🚀 Startup Idea Evaluator

A scalable, JWT-secured backend built with FastAPI, designed to evaluate user-submitted startup ideas using Gemini API. It allows authenticated users to submit ideas and receive structured AI feedback scored across five categories. Ideal for product validation, ideation tools, or investor pitch pre-analysis.

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✨ Features

• 🔐 JWT Authentication (SignUp & Login with scoped access)

• 💡 AI-Powered Startup Evaluation

  • Creativity
  • Demand
  • Uniqueness
  • Scalability
  • Investment readiness

• 🧠 GEMINI Integration via gemini-1.5-flash

• 📦 Database Storage of evaluated ideas per user

• 📈 Scalable Architecture – future-ready to add premium usage limits or analytics

• 🗃️ Usage History – fetch user-specific past evaluations

• 🛡️ Custom Free Usage Control (usage tracking logic ready)

• 🔌 Modular FastAPI Router Design

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🔧 Tech Stack

• Frontend: Streamlit
• Framework: FastAPI (with APIRouter modular structure)
• Auth: JWT via OAuth2 with token-based flow
• Database: SQLAlchemy ORM + SQLite
• AI API: Google gemini v1.5-flash via google-genai Python SDK
• Env Management: python-dotenv
• Schema Validation: Pydantic

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🛠️ Setup Instructions

1. Clone the repo

git clone https://github.com/Asthraris/AI-StartUp-Idea-Validator.git
cd startup-evaluator-api

2. Install dependencies

pip install -r requirements.txt

3. Setup .env

GOOGLE_API_KEY = "YOUR KEY"
SECRET_KEY=your_jwt_secret
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=60

4. Run the server

uvicorn app.main:app 

5. Run the Client

streamlit run UI.py

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📑 API Routes Overview

🔐 Auth Routes

Method	Route	Description
POST	/signup	Register new user
POST	/login	Login and get token

Each route returns a JWT token to be used in Authorization header: Authorization: Bearer <token>

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💡 Idea Routes

Method	Route	Description
POST	/ideas	Submit a startup idea and get evaluation
GET	/ideas/history	View your previously submitted ideas

Note: Auth required for both routes.

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🧠 Example AI Output Format

{
  "startup_idea": "An local LLM used coding repo visualizer",
  "evaluation": {
    "creativity": {
      "sentence": "The concept of using a local LLM to visualize coding repositories is novel and offers a unique approach to code understanding.",
      "score": 7
    },
    "demand": {
      "sentence": "Demand exists among developers for tools improving code comprehension and collaboration, but market saturation in code visualization tools needs consideration.",
      "score": 6
    },
    "uniqueness": {
      "sentence": "The combination of local LLM processing and repository visualization is relatively unique, offering potential advantages in privacy and speed.",
      "score": 7
    },
    "scale": {
      "sentence": "Scaling could be challenging due to the computational resources required for local LLM processing, limiting potential user base.",
      "score": 5
    },
    "investment": {
      "sentence": "Investment potential is moderate, contingent on demonstrating a clear value proposition over existing solutions and achieving scalability.",
      "score": 6
    }
  }
}

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📦 Future Roadmap

• Rate limiting & quota management
• Admin dashboard with analytics
• User-defined evaluation criteria
• Support for team-based accounts

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🪪 License

MIT – free to use and extend.