File size: 4,807 Bytes
a282d4b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
# BankBot AI β€” Deployment Guide

## Option 1: Local Development (Fastest)

```bash
# 1. Clone and setup backend
cd backend
python -m venv venv
venv\Scripts\activate          # Windows
pip install -r requirements.txt
copy .env.example .env         # Edit with your API keys

# 2. Seed demo data
python app/scripts/seed_demo.py

# 3. Start backend
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

# 4. In a new terminal β€” setup frontend
cd frontend
npm install --legacy-peer-deps
npm run dev

# Access: http://localhost:3000
# Login: alex@bankbot.dev / BankBot2026!
# API Docs: http://localhost:8000/docs
# Metrics: http://localhost:8000/api/metrics
```

---

## Option 2: Docker Compose (Recommended for Demo)

```bash
# 1. Configure environment
cp .env.example .env
# Edit .env β€” set OPENAI_API_KEY or GROQ_API_KEY

# 2. Start all services (PostgreSQL + Redis + Backend + Frontend)
docker compose up -d

# 3. Seed demo data
docker compose exec backend python app/scripts/seed_demo.py

# 4. Access
# Frontend: http://localhost:3000
# Backend:  http://localhost:8000
# API Docs: http://localhost:8000/docs

# 5. View logs
docker compose logs -f backend
docker compose logs -f frontend

# 6. Stop
docker compose down
```

### With Nginx (Production mode)

```bash
docker compose --profile production up -d
# Access via http://localhost (port 80)
```

---

## Option 3: Cloud Deployment

### Frontend β†’ Vercel

```bash
cd frontend

# Install Vercel CLI
npm i -g vercel

# Deploy
vercel --prod

# Set environment variable in Vercel dashboard:
# NEXT_PUBLIC_API_URL = https://your-backend.onrender.com
```

### Backend β†’ Render

1. Push code to GitHub
2. Go to https://render.com β†’ New β†’ Web Service
3. Connect your GitHub repo
4. Render auto-detects `render.yaml` in `backend/`
5. Set environment variables in Render dashboard:
   - `OPENAI_API_KEY` or `GROQ_API_KEY`
   - `JWT_SECRET_KEY` (generate a strong random string)
6. Render provisions PostgreSQL and Redis automatically

### Backend β†’ Railway

```bash
# Install Railway CLI
npm i -g @railway/cli
railway login

cd backend
railway init
railway up

# Add PostgreSQL and Redis plugins in Railway dashboard
# Set environment variables in Railway dashboard
```

### Backend β†’ DigitalOcean App Platform

1. Create new App β†’ GitHub repo
2. Set source directory: `backend`
3. Build command: `pip install -r requirements.txt`
4. Run command: `uvicorn app.main:app --host 0.0.0.0 --port $PORT`
5. Add PostgreSQL and Redis managed databases
6. Set environment variables

---

## Environment Variables Reference

### Backend (Required for Production)

```env
# REQUIRED
JWT_SECRET_KEY=<generate with: python -c "import secrets; print(secrets.token_hex(32))">
DATABASE_URL=postgresql://user:pass@host:5432/bankbot

# REQUIRED (at least one AI key)
OPENAI_API_KEY=sk-...
# OR
GROQ_API_KEY=gsk_...

# RECOMMENDED
REDIS_URL=redis://host:6379/0
BACKEND_CORS_ORIGINS=["https://your-frontend.vercel.app"]
ACCESS_TOKEN_EXPIRE_MINUTES=60
```

### Frontend (Required for Production)

```env
NEXT_PUBLIC_API_URL=https://your-backend.onrender.com
```

---

## Post-Deployment Checklist

```
[ ] Backend health check passes: GET /health β†’ {"status": "healthy"}
[ ] API status shows correct backend: GET /api/status
[ ] Demo account works: POST /api/auth/login
[ ] Dashboard loads: GET /api/dashboard/overview
[ ] WebSocket connects: ws://your-backend/api/ai/chat/ws
[ ] Metrics endpoint works: GET /api/metrics
[ ] Frontend loads at production URL
[ ] CORS allows frontend origin
[ ] JWT tokens work end-to-end
[ ] Seed demo data: python app/scripts/seed_demo.py
```

---

## Troubleshooting

### Backend won't start
```bash
# Check Python version (needs 3.11+)
python --version

# Check if port is in use
netstat -ano | findstr :8000

# Check logs
uvicorn app.main:app --port 8000 --log-level debug
```

### Frontend can't reach backend
```bash
# Check NEXT_PUBLIC_API_URL in .env.local
cat frontend/.env.local

# Test backend directly
curl http://localhost:8000/health

# Check CORS β€” backend must allow frontend origin
# Edit BACKEND_CORS_ORIGINS in .env
```

### WebSocket not connecting
```bash
# Check browser console for WS errors
# Verify backend is running on correct port
# Check Nginx config if using reverse proxy (ws:// upgrade headers)
```

### AI responses not working
```bash
# Check which backend is active
curl http://localhost:8000/api/status

# If ai_available: false, check your API keys
# For Ollama: ensure it's running with: ollama serve
# For Groq: verify key at https://console.groq.com
```

### Database issues
```bash
# Force SQLite (no PostgreSQL needed)
# In .env: USE_SQLITE=true

# Re-seed database
python app/scripts/seed_demo.py

# Check DB type
curl http://localhost:8000/api/status | python -m json.tool
```