MarketMind AI – Startup Intelligence Engine

An end-to-end AI system that uses Retrieval-Augmented Generation (RAG), web scraping, FAISS vector database, open-source embeddings, Groq-hosted LLM inference, and an intelligent scoring engine to analyze startup ideas.

Features

• Web Data Pipeline – Scrapes DuckDuckGo and Wikipedia for real-time market context
• RAG Engine – FAISS vector search + sentence-transformers embeddings for semantic retrieval
• Smart Scoring – Min-max normalized metrics for demand, competition, growth, monetization & viability
• LLM Analysis – Groq-powered (LLaMA 3.3 70B) structured market insights
• Beautiful UI – Premium dark-themed Streamlit dashboard with animated metric cards

Tech Stack

Layer	Technology
Frontend	Streamlit
Embeddings	sentence-transformers (all-MiniLM-L6-v2)
Vector DB	FAISS
LLM	Groq API (LLaMA 3.3 70B)
Scraping	BeautifulSoup4 + Requests
ML Scoring	Custom heuristic engine

Installation

1. Clone the repository:

   git clone https://github.com/YOUR_USERNAME/marketmind-ai.git
   cd marketmind-ai

2. Create a virtual environment (recommended):

   python3 -m venv .venv
   source .venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up your API key:

   cp .env.example .env
   # Edit .env and add your Groq API key

5. Run the app:

   streamlit run app.py

Deploying to Streamlit Cloud

1. Push your code to GitHub (the .gitignore already excludes .env and secrets)
2. Go to share.streamlit.io
3. Connect your GitHub repo
4. In the app settings, go to Secrets and add:

   GROQ_API_KEY = "your_actual_groq_api_key"
   GROQ_MODEL = "llama-3.3-70b-versatile"
   GROQ_FALLBACK_MODEL = "llama-3.1-8b-instant"

5. Deploy!

Environment Variables

Variable	Required	Default	Description
GROQ_API_KEY	✅ Yes	—	Your Groq API key
GROQ_MODEL	No	llama-3.3-70b-versatile	Primary LLM model
GROQ_FALLBACK_MODEL	No	llama-3.1-8b-instant	Fallback if primary hits rate limits

Project Structure

marketmind/
├── app.py                  # Streamlit UI & orchestration
├── ml/
│   └── model.py            # Scoring engine (normalize, monetization, viability)
├── rag/
│   ├── embeddings.py       # Sentence-transformer embedding generation
│   ├── vector_store.py     # FAISS index creation & search
│   ├── retriever.py        # Semantic retrieval pipeline
│   └── generator.py        # Groq LLM API integration
├── scraping/
│   └── web_scraper.py      # DuckDuckGo + Wikipedia data fetching
├── utils/
│   ├── cleaning.py         # Text preprocessing
│   └── chunking.py         # Text chunking for embeddings
├── requirements.txt
├── .env.example
├── .gitignore
└── README.md