial-gradient(circle at top, #161b22 0%, #0b0e14 70%);
-        }
-        .hero h1 {
-            font-size: 4.5rem;
-            font-weight: 900;
-            color: #58a6ff;
-            margin: 0;
-            line-height: 1.1;
-            letter-spacing: -1px;
-            text-transform: uppercase;
-        }
-        .hero h1 span {
-            color: #f0f6fc;
-            display: block;
-        }
-        .hero p {
-            max-width: 650px;
-            margin: 30px auto;
-            font-size: 1.15rem;
-            color: #8b949e;
-            line-height: 1.6;
-        }
-        .cta-group {
-            display: flex;
-            justify-content: center;
-            gap: 15px;
-            margin-top: 40px;
-        }
-        .btn-primary {
-            background-color: #388bfd;
-            color: #ffffff;
-            padding: 14px 32px;
-            border-radius: 6px;
-            font-weight: 600;
-            text-decoration: none;
-            display: inline-flex;
-            align-items: center;
-            gap: 8px;
-            box-shadow: 0 4px 14px rgba(56, 139, 253, 0.4);
-            cursor: pointer;
-        }
-        .btn-secondary {
-            background-color: transparent;
-            color: #f0f6fc;
-            border: 1px solid #30363d;
-            padding: 14px 32px;
-            border-radius: 6px;
-            font-weight: 600;
-            text-decoration: none;
-            cursor: pointer;
-        }
-
-        /* Stats Bar */
-        .stats-bar {
-            border-top: 1px solid #161b22;
-            border-bottom: 1px solid #161b22;
-            padding: 50px 20px;
-            background-color: #07090e;
-        }
-        .stats-grid {
-            max-width: 1000px;
-            margin: 0 auto;
-            display: flex;
-            justify-content: space-between;
-            text-align: center;
-        }
-        .stat-item h3 {
-            font-size: 2.5rem;
-            color: #388bfd;
-            margin: 0 0 5px 0;
-            font-weight: 700;
-        }
-        .stat-item p {
-            font-size: 0.75rem;
-            text-transform: uppercase;
-            letter-spacing: 1px;
-            color: #8b949e;
-            margin: 0;
-        }
-
-        /* PAGE 2 & PAGE 3: INNER PAGES STANDARD CONTENT */
-        .content-area {
-            max-width: 900px;
-            margin: 60px auto;
-            padding: 0 20px;
-        }
-        .content-area h2 {
-            color: #f0f6fc;
-            font-size: 2rem;
-            border-bottom: 1px solid #30363d;
-            padding-bottom: 12px;
-        }
-        .grid {
-            display: grid;
-            gap: 20px;
-            margin-top: 30px;
-        }
-        .card {
-            padding: 25px;
-            background-color: #12161f;
-            border-radius: 6px;
-            border: 1px solid #21262d;
-        }
-        .card h4 {
-            color: #58a6ff;
-            margin: 0 0 10px 0;
-            font-size: 1.2rem;
-        }
-        .card p {
-            margin: 0;
-            line-height: 1.5;
-        }
-        pre {
-            background-color: #12161f;
-            padding: 20px;
-            border-radius: 6px;
-            border: 1px solid #21262d;
-            color: #ff7b72;
-            overflow-x: auto;
-            font-family: monospace;
-            font-size: 0.95rem;
-        }
-        footer {
-            text-align: center;
-            margin-top: 100px;
-            color: #8b949e;
-            font-size: 0.85rem;
-            border-top: 1px solid #161b22;
-            padding: 30px 20px;
-        }
-    </style>
-</head>
-<body>
-
-    <!-- Header Navigation Bar -->
-    <div class="navbar">
-        <div class="logo" onclick="navigateTo('home')">AI-REVIEWER v1.0</div>
-        <div class="nav-links">
-            <a href="#home" id="nav-home" class="active" onclick="navigateTo('home')">Home</a>
-            <a href="#architecture" id="nav-architecture" onclick="navigateTo('architecture')">Architecture</a>
-            <a href="#training-guide" id="nav-training-guide" onclick="navigateTo('training-guide')">Training Guide</a>
-        </div>
-        <div class="status-badge">● PIPELINE LIVE</div>
-    </div>
-
-
-    <!-- ================= PAGE 1: HOME PAGE ================= -->
-    <div id="page-home" class="page active-page">
-        <div class="hero">
-            <h1>ANALYZE.<span>SCORE SMART.</span></h1>
-            <p>The AI-Reviewer uses an orchestrated LangGraph agentic model framework to map public developer profiles, compute contextual CodeBERT embeddings, and evaluate comprehensive syntax engineering metrics seamlessly.</p>
-            
-            <div class="cta-group">
-                <!-- Clicking these opens separate virtual inner pages! -->
-                <button onclick="navigateTo('training-guide')" class="btn-primary">Open Analyzer →</button>
-                <button onclick="navigateTo('architecture')" class="btn-secondary">How It Works</button>
-            </div>
-        </div>
-
-        <div class="stats-bar">
-            <div class="stats-grid">
-                <div class="stat-item">
-                    <h3>5+</h3>
-                    <p>Execution Nodes</p>
-                </div>
-                <div class="stat-item">
-                    <h3>768</h3>
-                    <p>Vector Array Dims</p>
-                </div>
-                <div class="stat-item">
-                    <h3>100%</h3>
-                    <p>State Tracking</p>
-                </div>
-                <div class="stat-item">
-                    <h3>PyTorch</h3>
-                    <p>Fine-Tuning Core</p>
-                </div>
-            </div>
-        </div>
-    </div>
-
-
-    <!-- ================= PAGE 2: ARCHITECTURE (HOW IT WORKS) ================= -->
-    <div id="page-architecture" class="page">
-        <div class="content-area">
-            <h2>System Architecture Matrix</h2>
-            <p>Our core model layers are segregated into specialized processing nodes orchestrated smoothly via a LangGraph state container loop:</p>
-            
-            <div class="grid">
-                <div class="card">
-                    <h4>1. GitHub Metric Extractor Node</h4>
-                    <p>Triggers advanced GraphQL queries to pull authentic repository data, commitments, and language footprints securely from live server channels.</p>
-                </div>
-                <div class="card">
-                    <h4>2. CodeBERT Vectorizer Node</h4>
-                    <p>Utilizes HuggingFace CodeBERT transformers to map complex source code text sequences into high-density 768-dimensional AI vector spaces.</p>
-                </div>
-                <div class="card">
-                    <h4>3. PyTorch Fine-Tuning Scoring Node</h4>
-                    <p>Runs neural network backpropagation sequences to update layer configurations and calculate authentic capabilities metrics dynamically.</p>
-                </div>
-                <div class="card">
-                    <h4>4. StarCoder Quality Estimator Node</h4>
-                    <p>Evaluates syntactic deployment blocks, documentation maturity limits, and structural architecture patterns.</p>
-                </div>
-                <div class="card">
-                    <h4>5. Cosine Vector Similarity Search Node</h4>
-                    <p>Compares user embedding matrices against regional vector database spaces to output instant "Find developers like X" recomendation values.</p>
-                </div>
-            </div>
-        </div>
-    </div>
-
-
-    <!-- ================= PAGE 3: TRAINING GUIDE (RUN TIME) ================= -->
-    <div id="page-training-guide" class="page">
-        <div class="content-area">
-            <h2>Local Compilation & Model Status</h2>
-            <p>Follow this deployment layout blueprint to execute and initialize the unified master orchestration pipeline layout on local development nodes:</p>
-            
-            <h3>Environmental Core Elements</h3>
-            <pre>
-# Install required infrastructure packages
-pip install langgraph torch transformers python-dotenv</pre>
-
-            <h3>Pipeline Launch Target Execution</h3>
-            <pre>
-# Boot central pipeline execution flow matrix
-python backend/graph/pipeline.py</pre>
-
-            <div class="card" style="margin-top: 30px; border-left: 4px solid #388bfd;">
-                <h4 style="margin: 0 0 5px 0;">Model Execution Validation</h4>
-                <p>Upon compilation, the terminal will dynamically display step-by-step neural loop weights optimization, structural fine-tuning epochs loss dropping to 0.0000, and comprehensive matrix comparison outputs automatically.</p>
-            </div>
-        </div>
-    </div>
-
-
-    <!-- Footer Component -->
-    <footer>
-        GitHub Profile AI-Reviewer Documentation • Presented via Adaptive Dynamic Engine
-    </footer>
-
-
-    <!-- Dynamic Multi-Page Router Script -->
-    <script>
-        function navigateTo(pageId) {
-            // 1. Hide all virtual page layers
-            const pages = document.querySelectorAll('.page');
-            pages.forEach(page => page.classList.remove('active-page'));
-
-            // 2. Remove active state colors from all menu items
-            const navLinks = document.querySelectorAll('.nav-links a');
-            navLinks.forEach(link => link.classList.remove('active'));
-
-            // 3. Show the targeted page layer
-            document.getElementById('page-' + pageId).classList.add('active-page');
-
-            // 4. Highlight the matching navigation menu option
-            const targetNav = document.getElementById('nav-' + pageId);
-            if(targetNav) {
-                targetNav.classList.add('active');
-            }
-
-            // 5. Smooth scroll window back to the top of the new page layout
-            window.scrollTo({ top: 0, behavior: 'smooth' });
-        }
-
-        // Handle browser fallback entry checks based on URL hashes
-        window.addEventListener('DOMContentLoaded', () => {
-            const currentHash = window.location.hash.replace('#', '');
-            if(['home', 'architecture', 'training-guide'].includes(currentHash)) {
-                navigateTo(currentHash);
-            }
-        });
-    </script>
-</body>
-</html>

main.py → scripts/analyze_user.py

@@ -3,6 +3,12 @@ from __future__ import annotations
 import argparse
 import asyncio
 import json
+import sys
+from pathlib import Path
+
+ROOT_DIR = Path(__file__).resolve().parents[1]
+if str(ROOT_DIR) not in sys.path:
+    sys.path.insert(0, str(ROOT_DIR))
 
 from app.service import AnalyzerService