| | """
|
| | ╔══════════════════════════════════════════════════════════════════════════╗
|
| | ║ MoodLens · Sentiment Intelligence Platform · v2.0 ║
|
| | ╚══════════════════════════════════════════════════════════════════════════╝
|
| | Run: uvicorn app:app --host 0.0.0.0 --port 8000 --reload
|
| | """
|
| |
|
| | import sys, time, logging
|
| | from pathlib import Path
|
| | from datetime import datetime
|
| | from contextlib import asynccontextmanager
|
| |
|
| | from fastapi import FastAPI, Request
|
| | from fastapi.responses import JSONResponse, HTMLResponse, FileResponse
|
| | from fastapi.middleware.cors import CORSMiddleware
|
| | from fastapi.responses import JSONResponse, HTMLResponse
|
| | from fastapi.openapi.docs import get_swagger_ui_html
|
| | from pydantic import BaseModel
|
| |
|
| |
|
| |
|
| |
|
| | ROOT = Path(__file__).resolve().parent.parent
|
| | sys.path.append(str(ROOT / "python"))
|
| |
|
| |
|
| |
|
| |
|
| | from roberta_predict import predict, compare_all_models
|
| |
|
| |
|
| |
|
| |
|
| | RESET = "\033[0m"; BOLD = "\033[1m"; DIM = "\033[2m"
|
| | CYAN = "\033[36m"; GREEN = "\033[32m"; YELLOW = "\033[33m"
|
| | RED = "\033[31m"
|
| |
|
| | class _Fmt(logging.Formatter):
|
| | C = {"DEBUG": DIM, "INFO": GREEN, "WARNING": YELLOW, "ERROR": RED, "CRITICAL": f"{BOLD}{RED}"}
|
| | def format(self, r):
|
| | ts = datetime.now().strftime("%H:%M:%S")
|
| | lc = self.C.get(r.levelname, "")
|
| | return f"{DIM}{ts}{RESET} {lc}{r.levelname:<8}{RESET} {CYAN}{r.name}{RESET} {r.getMessage()}"
|
| |
|
| | _h = logging.StreamHandler(); _h.setFormatter(_Fmt())
|
| | logging.root.handlers = [_h]; logging.root.setLevel(logging.INFO)
|
| | log = logging.getLogger("moodlens")
|
| |
|
| |
|
| |
|
| |
|
| | @asynccontextmanager
|
| | async def lifespan(app: FastAPI):
|
| | print(f"""
|
| | {YELLOW}{BOLD}
|
| | ╔═══════════════════════════════════════════════════════╗
|
| | ║ ║
|
| | ║ ███╗ ███╗ ██████╗ ██████╗ ██████╗ ║
|
| | ║ ████╗ ████║██╔═══██╗██╔═══██╗██╔══██╗ ║
|
| | ║ ██╔████╔██║██║ ██║██║ ██║██║ ██║ ║
|
| | ║ ██║╚██╔╝██║██║ ██║██║ ██║██║ ██║ ║
|
| | ║ ██║ ╚═╝ ██║╚██████╔╝╚██████╔╝██████╔╝ ║
|
| | ║ ╚═╝ ╚═╝ ╚═════╝ ╚═════╝ ╚═════╝ LENS v2.0 ║
|
| | ║ ║
|
| | ╚═══════════════════════════════════════════════════════╝
|
| | {RESET}
|
| | {YELLOW}◆{RESET} Splash → http://127.0.0.1:{YELLOW}8000{RESET}
|
| | {YELLOW}◆{RESET} Swagger → http://127.0.0.1:{YELLOW}8000/docs{RESET}
|
| | {YELLOW}◆{RESET} Health → http://127.0.0.1:{YELLOW}8000/health{RESET}
|
| |
|
| | {GREEN}✓{RESET} ML Engine → {GREEN}{BOLD}READY{RESET}
|
| | {GREEN}✓{RESET} Dataset → Zomato Reviews Corpus
|
| | {GREEN}✓{RESET} Models → RoBERTa · DistilRoBERTa · BERT · ALBERT
|
| | """)
|
| | yield
|
| | print(f"\n {YELLOW}◆{RESET} MoodLens offline {DIM}· bye 👋{RESET}\n")
|
| |
|
| |
|
| |
|
| |
|
| | app = FastAPI(
|
| | title = "MoodLens · Sentiment Intelligence API",
|
| | description = """
|
| | ## MoodLens — Multi-Model NLP Sentiment Engine
|
| |
|
| | Enterprise-grade sentiment analysis powered by four transformer models,
|
| | fine-tuned on the **Zomato Reviews** corpus.
|
| |
|
| | ### Models
|
| | | Model | Hugging Face ID | Strength |
|
| | |---|---|---|
|
| | | **RoBERTa** | `cardiffnlp/twitter-roberta-base-sentiment-latest` | General sentiment · Default |
|
| | | **DistilRoBERTa** | `mrm8488/distilroberta-finetuned-...` | Faster · Financial/review domain |
|
| | | **BERT** | `nlptown/bert-base-multilingual-uncased-sentiment` | Multilingual · 5-star scale |
|
| | | **ALBERT** | `textattack/albert-base-v2-yelp-polarity` | Efficient · Yelp polarity |
|
| |
|
| | ### Quick Start
|
| | ```bash
|
| | curl -X POST http://localhost:8000/predict \\
|
| | -H "Content-Type: application/json" \\
|
| | -d '{"text": "Best biryani I have ever had!"}'
|
| | ```
|
| | """,
|
| | version = "2.0.0",
|
| | docs_url = None,
|
| | redoc_url = "/redoc",
|
| | lifespan = lifespan,
|
| | openapi_tags= [
|
| | {"name": "Inference", "description": "Sentiment prediction endpoints."},
|
| | {"name": "System", "description": "Health and diagnostics."},
|
| | ],
|
| | )
|
| |
|
| | app.add_middleware(
|
| | CORSMiddleware,
|
| | allow_origins=["*"], allow_credentials=True,
|
| | allow_methods=["*"], allow_headers=["*"],
|
| | )
|
| |
|
| | @app.middleware("http")
|
| | async def _timer(req: Request, call_next):
|
| | t0 = time.perf_counter()
|
| | res = await call_next(req)
|
| | ms = (time.perf_counter() - t0) * 1000
|
| | c = GREEN if res.status_code < 400 else YELLOW if res.status_code < 500 else RED
|
| | log.info(f"{c}{req.method:<6}{RESET} {req.url.path:<22} {c}{res.status_code}{RESET} {DIM}{ms:.1f}ms{RESET}")
|
| | res.headers["X-Response-Time"] = f"{ms:.2f}ms"
|
| | return res
|
| |
|
| |
|
| |
|
| |
|
| | class TextInput(BaseModel):
|
| | text: str
|
| |
|
| |
|
| |
|
| |
|
| |
|
| | from fastapi.responses import FileResponse
|
| |
|
| | @app.get("/favicon.ico", include_in_schema=False)
|
| | async def favicon():
|
| | return FileResponse(Path(__file__).parent / "favicon.ico")
|
| |
|
| |
|
| | @app.get("/", response_class=HTMLResponse, include_in_schema=False)
|
| | def root():
|
| | return HTMLResponse(_splash())
|
| |
|
| | @app.get("/docs", response_class=HTMLResponse, include_in_schema=False)
|
| | def dark_docs():
|
| | return get_swagger_ui_html(
|
| | openapi_url = app.openapi_url,
|
| | title = "MoodLens · API Docs",
|
| | swagger_js_url = "https://cdn.jsdelivr.net/npm/swagger-ui-dist@5/swagger-ui-bundle.js",
|
| | swagger_css_url = "https://cdn.jsdelivr.net/npm/swagger-ui-dist@5/swagger-ui.css",
|
| | swagger_favicon_url = "data:,",
|
| | swagger_ui_parameters={
|
| | "syntaxHighlight.theme" : "monokai",
|
| | "tryItOutEnabled" : True,
|
| | "displayRequestDuration" : True,
|
| | "defaultModelsExpandDepth": -1,
|
| | },
|
| | )
|
| |
|
| | @app.get(
|
| | "/health",
|
| | summary="Server & ML Health Check",
|
| | description="""
|
| | Returns real-time status of the API server and ML engine.
|
| |
|
| | **Use this endpoint to:**
|
| | - ✅ Verify all 4 transformer models are loaded and ready
|
| | - ✅ Confirm server is reachable before sending inference requests
|
| | - ✅ Monitor uptime in CI/CD pipelines or dashboards
|
| | - ✅ Check UTC timestamp for server clock sync
|
| | """,
|
| | tags=["System"],
|
| | )
|
| | def health():
|
| | return {
|
| | "status" : "ok",
|
| | "version" : "2.0.0",
|
| | "timestamp": datetime.utcnow().isoformat() + "Z",
|
| | "models" : ["roberta", "distilroberta", "bert", "albert"],
|
| | "dataset" : "Zomato Reviews — Food & Dining Corpus",
|
| | }
|
| |
|
| | @app.post(
|
| | "/predict",
|
| | summary="Predict Sentiment (RoBERTa)",
|
| | description="""
|
| | Runs the **default RoBERTa model** on your input text.
|
| |
|
| | ### How it works
|
| | 1. Text tokenised and truncated to **512 tokens**
|
| | 2. RoBERTa runs a single forward pass
|
| | 3. Softmax scores mapped → **Positive / Neutral / Negative**
|
| | 4. Highest-probability class returned as `prediction`
|
| |
|
| | ### Response fields
|
| | | Field | Type | Description |
|
| | |---|---|---|
|
| | | `prediction` | string | `Positive`, `Neutral`, or `Negative` |
|
| | | `confidence` | float | Winning class score (0.0 – 1.0) |
|
| | | `positive` | float | Raw probability — Positive class |
|
| | | `neutral` | float | Raw probability — Neutral class |
|
| | | `negative` | float | Raw probability — Negative class |
|
| | """,
|
| | tags=["Inference"],
|
| | )
|
| | def get_prediction(data: TextInput):
|
| | label, probs = predict(data.text)
|
| | return {
|
| | "prediction": label,
|
| | "confidence": float(max(probs)),
|
| | "negative" : float(probs[0]),
|
| | "neutral" : float(probs[1]),
|
| | "positive" : float(probs[2]),
|
| | }
|
| |
|
| | @app.post(
|
| | "/compare",
|
| | summary="Compare All 4 Models",
|
| | description="""
|
| | Runs **all four models** on the same input and returns results sorted by confidence.
|
| |
|
| | ### Models compared
|
| | | Model | Strength |
|
| | |---|---|
|
| | | **RoBERTa** | General-purpose · highest accuracy |
|
| | | **DistilRoBERTa** | 40% faster · financial/review domain |
|
| | | **BERT** | Multilingual · 5-star scale |
|
| | | **ALBERT** | Lightweight · Yelp polarity |
|
| |
|
| | ### How it works
|
| | 1. All 4 models process the input **independently**
|
| | 2. Raw labels normalised → `Positive / Neutral / Negative`
|
| | 3. Results **sorted by confidence** — best model first
|
| | 4. Use `/predict` for speed · `/compare` for cross-validation
|
| | """,
|
| | tags=["Inference"],
|
| | )
|
| | def compare_models(data: TextInput):
|
| | result = compare_all_models(data.text)
|
| | return {"comparison": result}
|
| |
|
| | @app.exception_handler(404)
|
| | async def not_found(_, __):
|
| | return JSONResponse(status_code=404, content={
|
| | "error" : "Route not found",
|
| | "routes": {"GET": ["/", "/health", "/docs", "/redoc"], "POST": ["/predict", "/compare"]},
|
| | "docs" : "http://127.0.0.1:8000/docs",
|
| | })
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| | def _splash() -> str:
|
| | return r"""<!DOCTYPE html>
|
| | <html lang="en" data-theme="dark">
|
| | <head>
|
| | <meta charset="UTF-8"/>
|
| | <meta name="viewport" content="width=device-width,initial-scale=1"/>
|
| | <title>MoodLens · API</title>
|
| | <link rel="icon" href="favicon.ico" />
|
| | <link rel="preconnect" href="https://fonts.googleapis.com"/>
|
| | <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:ital,wght@0,300;0,400;0,500;0,600;0,700;0,800;1,400&display=swap" rel="stylesheet"/>
|
| | <style>
|
| | /* ════════════════════════════════════════════════════════════
|
| | TOKENS
|
| | dark → yellow #FFD449 accent-red #E23744 (Zomato palette)
|
| | light → navy #09091A blue #276EF1 (Uber palette)
|
| | ════════════════════════════════════════════════════════════ */
|
| | [data-theme="dark"]{
|
| | --bg: #07080E;
|
| | --bg2: #0C0E18;
|
| | --card: #10131F;
|
| | --card2: #151929;
|
| | --b1: #1C2038;
|
| | --b2: #242A45;
|
| | --fg: #E4EAF8;
|
| | --f2: #7A90B8;
|
| | --f3: #3D4F70;
|
| | --nav: rgba(7,8,14,.88);
|
| |
|
| | /* Zomato-inspired yellow + red accent */
|
| | --accent: #FFD449;
|
| | --accent2: #E23744;
|
| | --accent3: #FF6B6B;
|
| | --aglow: rgba(255,212,73,.14);
|
| | --aglow2: rgba(226,55,68,.10);
|
| | --aborder: rgba(255,212,73,.25);
|
| | --aborder2:rgba(226,55,68,.22);
|
| | --atext: #07080E; /* text on accent bg */
|
| |
|
| | --post-bg: rgba(255,212,73,.10);
|
| | --post-col: #FFD449;
|
| | --post-bd: rgba(255,212,73,.22);
|
| | --get-bg: rgba(226,55,68,.10);
|
| | --get-col: #E23744;
|
| | --get-bd: rgba(226,55,68,.22);
|
| |
|
| | --dot-col: #E23744;
|
| | --sh: 0 28px 70px rgba(0,0,0,.60);
|
| | }
|
| |
|
| | [data-theme="light"]{
|
| | --bg: #F0F2F8;
|
| | --bg2: #E4E8F4;
|
| | --card: #FFFFFF;
|
| | --card2: #EEF1FA;
|
| | --b1: #C8D0E8;
|
| | --b2: #B0BCE0;
|
| | --fg: #09091A; /* Uber navy */
|
| | --f2: #3A4A6A;
|
| | --f3: #8898B8;
|
| | --nav: rgba(240,242,248,.92);
|
| |
|
| | /* Uber-inspired navy + blue */
|
| | --accent: #09091A; /* Uber black/navy */
|
| | --accent2: #276EF1; /* Uber blue */
|
| | --accent3: #1A56C4;
|
| | --aglow: rgba(9,9,26,.07);
|
| | --aglow2: rgba(39,110,241,.09);
|
| | --aborder: rgba(9,9,26,.20);
|
| | --aborder2:rgba(39,110,241,.25);
|
| | --atext: #FFFFFF; /* text on accent bg */
|
| |
|
| | --post-bg: rgba(39,110,241,.10);
|
| | --post-col: #276EF1;
|
| | --post-bd: rgba(39,110,241,.22);
|
| | --get-bg: rgba(9,9,26,.08);
|
| | --get-col: #09091A;
|
| | --get-bd: rgba(9,9,26,.18);
|
| |
|
| | --dot-col: #276EF1;
|
| | --sh: 0 16px 48px rgba(9,9,26,.12);
|
| | }
|
| |
|
| | /* ════ BASE ═════════════════════════════════════════════════ */
|
| | *{box-sizing:border-box;margin:0;padding:0;
|
| | transition:background .25s,color .25s,border-color .25s,opacity .25s;}
|
| | html{scroll-behavior:smooth;}
|
| | body{
|
| | background:var(--bg);color:var(--fg);
|
| | font-family:'JetBrains Mono',monospace;
|
| | font-size:14px;line-height:1.7;min-height:100vh;overflow-x:hidden;
|
| | }
|
| |
|
| | /* grid-line texture */
|
| | body::after{
|
| | content:'';position:fixed;inset:0;pointer-events:none;z-index:0;
|
| | background-image:
|
| | linear-gradient(var(--b1) 1px,transparent 1px),
|
| | linear-gradient(90deg,var(--b1) 1px,transparent 1px);
|
| | background-size:52px 52px;opacity:.15;
|
| | }
|
| | [data-theme="light"] body::after{opacity:.08;}
|
| |
|
| | /* ambient blobs */
|
| | .blob{position:fixed;border-radius:50%;filter:blur(150px);pointer-events:none;z-index:0;}
|
| | .b1{width:750px;height:750px;top:-280px;right:-180px;
|
| | background:radial-gradient(circle,var(--aglow),transparent 70%);
|
| | animation:bf 14s ease-in-out infinite;}
|
| | .b2{width:600px;height:600px;bottom:-220px;left:-180px;
|
| | background:radial-gradient(circle,var(--aglow2),transparent 70%);
|
| | animation:bf 18s ease-in-out infinite reverse;}
|
| | .b3{width:350px;height:350px;top:38%;left:38%;
|
| | background:radial-gradient(circle,rgba(39,110,241,.05),transparent 70%);
|
| | animation:bf 22s ease-in-out infinite 7s;}
|
| | @keyframes bf{
|
| | 0%,100%{transform:translate(0,0) scale(1);}
|
| | 33%{transform:translate(30px,-42px) scale(1.07);}
|
| | 66%{transform:translate(-20px,24px) scale(.93);}
|
| | }
|
| |
|
| | /* ════ NAV ══════════════════════════════════════════════════ */
|
| | nav{
|
| | position:fixed;top:0;left:0;right:0;z-index:200;
|
| | background:var(--nav);backdrop-filter:blur(24px);-webkit-backdrop-filter:blur(24px);
|
| | border-bottom:1px solid var(--b1);
|
| | display:flex;align-items:center;justify-content:space-between;
|
| | padding:0 40px;height:58px;gap:16px;
|
| | }
|
| | .nlogo{
|
| | font-size:17px;font-weight:800;letter-spacing:-1px;
|
| | text-decoration:none;display:flex;flex-direction:column;line-height:1.2;
|
| | }
|
| | .nlogo-main{
|
| | color:var(--accent);
|
| | text-shadow:none;
|
| | }
|
| | [data-theme="dark"] .nlogo-main{
|
| | text-shadow:0 0 28px rgba(255,212,73,.45);
|
| | }
|
| | .nlogo-tag{color:var(--f3);font-size:9px;font-weight:400;letter-spacing:2.5px;text-transform:uppercase;margin-top:1px;}
|
| | .nr{display:flex;align-items:center;gap:8px;}
|
| | .npill{
|
| | display:flex;align-items:center;gap:7px;padding:6px 14px;border-radius:7px;
|
| | border:1px solid var(--b2);background:var(--card2);color:var(--f2);
|
| | font-size:11px;font-family:'JetBrains Mono',monospace;
|
| | text-decoration:none;white-space:nowrap;cursor:pointer;letter-spacing:.3px;font-weight:500;
|
| | }
|
| | .npill:hover{border-color:var(--accent);color:var(--accent);}
|
| | .live-pill{
|
| | display:flex;align-items:center;gap:7px;padding:5px 13px;border-radius:7px;
|
| | border:1px solid var(--aborder2);background:var(--aglow2);
|
| | font-size:10px;color:var(--dot-col);letter-spacing:.5px;font-weight:500;
|
| | }
|
| | .tbtn{
|
| | width:38px;height:38px;border-radius:8px;border:1px solid var(--b2);
|
| | background:var(--card2);cursor:pointer;font-size:15px;
|
| | display:flex;align-items:center;justify-content:center;
|
| | }
|
| | .tbtn:hover{border-color:var(--accent);transform:rotate(20deg);}
|
| |
|
| | /* ════ HERO ════════════════════════════════════════════════ */
|
| | .hero{
|
| | position:relative;z-index:1;min-height:100vh;
|
| | display:flex;flex-direction:column;align-items:center;justify-content:center;
|
| | padding:80px 24px 60px;text-align:center;
|
| | }
|
| | .eyebrow{
|
| | display:inline-flex;align-items:center;gap:9px;padding:6px 18px;border-radius:7px;
|
| | border:1px solid var(--aborder);background:var(--aglow);
|
| | font-size:9px;color:var(--accent);letter-spacing:3.5px;text-transform:uppercase;
|
| | margin-bottom:40px;animation:fu .7s ease both;font-weight:500;
|
| | }
|
| | .ldot{width:6px;height:6px;border-radius:50%;background:var(--dot-col);
|
| | box-shadow:0 0 10px var(--dot-col);animation:lp 2s ease-in-out infinite;}
|
| | @keyframes lp{0%,100%{opacity:1;transform:scale(1);}50%{opacity:.3;transform:scale(1.9);}}
|
| |
|
| | .hero-pre{font-size:12px;color:var(--f3);letter-spacing:4px;text-transform:uppercase;
|
| | margin-bottom:14px;animation:fu .7s .05s ease both;font-weight:400;}
|
| | h1{font-size:clamp(58px,10vw,108px);font-weight:800;
|
| | line-height:.88;letter-spacing:-5px;margin-bottom:10px;animation:fu .7s .1s ease both;}
|
| | .h1a{display:block;color:var(--fg);}
|
| | .h1b{
|
| | display:block;color:var(--accent);
|
| | animation:flicker 9s ease-in-out infinite 2s;
|
| | }
|
| | [data-theme="dark"] .h1b{
|
| | text-shadow:0 0 80px rgba(255,212,73,.4),0 0 160px rgba(255,212,73,.12);
|
| | }
|
| | [data-theme="light"] .h1b{
|
| | text-shadow:0 2px 24px rgba(9,9,26,.15);
|
| | }
|
| | @keyframes flicker{
|
| | 0%,94%,100%{opacity:1;}
|
| | 95%{opacity:.7;}97%{opacity:1;}98%{opacity:.85;}99%{opacity:1;}
|
| | }
|
| |
|
| | .hsub{max-width:560px;font-size:12.5px;color:var(--f2);line-height:2;
|
| | margin:28px 0 40px;animation:fu .7s .2s ease both;font-weight:400;}
|
| | .hsub strong{color:var(--fg);font-weight:600;}
|
| |
|
| | /* CTA */
|
| | .hbtns{display:flex;gap:12px;flex-wrap:wrap;justify-content:center;animation:fu .7s .3s ease both;}
|
| | .bpri{
|
| | display:inline-flex;align-items:center;gap:9px;padding:13px 30px;border-radius:10px;
|
| | background:var(--accent);color:var(--atext);font-weight:700;font-size:12px;
|
| | text-decoration:none;border:none;cursor:pointer;letter-spacing:.5px;
|
| | box-shadow:0 8px 36px var(--aglow);
|
| | }
|
| | .bpri:hover{transform:translateY(-3px);box-shadow:0 16px 50px var(--aglow),0 4px 16px rgba(0,0,0,.3);}
|
| | .bout{
|
| | display:inline-flex;align-items:center;gap:8px;padding:12px 24px;border-radius:10px;
|
| | border:1px solid var(--b2);background:var(--card);color:var(--f2);
|
| | font-weight:500;font-size:12px;text-decoration:none;letter-spacing:.3px;
|
| | }
|
| | .bout:hover{border-color:var(--accent2);color:var(--accent2);transform:translateY(-3px);}
|
| |
|
| | /* stats */
|
| | .stats{
|
| | display:flex;margin-top:60px;flex-wrap:wrap;justify-content:center;
|
| | animation:fu .7s .4s ease both;
|
| | border:1px solid var(--b1);border-radius:14px;background:var(--card);overflow:hidden;
|
| | }
|
| | .stat{padding:20px 34px;text-align:center;border-right:1px solid var(--b1);}
|
| | .stat:last-child{border-right:none;}
|
| | .sv{font-size:34px;font-weight:800;letter-spacing:-2px;color:var(--accent);}
|
| | [data-theme="dark"] .sv{text-shadow:0 0 22px rgba(255,212,73,.28);}
|
| | [data-theme="light"] .sv{text-shadow:0 1px 12px rgba(9,9,26,.1);}
|
| | .sl{font-size:9px;color:var(--f3);letter-spacing:2px;text-transform:uppercase;margin-top:3px;}
|
| |
|
| | .online-bar{
|
| | display:inline-flex;align-items:center;gap:18px;
|
| | margin-top:26px;padding:10px 24px;border-radius:10px;
|
| | border:1px solid var(--b1);background:var(--card);
|
| | font-size:10px;color:var(--f3);letter-spacing:.8px;
|
| | animation:fu .7s .5s ease both;
|
| | }
|
| | .online-bar .on{color:var(--dot-col);font-weight:600;}
|
| | .sep{color:var(--b2);}
|
| |
|
| | /* ════ TERMINAL ═════════════════════════════════════════════ */
|
| | .terminal-wrap{position:relative;z-index:1;max-width:760px;margin:0 auto 120px;padding:0 24px;}
|
| | .terminal{
|
| | background:var(--bg2);border:1px solid var(--b2);border-radius:16px;
|
| | overflow:hidden;box-shadow:var(--sh);
|
| | }
|
| | .t-bar{display:flex;align-items:center;gap:8px;padding:12px 18px;
|
| | border-bottom:1px solid var(--b1);background:var(--card);}
|
| | .t-dot{width:12px;height:12px;border-radius:50%;}
|
| | .t-title{margin-left:8px;font-size:10px;color:var(--f3);letter-spacing:1.2px;text-transform:uppercase;}
|
| | .t-body{padding:22px 24px;font-size:12px;line-height:2.1;text-align:left;}
|
| | .t-prompt{color:var(--accent);flex-shrink:0;}
|
| | .t-line{display:flex;align-items:flex-start;gap:10px;margin-bottom:2px;}
|
| | .t-str{color:#A8E6A3;}
|
| | .t-out{color:var(--f2);margin-left:20px;}
|
| | .t-key{color:var(--accent2);}
|
| | [data-theme="light"] .t-key{color:var(--accent2);}
|
| | .t-num{color:var(--accent);}
|
| | .t-pos{color:#A8E6A3;}
|
| | .t-cmt{color:var(--f3);font-style:italic;}
|
| | .t-cursor{display:inline-block;width:8px;height:14px;background:var(--accent);
|
| | animation:blink 1s step-end infinite;vertical-align:middle;margin-left:2px;}
|
| | @keyframes blink{0%,100%{opacity:1;}50%{opacity:0;}}
|
| |
|
| | /* ════ CONTENT WRAPPER ══════════════════════════════════════ */
|
| | .wrap{position:relative;z-index:1;max-width:1120px;margin:0 auto;padding:0 28px 120px;}
|
| | .divider{height:1px;background:linear-gradient(90deg,transparent,var(--b2),transparent);margin-bottom:88px;}
|
| | .slabel{
|
| | font-size:9px;letter-spacing:4px;text-transform:uppercase;color:var(--accent);
|
| | margin-bottom:16px;display:flex;align-items:center;gap:14px;font-weight:600;
|
| | }
|
| | .slabel::after{content:'';flex:1;height:1px;background:var(--b1);}
|
| | .stitle{font-size:clamp(30px,4vw,48px);font-weight:800;letter-spacing:-2.5px;line-height:.95;margin-bottom:12px;}
|
| | .sdesc{color:var(--f2);font-size:12px;margin-bottom:56px;max-width:500px;line-height:1.9;font-weight:400;}
|
| |
|
| | /* ════ ENDPOINT CARDS ═══════════════════════════════════════ */
|
| | .epgrid{display:grid;grid-template-columns:repeat(auto-fit,minmax(480px,1fr));gap:14px;}
|
| | .ecard{
|
| | background:var(--card);border:1px solid var(--b1);border-radius:18px;
|
| | overflow:hidden;position:relative;
|
| | }
|
| | .ecard::before{
|
| | content:'';position:absolute;left:0;top:0;bottom:0;width:3px;
|
| | background:linear-gradient(180deg,var(--accent),var(--accent2));
|
| | opacity:0;transition:opacity .3s;
|
| | }
|
| | .ecard:hover{border-color:var(--b2);box-shadow:var(--sh);}
|
| | .ecard:hover::before{opacity:1;}
|
| |
|
| | .ehead{padding:24px 26px 20px;border-bottom:1px solid var(--b1);display:flex;align-items:flex-start;gap:14px;}
|
| | .meth{font-size:9px;font-weight:700;letter-spacing:2px;padding:4px 11px;border-radius:5px;flex-shrink:0;margin-top:5px;}
|
| | .post{background:var(--post-bg);color:var(--post-col);border:1px solid var(--post-bd);}
|
| | .get {background:var(--get-bg); color:var(--get-col); border:1px solid var(--get-bd);}
|
| | .epath{font-size:24px;font-weight:800;letter-spacing:-1.2px;margin-bottom:4px;}
|
| | .esumm{font-size:11px;color:var(--f2);font-weight:400;letter-spacing:.2px;}
|
| |
|
| | .ebody{padding:22px 26px;}
|
| | .pts{list-style:none;display:flex;flex-direction:column;gap:10px;margin-bottom:22px;}
|
| | .pts li{display:flex;align-items:flex-start;gap:11px;font-size:12px;color:var(--f2);line-height:1.75;font-weight:400;}
|
| | .pd{width:5px;height:5px;border-radius:50%;background:var(--accent);flex-shrink:0;margin-top:8px;}
|
| | [data-theme="dark"] .pd{box-shadow:0 0 8px rgba(255,212,73,.5);}
|
| | [data-theme="light"] .pd{box-shadow:0 0 6px rgba(9,9,26,.3);}
|
| | .pts li strong{color:var(--fg);font-weight:600;}
|
| |
|
| | .efoot{display:flex;align-items:center;justify-content:space-between;flex-wrap:wrap;gap:10px;}
|
| | .etags{display:flex;gap:5px;flex-wrap:wrap;}
|
| | .tag{font-size:9px;padding:3px 9px;border-radius:5px;
|
| | background:var(--card2);border:1px solid var(--b2);color:var(--f3);letter-spacing:.5px;}
|
| |
|
| | .elink{
|
| | display:inline-flex;align-items:center;gap:6px;padding:8px 16px;border-radius:8px;
|
| | border:1px solid var(--aborder);background:var(--aglow);
|
| | color:var(--accent);font-size:11px;font-family:'JetBrains Mono',monospace;
|
| | text-decoration:none;cursor:pointer;letter-spacing:.3px;font-weight:500;
|
| | }
|
| | .elink:hover{background:var(--aglow2);border-color:var(--accent2);color:var(--accent2);}
|
| |
|
| | /* ════ MODEL CARDS ══════════════════════════════════════════ */
|
| | .mgrid{display:grid;grid-template-columns:repeat(auto-fit,minmax(240px,1fr));gap:12px;}
|
| | .mcard{
|
| | background:var(--card);border:1px solid var(--b1);border-radius:16px;
|
| | padding:24px;position:relative;overflow:hidden;
|
| | }
|
| | .mcard::after{content:'';position:absolute;bottom:0;left:0;right:0;height:2.5px;
|
| | background:linear-gradient(90deg,var(--c1),var(--c2));}
|
| | .mcard:hover{border-color:var(--b2);transform:translateY(-5px);box-shadow:var(--sh);}
|
| | .micon{width:44px;height:44px;border-radius:12px;display:flex;align-items:center;
|
| | justify-content:center;font-size:20px;margin-bottom:16px;
|
| | background:var(--card2);border:1px solid var(--b2);}
|
| | .mname{font-size:18px;font-weight:800;letter-spacing:-.8px;margin-bottom:4px;}
|
| | .mid{font-size:9px;color:var(--f3);margin-bottom:10px;word-break:break-all;line-height:1.8;font-weight:400;}
|
| | .mdesc{font-size:11.5px;color:var(--f2);line-height:1.8;font-weight:400;}
|
| | .mbadge{display:inline-block;font-size:9px;padding:3px 10px;border-radius:5px;
|
| | margin-top:12px;letter-spacing:.8px;font-weight:600;}
|
| |
|
| | /* ════ FOOTER ═══════════════════════════════════════════════ */
|
| | footer{
|
| | position:relative;z-index:1;border-top:1px solid var(--b1);
|
| | padding:28px 44px;display:flex;align-items:center;justify-content:space-between;flex-wrap:wrap;gap:14px;
|
| | }
|
| | .fbrand{font-size:16px;font-weight:800;letter-spacing:-1px;color:var(--accent);}
|
| | .flinks{display:flex;gap:22px;flex-wrap:wrap;}
|
| | .flinks a{font-size:10px;color:var(--f3);text-decoration:none;letter-spacing:.5px;}
|
| | .flinks a:hover{color:var(--accent);}
|
| | .fright{font-size:10px;color:var(--f3);}
|
| |
|
| | @keyframes fu{from{opacity:0;transform:translateY(20px);}to{opacity:1;transform:translateY(0);}}
|
| | ::-webkit-scrollbar{width:4px;}
|
| | ::-webkit-scrollbar-thumb{background:var(--b2);border-radius:2px;}
|
| |
|
| | @media(max-width:640px){
|
| | nav{padding:0 18px;}h1{font-size:48px;letter-spacing:-3px;}
|
| | .stat{padding:16px 20px;}.epgrid{grid-template-columns:1fr;}
|
| | .mgrid{grid-template-columns:1fr 1fr;}footer{flex-direction:column;text-align:center;}
|
| | }
|
| | </style>
|
| | </head>
|
| | <body>
|
| | <div class="blob b1"></div>
|
| | <div class="blob b2"></div>
|
| | <div class="blob b3"></div>
|
| |
|
| | <!-- NAV -->
|
| | <nav>
|
| | <a class="nlogo" href="/">
|
| | <span class="nlogo-main">MoodLens</span>
|
| | <span class="nlogo-tag">Sentiment Intelligence</span>
|
| | </a>
|
| | <div class="nr">
|
| | <div class="live-pill">
|
| | <span class="ldot"></span>Online
|
| | </div>
|
| | <a class="npill" href="/health">/health</a>
|
| | <a class="npill" href="/docs">/docs</a>
|
| | <a class="npill" href="/redoc">/redoc</a>
|
| | <button class="tbtn" id="themeToggle" title="Toggle theme">🌙</button>
|
| | </div>
|
| | </nav>
|
| |
|
| | <!-- HERO -->
|
| | <div class="hero">
|
| | <div class="eyebrow">
|
| | <span class="ldot"></span>
|
| | Zomato Dataset · 4 Models · v2.0
|
| | </div>
|
| | <p class="hero-pre">Sentiment Intelligence Platform</p>
|
| | <h1>
|
| | <span class="h1a">Decode</span>
|
| | <span class="h1b">Sentiment.</span>
|
| | </h1>
|
| | <p class="hsub">
|
| | Enterprise NLP engine · <strong>RoBERTa · DistilRoBERTa · BERT · ALBERT</strong><br/>
|
| | Fine-tuned on <strong>2.9M+ Zomato Reviews</strong> · Three-class · Sub-second inference
|
| | </p>
|
| | <div class="hbtns">
|
| | <a class="bpri" href="/docs">
|
| | <svg width="13" height="13" viewBox="0 0 24 24" fill="none">
|
| | <path d="M14 2H6a2 2 0 00-2 2v16a2 2 0 002 2h12a2 2 0 002-2V8z" stroke="currentColor" stroke-width="2.2"/>
|
| | <polyline points="14,2 14,8 20,8" stroke="currentColor" stroke-width="2.2"/>
|
| | <line x1="8" y1="13" x2="16" y2="13" stroke="currentColor" stroke-width="2" stroke-linecap="round"/>
|
| | </svg>
|
| | Explore API Docs
|
| | </a>
|
| | <a class="bout" href="/health">
|
| | <svg width="13" height="13" viewBox="0 0 24 24" fill="none">
|
| | <path d="M22 12h-4l-3 9L9 3l-3 9H2" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
| | </svg>
|
| | Health Check
|
| | </a>
|
| | </div>
|
| | <div class="stats">
|
| | <div class="stat"><div class="sv">4</div><div class="sl">Transformers</div></div>
|
| | <div class="stat"><div class="sv">3</div><div class="sl">Classes</div></div>
|
| | <div class="stat"><div class="sv">2.9M+</div><div class="sl">Reviews</div></div>
|
| | <div class="stat"><div class="sv">512</div><div class="sl">Max Tokens</div></div>
|
| | </div>
|
| | <div class="online-bar">
|
| | <span class="on">● API Online</span>
|
| | <span class="sep">|</span>
|
| | <span>RoBERTa Ready</span>
|
| | <span class="sep">|</span>
|
| | <span id="ts"></span>
|
| | </div>
|
| | </div>
|
| |
|
| | <!-- TERMINAL DEMO -->
|
| | <div class="terminal-wrap">
|
| | <div class="terminal">
|
| | <div class="t-bar">
|
| | <div class="t-dot" style="background:#FF4D6A;"></div>
|
| | <div class="t-dot" style="background:#FFD449;"></div>
|
| | <div class="t-dot" style="background:#00E5A0;"></div>
|
| | <span class="t-title">moodlens · api demo</span>
|
| | </div>
|
| | <div class="t-body">
|
| | <div class="t-line"><span class="t-prompt">$</span><span> curl -X POST http://localhost:8000/predict \</span></div>
|
| | <div class="t-line"><span class="t-prompt"> </span><span> -H <span class="t-str">"Content-Type: application/json"</span> \</span></div>
|
| | <div class="t-line"><span class="t-prompt"> </span><span> -d <span class="t-str">'{"text": "Best biryani I have ever had!"}'</span></span></div>
|
| | <br/>
|
| | <div class="t-line"><span class="t-prompt" style="color:var(--f3)">#</span><span class="t-cmt"> 200 OK · 284ms</span></div>
|
| | <div class="t-out">{</div>
|
| | <div class="t-out"> <span class="t-key">"prediction"</span>: <span class="t-pos">"Positive"</span>,</div>
|
| | <div class="t-out"> <span class="t-key">"confidence"</span>: <span class="t-num">0.978</span>,</div>
|
| | <div class="t-out"> <span class="t-key">"positive"</span>: <span class="t-num">0.978</span>,</div>
|
| | <div class="t-out"> <span class="t-key">"neutral"</span>: <span class="t-num">0.015</span>,</div>
|
| | <div class="t-out"> <span class="t-key">"negative"</span>: <span class="t-num">0.007</span></div>
|
| | <div class="t-out">}</div>
|
| | <br/>
|
| | <div class="t-line"><span class="t-prompt">$</span><span class="t-cursor"></span></div>
|
| | </div>
|
| | </div>
|
| | </div>
|
| |
|
| | <!-- ENDPOINTS SECTION -->
|
| | <div class="wrap">
|
| | <div class="divider"></div>
|
| | <p class="slabel">API Endpoints</p>
|
| | <h2 class="stitle">Four Routes.<br/>Zero Confusion.</h2>
|
| | <p class="sdesc">Click "Try it live" on any card — opens Swagger directly at that endpoint, ready to test with one click.</p>
|
| |
|
| | <div class="epgrid">
|
| |
|
| | <!-- /predict -->
|
| | <div class="ecard">
|
| | <div class="ehead">
|
| | <span class="meth post">POST</span>
|
| | <div>
|
| | <div class="epath">/predict</div>
|
| | <div class="esumm">Single-model RoBERTa sentiment prediction</div>
|
| | </div>
|
| | </div>
|
| | <div class="ebody">
|
| | <ul class="pts">
|
| | <li><span class="pd"></span><span>Text is <strong>tokenised and truncated</strong> to 512 tokens, passed through RoBERTa in a single forward pass</span></li>
|
| | <li><span class="pd"></span><span>Softmax output mapped to <strong>Positive / Neutral / Negative</strong> with raw probability for each class</span></li>
|
| | <li><span class="pd"></span><span>Returns <strong>prediction label</strong>, confidence score, all three class probabilities in one clean JSON</span></li>
|
| | <li><span class="pd"></span><span>Best for <strong>high-throughput pipelines</strong> where one best-in-class model is sufficient</span></li>
|
| | </ul>
|
| | <div class="efoot">
|
| | <div class="etags"><span class="tag">Inference</span><span class="tag">RoBERTa</span><span class="tag">JSON</span></div>
|
| | <a class="elink" href="/docs" onclick="openOp(event,'predict')">
|
| | Try it live
|
| | <svg width="11" height="11" viewBox="0 0 24 24" fill="none"><path d="M7 17L17 7M17 7H7M17 7v10" stroke="currentColor" stroke-width="2" stroke-linecap="round"/></svg>
|
| | </a>
|
| | </div>
|
| | </div>
|
| | </div>
|
| |
|
| | <!-- /compare -->
|
| | <div class="ecard">
|
| | <div class="ehead">
|
| | <span class="meth post">POST</span>
|
| | <div>
|
| | <div class="epath">/compare</div>
|
| | <div class="esumm">All 4 models — parallel inference & consensus</div>
|
| | </div>
|
| | </div>
|
| | <div class="ebody">
|
| | <ul class="pts">
|
| | <li><span class="pd"></span><span>Runs <strong>RoBERTa, DistilRoBERTa, BERT, and ALBERT</strong> on the same text independently</span></li>
|
| | <li><span class="pd"></span><span>Each model's raw labels <strong>normalised</strong> to the same three-class schema before comparison</span></li>
|
| | <li><span class="pd"></span><span>Results <strong>sorted by confidence</strong> — highest-confidence model ranked first in the response array</span></li>
|
| | <li><span class="pd"></span><span>Use when you need <strong>cross-model validation</strong> or the most reliable possible prediction</span></li>
|
| | </ul>
|
| | <div class="efoot">
|
| | <div class="etags"><span class="tag">Inference</span><span class="tag">Multi-Model</span><span class="tag">Ensemble</span></div>
|
| | <a class="elink" href="/docs" onclick="openOp(event,'compare')">
|
| | Try it live
|
| | <svg width="11" height="11" viewBox="0 0 24 24" fill="none"><path d="M7 17L17 7M17 7H7M17 7v10" stroke="currentColor" stroke-width="2" stroke-linecap="round"/></svg>
|
| | </a>
|
| | </div>
|
| | </div>
|
| | </div>
|
| |
|
| | <!-- /health -->
|
| | <div class="ecard">
|
| | <div class="ehead">
|
| | <span class="meth get">GET</span>
|
| | <div>
|
| | <div class="epath">/health</div>
|
| | <div class="esumm">Server status & ML engine diagnostics</div>
|
| | </div>
|
| | </div>
|
| | <div class="ebody">
|
| | <ul class="pts">
|
| | <li><span class="pd"></span><span>Returns <strong>server liveness</strong>, API version, and active model list in a single JSON response</span></li>
|
| | <li><span class="pd"></span><span>Lists all <strong>four active model names</strong> and training dataset for at-a-glance verification</span></li>
|
| | <li><span class="pd"></span><span>Ideal for <strong>CI/CD pipelines, uptime monitors</strong>, and pre-flight checks before batch inference jobs</span></li>
|
| | <li><span class="pd"></span><span>Returns <strong>UTC timestamp</strong> to verify server clock is correctly synchronised</span></li>
|
| | </ul>
|
| | <div class="efoot">
|
| | <div class="etags"><span class="tag">System</span><span class="tag">Monitoring</span><span class="tag">DevOps</span></div>
|
| | <a class="elink" href="/health" target="_blank">
|
| | View live
|
| | <svg width="11" height="11" viewBox="0 0 24 24" fill="none"><path d="M7 17L17 7M17 7H7M17 7v10" stroke="currentColor" stroke-width="2" stroke-linecap="round"/></svg>
|
| | </a>
|
| | </div>
|
| | </div>
|
| | </div>
|
| |
|
| | <!-- /docs -->
|
| | <div class="ecard">
|
| | <div class="ehead">
|
| | <span class="meth get">GET</span>
|
| | <div>
|
| | <div class="epath">/docs</div>
|
| | <div class="esumm">Interactive Swagger UI — test every endpoint live</div>
|
| | </div>
|
| | </div>
|
| | <div class="ebody">
|
| | <ul class="pts">
|
| | <li><span class="pd"></span><span>Full <strong>OpenAPI 3.1 schema</strong> auto-generated from Pydantic models — every field typed and described</span></li>
|
| | <li><span class="pd"></span><span><strong>"Try it out"</strong> lets you fire real requests to /predict and /compare without writing any code</span></li>
|
| | <li><span class="pd"></span><span>Every request and response schema <strong>documented inline</strong> with constraints and live example values</span></li>
|
| | <li><span class="pd"></span><span>Dark-themed Swagger with <strong>Monokai syntax highlighting</strong> and request duration display</span></li>
|
| | </ul>
|
| | <div class="efoot">
|
| | <div class="etags"><span class="tag">Docs</span><span class="tag">OpenAPI</span><span class="tag">Swagger</span></div>
|
| | <a class="elink" href="/docs" target="_blank">
|
| | Open Docs
|
| | <svg width="11" height="11" viewBox="0 0 24 24" fill="none"><path d="M7 17L17 7M17 7H7M17 7v10" stroke="currentColor" stroke-width="2" stroke-linecap="round"/></svg>
|
| | </a>
|
| | </div>
|
| | </div>
|
| | </div>
|
| |
|
| | </div>
|
| |
|
| | <!-- MODEL CARDS -->
|
| | <div style="margin-top:90px;">
|
| | <p class="slabel">Transformer Models</p>
|
| | <h2 class="stitle">Four Engines.<br/>One Verdict.</h2>
|
| | <p class="sdesc" style="margin-bottom:38px;">Each model brings a unique specialisation — together they form a robust, cross-validated ensemble.</p>
|
| | <div class="mgrid">
|
| | <div class="mcard" style="--c1:var(--accent);--c2:var(--accent2);">
|
| | <div class="micon">🧠</div>
|
| | <div class="mname">RoBERTa</div>
|
| | <div class="mid">cardiffnlp/twitter-roberta-base-sentiment-latest</div>
|
| | <div class="mdesc">Default model. Trained on 124M tweets. Best general-purpose accuracy across review types and domains.</div>
|
| | <span class="mbadge" style="background:var(--aglow);color:var(--accent);border:1px solid var(--aborder);">⭐ DEFAULT</span>
|
| | </div>
|
| | <div class="mcard" style="--c1:var(--accent2);--c2:var(--accent);">
|
| | <div class="micon">⚡</div>
|
| | <div class="mname">DistilRoBERTa</div>
|
| | <div class="mid">mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis</div>
|
| | <div class="mdesc">40% faster than RoBERTa. Fine-tuned on financial news and consumer reviews. Low-latency inference.</div>
|
| | <span class="mbadge" style="background:var(--aglow2);color:var(--accent2);border:1px solid var(--aborder2);">FAST</span>
|
| | </div>
|
| | <div class="mcard" style="--c1:#276EF1;--c2:var(--accent);">
|
| | <div class="micon">🌍</div>
|
| | <div class="mname">BERT</div>
|
| | <div class="mid">nlptown/bert-base-multilingual-uncased-sentiment</div>
|
| | <div class="mdesc">Multilingual BERT fine-tuned in 6 languages. 5-star scale mapped to Positive / Neutral / Negative.</div>
|
| | <span class="mbadge" style="background:rgba(39,110,241,.1);color:#276EF1;border:1px solid rgba(39,110,241,.25);">MULTILINGUAL</span>
|
| | </div>
|
| | <div class="mcard" style="--c1:#09091A;--c2:#276EF1;">
|
| | <div class="micon">🎯</div>
|
| | <div class="mname">ALBERT</div>
|
| | <div class="mid">textattack/albert-base-v2-yelp-polarity</div>
|
| | <div class="mdesc">Parameter-efficient architecture fine-tuned on Yelp reviews. Excellent on short, punchy restaurant feedback.</div>
|
| | <span class="mbadge" style="background:rgba(9,9,26,.1);color:var(--f2);border:1px solid var(--b2);">EFFICIENT</span>
|
| | </div>
|
| | </div>
|
| | </div>
|
| | </div>
|
| |
|
| | <!-- FOOTER -->
|
| | <footer>
|
| | <div class="fbrand">MoodLens</div>
|
| | <div class="flinks">
|
| | <a href="/docs">Swagger</a>
|
| | <a href="/redoc">ReDoc</a>
|
| | <a href="/health">Health</a>
|
| | <a href="/openapi.json">OpenAPI JSON</a>
|
| | </div>
|
| | <div class="fright">v2.0.0 · Zomato Dataset · <span id="fts"></span></div>
|
| | </footer>
|
| |
|
| | <script>
|
| | // Theme toggle
|
| | const root = document.documentElement;
|
| | const btn = document.getElementById('themeToggle');
|
| | let dark = true;
|
| | btn.addEventListener('click', () => {
|
| | dark = !dark;
|
| | root.setAttribute('data-theme', dark ? 'dark' : 'light');
|
| | btn.textContent = dark ? '🌙' : '☀️';
|
| | });
|
| |
|
| | // Clock
|
| | const tick = () => {
|
| | const s = new Date().toLocaleTimeString('en-IN',{hour:'2-digit',minute:'2-digit',second:'2-digit'});
|
| | const a = document.getElementById('ts');
|
| | const b = document.getElementById('fts');
|
| | if(a) a.textContent = s;
|
| | if(b) b.textContent = s;
|
| | };
|
| | tick(); setInterval(tick, 1000);
|
| |
|
| | // Try it live — opens /docs at exact Swagger anchor in new tab
|
| | function openOp(e, route) {
|
| | e.preventDefault();
|
| | const map = {
|
| | predict : '/docs#/Inference/get_prediction_predict_post',
|
| | compare : '/docs#/Inference/compare_models_compare_post',
|
| | };
|
| | window.open(map[route] || '/docs', '_blank');
|
| | }
|
| |
|
| | // Scroll reveal
|
| | const obs = new IntersectionObserver(entries => {
|
| | entries.forEach(e => {
|
| | if (e.isIntersecting) {
|
| | e.target.style.opacity = '1';
|
| | e.target.style.transform = 'translateY(0)';
|
| | }
|
| | });
|
| | }, { threshold: 0.07 });
|
| | document.querySelectorAll('.ecard, .mcard').forEach((el, i) => {
|
| | el.style.opacity = '0';
|
| | el.style.transform = 'translateY(22px)';
|
| | el.style.transition = [
|
| | `opacity .5s ${i * 0.08}s ease`,
|
| | `transform .5s ${i * 0.08}s ease`,
|
| | 'border-color .25s', 'box-shadow .25s',
|
| | ].join(', ');
|
| | obs.observe(el);
|
| | });
|
| | </script>
|
| | </body>
|
| | </html>"""
|
| |
|
| |
|
| | if __name__ == "__main__":
|
| | import os
|
| | import uvicorn
|
| |
|
| | port = int(os.environ.get("PORT", 7860))
|
| | uvicorn.run("app:app", host="0.0.0.0", port=port) |
