Product Intelligence Agent

A dual-mode AI assistant powered by OpenRouter's free models (no API key needed!) that acts as:

• Pre-Purchase: Consultative salesperson helping customers find the right product
• Post-Purchase: Technical support engineer for troubleshooting and guidance

🚀 Quick Start (FREE - No API Key Required!)

# Backend
cd backend
echo AI_PROVIDER=openrouter > .env
pip install -r requirements.txt
python main.py

# Frontend
cd frontend  
npm run dev

Open http://localhost:5173 - That's it! Works completely free without any API keys.

Features

✨ Dual Mode Operation

• PRE_PURCHASE: Product recommendations, room analysis, color matching
• POST_PURCHASE: Troubleshooting, error codes, installation help, maintenance tips

🎨 Room Image Analysis

• Upload room photos for placement recommendations
• Color matching with available product variants
• Space assessment and fit validation
• Safety and ventilation considerations

🤖 Intelligent Agent

• Powered by OpenRouter free models (Mistral 7B + Qwen2 VL + Llama 3.1)
• Alternative: Google Gemini support included
• Honest product recommendations (never hallucinates specs)
• Safety-first approach (warns when professional help needed)
• Conversation memory for context-aware responses

📦 Product Catalog

• Washing Machines
• Refrigerators
• Headphones
• EV Batteries
• (Easily extensible)

Tech Stack

Backend:

• FastAPI (Python)
• OpenRouter API (free tier - Mistral, Qwen, Llama models)
• Alternative: Google Generative AI (Gemini)
• httpx (HTTP client)
• Pillow (image processing)
• Pydantic (data validation)

Frontend:

• React + Vite
• Axios (API calls)
• Lucide React (icons)
• Premium glassmorphism design

Setup Instructions

Prerequisites

• Python 3.9+
• Node.js 18+
• No API key required! (OpenRouter free tier)
• Optional: Google API Key for Gemini (Get it here)

Backend Setup

1. Navigate to backend directory:

cd backend

2. Create virtual environment:

python -m venv venv
venv\Scripts\activate  # Windows
# source venv/bin/activate  # macOS/Linux

3. Install dependencies:

pip install -r requirements.txt

4. Create .env file:

cp .env.example .env

5. Edit .env - No API key needed for OpenRouter!

AI_PROVIDER=openrouter
MODE=PRE_PURCHASE
BRAND_NAME=TechHome

Optional: For Gemini instead:

AI_PROVIDER=gemini
GOOGLE_API_KEY=your_actual_api_key_here

6. Run the backend:

python main.py

Backend will be available at http://localhost:8000

Frontend Setup

1. Navigate to frontend directory:

cd frontend

2. Install dependencies:

npm install

3. Run the development server:

npm run dev

Frontend will be available at http://localhost:5173

API Endpoints

Chat

POST /chat
{
  "message": "I need a washing machine for a family of 4",
  "category": "washing_machines"
}

Room Analysis

POST /analyze-room
Content-Type: multipart/form-data

file: <image>

Error Code Lookup

POST /error-code
{
  "model_id": "AT-WM-9KG-BLACK",
  "error_code": "E01"
}

Mode Switch

POST /mode
{
  "mode": "POST_PURCHASE"
}

Adding Your Product Data

Replace backend/data/products.json with your actual product catalog. Format:

{
  "category_name": [
    {
      "product_id": "unique_id",
      "category": "category_name",
      "brand": "Brand Name",
      "models": [
        {
          "model_id": "MODEL-ID",
          "color": "Color Name",
          "hex_color": "#hexcode",
          "dimensions_cm": [height, width, depth],
          "price": 999,
          "warranty_years": 2,
          "features": ["feature1", "feature2"],
          "installation": "Installation instructions",
          "common_issues": [
            {
              "error": "E01",
              "meaning": "Error meaning",
              "fix": "How to fix"
            }
          ]
        }
      ]
    }
  ]
}

Deployment

Backend (Google Cloud Run)

1. Create Dockerfile in backend directory
2. Build and deploy:

gcloud run deploy product-agent-api \
  --source . \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

Frontend (Vercel)

1. Update frontend/src/App.jsx with your API URL
2. Deploy:

cd frontend
vercel

Usage Examples

Pre-Purchase Mode

User: "I need a washing machine for my laundry room"

Agent: Asks clarifying questions about space, family size, budget

User: Uploads room image

Agent: Analyzes room, recommends best size and color variant

Post-Purchase Mode

User: "My fridge is showing error E01"

Agent: Looks up error code, provides step-by-step fix

User: "How do I clean the filter?"

Agent: Provides maintenance instructions

Safety Features

The agent automatically identifies dangerous operations and warns users:

• High voltage work → "This requires a certified technician"
• Gas connections → Refuses to provide guidance
• Internal electronics → Recommends professional service

License

MIT License - feel free to use for your products!

Support

For issues or questions, please open a GitHub issue or contact support.