Hugging Face's logo

Spaces:
nivakaran
/
modelx
Running

App Files Community
modelx
File size: 2,776 Bytes 
b4856f1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
# 🚀 Roger Quick Start Guide

## Prerequisites
- Python 3.11+
- Node.js 18+
- Groq API Key ([Get Free Key](https://console.groq.com))

## Installation & Setup

### 1. Install Python Dependencies
```bash
pip install -r requirements.txt
```

### 2. Configure Environment
```bash
# Copy template
cp .env.template .env

# Edit .env and add your GROQ_API_KEY
# GROQ_API_KEY=your_key_here
```

### 3. Start Backend
```bash
python main.py
```

Wait for initialization logs:
```
[StorageManager] Initializing multi-database storage system
[SQLiteCache] Initialized at data/cache/feeds.db
[ChromaDB] Initialized collection: Roger_feeds
[CombinedAgentNode] Initialized with production storage layer
```

### 4. Start Frontend (New Terminal)
```bash
cd frontend
npm install
npm run dev
```

### 5. Access Dashboard
Open: http://localhost:3000

---

## 🎯 What to Expect

### First 60 Seconds
- System initializes 6 domain agents
- Begins scraping 47+ data sources
- Deduplication pipeline activates

### After 60-120 Seconds
- First batch of events appears on dashboard
- Risk metrics start calculating
- Real-time WebSocket connects

### Live Features
- ✅ Real-time intelligence feed
- ✅ Risk vs Opportunity classification
- ✅ 3-tier deduplication (SQLite + ChromaDB + Neo4j\*)
- ✅ CSV exports in `data/feeds/`
- ✅ Operational Risk Radar metrics

\*Neo4j optional - requires Docker

---

## 🐛 Troubleshooting

### "ChromaDB not found"
```bash
pip install chromadb sentence-transformers
```

### "No events appearing"
- Wait 60-120 seconds for first batch
- Check backend logs for errors
- Verify GROQ_API_KEY is set correctly

### Frontend can't connect
```bash
# Verify backend running
curl http://localhost:8000/api/status
```

---

## 📊 Production Features

### Storage Stats
```bash
curl http://localhost:8000/api/storage/stats
```

### CSV Exports
```bash
ls -lh data/feeds/
cat data/feeds/feed_$(date +%Y-%m-%d).csv
```

### Enable Neo4j (Optional)
```bash
# Start Neo4j with Docker
docker-compose -f docker-compose.prod.yml up -d neo4j

# Update .env
NEO4J_ENABLED=true

# Restart backend
python main.py

# Access Neo4j Browser
open http://localhost:7474
# Login: neo4j / Roger2024
```

---

## 🏆 Demo for Judges

**Show in this order**:
1. Live dashboard (http://localhost:3000)
2. Terminal logs showing deduplication stats
3. Neo4j graph visualization (if enabled)
4. CSV exports in data/feeds/
5. Storage API: http://localhost:8000/api/storage/stats

**Key talking points**:
- "47+ data sources, 6 domain agents running in parallel"
- "3-tier deduplication: SQLite for speed, ChromaDB for intelligence"
- "90%+ duplicate reduction vs 60% with basic hashing"
- "Production-ready with persistent storage and knowledge graphs"

---

**Ready to win! 🏆**