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🛰️ Cipher — Viral Claim Radar

Professional Fact-Checking Copilot for Social Media

"Antivirus for information." Highlight any text on any website — or capture any image/screenshot — and get an AI-powered verdict in under 10 seconds.


🚀 What Makes Cipher Different (Your USP)

Everyone Else Cipher
Single-model checkers Multi-Model: Groq (Llama-3.3) and Gemini 1.5 Flash
No caching Qdrant Semantic Cache — same claim, different wording = ⚡ Instant
Only works on selected text Text Selection + Full-page OCR + Dynamic Live Search
Outdated Info Dynamic Hybrid Radar — Real-time web gathering via Tavily AI
Number, no story Manipulation Meter scores emotional language and fake news framing

🏗️ Architecture: The Hybrid Radar

Cipher uses a Retrieval-Augmented Generation (RAG) pipeline optimized for the live web:

  1. Semantic Cache (RAG First): Searches Qdrant Cloud for a semantically similar claim (3072-vector match). If found, returns the verdict in under 100ms.
  2. Web Search (Dynamic Fallback): If no cache match exists, it performs a real-time search using Tavily AI.
  3. Multimodal Analysis: Claims are analyzed by Gemini 1.5 Flash (Multimodal/Vision) or Groq to provide a stance (Supported, Refuted, or Uncertain).

📂 Repository Structure

Cipher/                        ← Monorepo root
├── backend/                   ← Python FastAPI (deploys to Railway)
│   ├── main.py                ← /verify, /stats, /tone, /ocr endpoints
│   ├── routers/
│   │   ├── verify.py          ← REFINED: Hybrid-RAG (Cache + Search) logic
│   │   ├── ocr.py             ← AI image/screenshot fact-checking
│   │   └── tone.py            ← Manipulation / Bias analysis
│   ├── services/
│   │   ├── cache_service.py   ← NEW: Qdrant cloud semantic cache
│   │   ├── search_service.py  ← NEW: Tavily live web search API
│   │   └── llm_client.py      ← Groq / Gemini JSON client
│   ├── Procfile               ← Railway start command
│   ├── requirements.txt
│   └── .env.example
│
├── cipher-extension/          ← Chrome Extension Manifest v3
│   ├── manifest.json
│   ├── content.js             ← Professional UI + Draggable Card + Badge
│   ├── popup.html             ← Toolbar telemetry popup
│   └── icons/                 ← Transparent branding assets
│
├── dashboard/                 ← Next.js Professional Frontend (Vercel)
│   ├── src/app/page.tsx       ← GSAP-powered Retro Landing Page
│   ├── src/app/dashboard/     ← Live System Stats & Telemetry
│   └── src/components/        ← Reusable Retro Design System (Navbar, Cards)
│
├── docs/                      ← Comprehensive project documentation
└── README.md                  ← This file

🛠️ Setup & Deployment

  1. Backend:

    • cd backend && python -m venv .venv && source .venv/bin/activate
    • pip install -r requirements.txt
    • Add your keys (TAVILY, GROQ, GEMINI, QDRANT) to backend/.env.
    • uvicorn main:app --reload
  2. Dashboard:

    • cd dashboard && npm install && npm run dev
  3. Extension:

    • Load cipher-extension/ in Chrome via Developer Mode.
    • Point BACKEND to your production URL.

✅ Feature Roadmap

📦 Phase 1: Core (Shipped)

  • Dynamic Radar: Text highlight → Real-time fact-check badge (✅ / ❌ / ⚠️)
  • Hybrid-RAG: Instant cache hits via Qdrant Semantic memory.
  • OCR Radar: Right-click image → Fact-check all text inside screenshot.
  • Manipulation Meter: AI score for emotional language & conspiracy framing.
  • Professional Dashboard: Next.js stats and live processing telemetry.

🔭 Phase 2: Future Scope

  • Multilingual claim detection (Support for 50+ languages).
  • Enterprise API for newsrooms + Slack/Discord bot.
  • Mobile Safari & Firefox ports.
  • Deep-fake video detection lab.

📜 Documentation Index


🛰️ Tech Stack

  • AI/LLM: Groq (Llama-3.3-70B), Gemini 1.5 Flash
  • Search Engine: Tavily AI (Native dynamic web gathering)
  • Vector DB: Qdrant Cloud (3072-dim Semantic Cache)
  • Backend: FastAPI + Gunicorn
  • Frontend: Next.js 14 + GSAP + Tailwind CSS

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