This is a production-ready MVP scaffold covering:

• Python + Streamlit multipage web app
• Secure auth (signup/login with hashed passwords + JWT helpers)
• SQLite data logging + historical tracking
• BloomInsight (Instagram analysis) with custom scraper placeholders + fallbacks
• KPI dashboards (mock + real-ready)
• Weekly PDF Report generation (ReportLab) + Email delivery (SMTP / MailerLite-ready)
• APScheduler-based tasks (+ Render Cron alternative)
• Modular AI tools (caption/hashtag generator, audit tools, etc., with OpenAI pluggable)
• Deployment on Render with render.yaml

⚠️ Instagram's public HTML and endpoints change often. This repo provides a Custom Scraper API stub and a fallback mock so your app works end-to-end even without live scrape access. Swap in your working scraper in bloominsight/scraper.py.

# 1) Create & activate venv (recommended)
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# 2) Install deps
pip install -r requirements.txt

# 3) Create .env (see .env.example)
cp .env.example .env
# Fill in OPENAI_API_KEY, SMTP creds, etc.

# 4) Initialize DB
python tools/init_db.py

# 5) Run Streamlit app
streamlit run streamlit_app.py

Open: http://localhost:8501

Default pages: Home, Features, Pricing, Blog, Dashboard, Contact, About, Login, Signup.

1. Push this repo to GitHub.
2. On Render, create a Web Service:
   • Runtime: Python 3.12+
   • Build Command: pip install -r requirements.txt
   • Start Command: streamlit run streamlit_app.py --server.port $PORT --server.address 0.0.0.0
3. Add Environment Variables (Render Dashboard → Environment):
   • OPENAI_API_KEY (optional, for AI features)
   • JWT_SECRET (required, any random string)
   • SMTP_HOST, SMTP_PORT, SMTP_USER, SMTP_PASS, SMTP_FROM
   • MAILERLITE_API_KEY (optional)
   • BASE_URL (your service URL, used in links/reports)
4. (Optional) Cron Job on Render to send weekly reports:
   • Make a second Web Service from api_server.py (FastAPI endpoints), or
   • Create a Cron Job that runs: python tools/run_weekly_reports.py

Using two services: one for the Streamlit UI, one for the API/scheduler, is a clean separation for scale.

brand-n-bloom/
├─ streamlit_app.py                # Entrypoint (Home page)
├─ pages/
│  ├─ 01_🏠_Home.py
│  ├─ 02_✨_Features.py
│  ├─ 03_💰_Pricing.py
│  ├─ 04_📰_Blog.py
│  ├─ 05_📊_Dashboard.py
│  ├─ 06_📨_Contact.py
│  ├─ 07_ℹ️_About.py
│  ├─ 08_🔐_Login.py
│  └─ 09_🆕_Signup.py
├─ bloominsight/
│  ├─ __init__.py
│  ├─ scraper.py                   # Custom Instagram scraper (stub + fallback)
│  ├─ analyzer.py                  # KPIs, ER, hashtag/caption suggestions
│  ├─ report_api.py                # PDF pipeline & weekly email
│  └─ utils.py
├─ ai_tools/
│  ├─ __init__.py
│  ├─ caption_generator.py
│  ├─ hashtag_recommender.py
│  ├─ prompts.py
│  └─ audit_tools.py
├─ auth/
│  ├─ __init__.py
│  ├─ jwt_utils.py
│  ├─ security.py
│  └─ session.py
├─ data/
│  ├─ schema.sql
│  └─ sample_instagram.json
├─ db/
│  ├─ __init__.py
│  ├─ db.py
│  └─ models.py
├─ emailer/
│  ├─ __init__.py
│  └─ mailer.py
├─ scheduler/
│  ├─ __init__.py
│  └─ scheduler.py
├─ tools/
│  ├─ init_db.py
│  └─ run_weekly_reports.py
├─ utils/
│  ├─ charts.py
│  └─ pdf.py
├─ api_server.py                   # Optional FastAPI for background tasks
├─ requirements.txt
├─ render.yaml
├─ .streamlit/config.toml
├─ .env.example
└─ .gitignore

• Auth: Passwords are hashed (bcrypt). JWT helpers allow stateless auth if you decide to expose APIs later. Streamlit session is used for UI-session.
• SEO: Streamlit is SPA-like. Use st.set_page_config, meta description, and fast-loading assets. Consider a static marketing site (Next.js) fronting this for advanced SEO.
• PDF: utils/pdf.py uses ReportLab → lightweight and Render-friendly.
• Scheduler: APScheduler works in-process; on Render, prefer a Cron Job or separate worker for reliability.
• Scraper: Replace stubs in bloominsight/scraper.py with your working implementation.
• OpenAI features are guarded by env checks, so the app runs without keys.

This project includes a live Playwright scraper with proxy support and a safe fallback.

Set environment variable:

IG_SCRAPER_MODE=live

Optional proxy:

PROXY_SERVER=http://host:port
PROXY_USERNAME=youruser
PROXY_PASSWORD=yourpass

After installing requirements, run:

python -m playwright install chromium

If scraping fails (rate limits/cookies/layout changes), the app automatically falls back to demo data so dashboards remain usable.

1. Install dependencies:

   pip install -r requirements.txt

2. Install Playwright browsers (required for live scraping):

   python -m playwright install chromium

3. Set environment variables (Render Dashboard or .env):

   • JWT_SECRET - random string
   • SMTP_HOST, SMTP_PORT, SMTP_USER, SMTP_PASS, SMTP_FROM
   • IG_SCRAPER_MODE=live (or fallback)
   • PROXY_SERVER (optional), PROXY_USERNAME, PROXY_PASSWORD
   • OPENAI_API_KEY (optional)
   • DATABASE_URL (e.g., sqlite:///bnb.sqlite3 or Postgres URL)
   • BASE_URL (your app URL)

4. On Render:

   • Create two services: brand-n-bloom-app (Streamlit) and brand-n-bloom-api (Flask).
   • For the Flask service set start command: gunicorn app:app --bind 0.0.0.0:$PORT
   • For Streamlit start command: streamlit run streamlit_app.py --server.port $PORT --server.address 0.0.0.0
   • Add a Cron Job or Scheduled Job to call /send-report per user weekly (or run python tools/run_weekly_reports.py as a cron script).

5. Database:

   • For production, use Postgres (Render Postgres addon) and set DATABASE_URL accordingly. Update db/db.py to use SQLAlchemy/Postgres if needed.

6. Logs & Monitoring:

   • Enable Render logs for both services. For Playwright scraping errors, ensure proxy and rate-limiting are configured.

• BloomScore (ai_tools/bloomscore.py) — combined brand health scoring.
• Consumer Behavior Analysis (ai_tools/consumer_behavior.py) — questionnaire engine.
• Automated Email Marketing (emailer/mailer and emailer/mailerlite_adapter) — SMTP + MailerLite adapter.
• Influencer Finder (ai_tools/influencer_finder.py) — rank handles by ER & followers.
• Business Comparison (ai_tools/business_compare.py) — compare key metrics across handles.
• Menu Pricing Optimization (ai_tools/menu_pricing.py) — pricing suggestions based on cost & margin.
• Customer Loyalty AI (ai_tools/loyalty.py) — simple points-to-rewards engine.

These modules are rule-based and ready to replace with ML-backed logic as you scale.