⚡ JarvisChat

A lightweight Ollama coding companion that runs on Python 3.13

JarvisChat is a single-file FastAPI application that provides a clean, responsive web interface for Ollama. It features persistent memory, automatic web search when the model is uncertain, and real-time token tracking.

Features

• Persistent Profile/Memory — Your context is injected into every conversation automatically
• System Prompt Presets — Switch between coding assistant, sysadmin, general, or custom modes
• Streaming Chat — Real-time token streaming with conversation history
• Model Switching — Hot-swap between all installed Ollama models
• Web Search Integration — SearXNG kicks in automatically when the model is uncertain (perplexity-based)
• Weather Queries — Direct wttr.in integration for weather questions
• Token Thermometer — Visual context usage bar with live updates as you type
• Perplexity & Speed Badges — See model confidence (PPL) and tokens/sec on each response
• Copy-to-Clipboard — One-click copy on all code blocks
• Dark Theme — Easy on the eyes for long coding sessions

Architecture

Browser ◄──► app.py (FastAPI) ◄──► Ollama (LLM)
                    │
                    ▼ (when uncertain)
               SearXNG (web search)

JarvisChat acts as middleware between your browser and Ollama. When the model's perplexity exceeds a threshold (default 15.0) or it refuses to answer, JarvisChat automatically queries SearXNG, injects the results, and re-prompts the model.

This is NOT training — SearXNG is only used at runtime as a fallback for uncertain responses.

Requirements

• Python 3.11+ (tested on 3.13)
• Ollama running locally (default: localhost:11434)
• SearXNG (optional, for web search — default: localhost:8888)
• ROCm (optional, for AMD GPU stats — rocm-smi must be in PATH)

Installation

# Clone or download app.py
git clone https://github.com/llamachileshop-code/313_webui.git
cd 313_webui

# Create virtual environment (recommended)
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install fastapi httpx uvicorn psutil

# Run
python app.py
# or
uvicorn app:app --host 0.0.0.0 --port 8080

Open http://localhost:8080 in your browser.

Note: If running as a systemd service with a venv, install dependencies using the venv pip directly:

/opt/jarvischat/venv/bin/pip install fastapi httpx uvicorn psutil

Running as a Service

Important: Although JarvisChat is a single-file Python application, it's designed to run as a persistent service alongside Ollama — not as a one-off script. Both services should start on boot.

systemd Service (recommended)

Create /etc/systemd/system/jarvischat.service:

[Unit]
Description=JarvisChat - Ollama Web UI
After=network.target ollama.service
Wants=ollama.service

[Service]
Type=simple
User=your-username
WorkingDirectory=/path/to/313_webui
ExecStart=/usr/bin/python3 app.py
Restart=on-failure
RestartSec=5

[Install]
WantedBy=multi-user.target

Then enable and start:

sudo systemctl daemon-reload
sudo systemctl enable jarvischat
sudo systemctl start jarvischat

Verify Both Services

# Check Ollama
systemctl status ollama

# Check JarvisChat
systemctl status jarvischat

# View JarvisChat logs
journalctl -t jarvischat -f

Configuration

Edit these constants at the top of app.py:

VERSION = "1.3.1"
OLLAMA_BASE = "http://localhost:11434"
SEARXNG_BASE = "http://localhost:8888"
DEFAULT_MODEL = "deepseek-coder:6.7b"
PERPLEXITY_THRESHOLD = 15.0  # Higher = less likely to trigger search

Database

JarvisChat uses SQLite (jarvischat.db in the same directory as app.py):

Table	Purpose
conversations	Chat sessions with model and timestamps
messages	Individual messages with role and content
system_presets	Saved system prompt presets
profile	Your persistent memory/context
settings	App settings (search/profile toggles, default model)

Logging

JarvisChat logs to syslog via journald:

# Follow live logs
journalctl -t jarvischat -f

# View last 100 entries
journalctl -t jarvischat -n 100

Token Thermometer

The vertical bar next to the input shows your context usage in real-time:

• Green — Plenty of room
• Yellow — 70%+ used
• Red — 90%+ used (approaching limit)

The count includes: profile + preset + conversation history + current input. Context size is fetched from Ollama when you switch models.

Search Flow

1. User sends message → Ollama streams response with logprobs
2. JarvisChat calculates perplexity from logprobs
3. If perplexity > 15.0 OR refusal patterns detected:
   • Yield {searching: True} to show spinner
   • Query SearXNG (or wttr.in for weather)
   • Inject results into context
   • Re-prompt Ollama
4. If model still refuses, format raw search results directly
5. Clean hedging phrases from response
6. Yield final response with PPL and t/s badges

API Endpoints

Endpoint	Method	Description
/	GET	Web UI
/api/models	GET	List Ollama models
/api/ps	GET	Running models
/api/show	POST	Model info (context size)
/api/stats	GET	System stats (CPU, memory, GPU, VRAM)
/api/chat	POST	Stream chat (SSE)
/api/conversations	GET/DELETE	List/delete all conversations
/api/conversations/{id}	GET/DELETE	Get/delete conversation
/api/profile	GET/PUT	Get/update profile
/api/presets	GET/POST	List/create presets
/api/presets/{id}	PUT/DELETE	Update/delete preset
/api/settings	GET/PUT	App settings
/api/search/status	GET	SearXNG availability

Screenshots

(Add your own screenshot here)

TODO

Active

1. Mass-delete conversation history ✓ (v1.3.0)

2. Verify SearXNG and Docker services persist across reboots

   • Expand refusal patterns: "As an AI model", "based on my training data", "I don't have the capability"

3. Input trigger: search+ prefix

   • Strip prefix, query SearXNG directly, Ollama summarizes
   • Raw results in expandable div (not tooltip)

4. Add profile.example.md

   • Recommended default profile with anti-bullshit rules (no "As an AI", no OpenAI mentions)

Backlog

5. Conversation search/filter by keyword
6. Export conversation to markdown/text
7. Keyboard shortcuts (Ctrl+N new chat, Ctrl+Enter send)
8. Token count estimate before sending ✓ (v1.2.9)
9. Model info display — context length, VRAM usage from Ollama /api/ps
10. Retry button on assistant messages
11. Source links — clickable links when search used
12. Allow conversation renaming
13. Multiple profiles — coding/sysadmin/general
14. Auto-generate conversation tags (client-side KWIC, top 5, filterable badges)
15. Image input support
    • Pull vision model (llava, llama3.2-vision, etc.)
    • Frontend: file input / drag-drop, base64 encode
    • Backend: pass images array to Ollama /api/chat

Version History

Version	Changes
1.3.1	System stats panel (CPU, memory, GPU, VRAM) in sidebar
1.3.0	Delete all conversations button
1.2.9	Token thermometer with live context tracking
1.2.8	Logo in sidebar, llama emoji tagline
1.2.7	Tokens per second (t/s) badge on responses
1.2.6	wttr.in weather integration, improved search extraction
1.2.5	SearXNG infoboxes/answers, smarter query building
1.2.4	Perplexity badges, hedging cleanup
1.2.3	SearXNG integration with perplexity-based triggering
1.2.0	System prompt presets, settings persistence
1.1.0	Profile memory, model switching
1.0.0	Initial release

License

MIT

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A Note from Gramps

I named my AI machine "jarvis" after the AI assistant in Iron Man (2008) — because it's an awesome name. When I started building a local coding companion to talk to it, "JarvisChat" just made sense.

This project is in active development. Eventually it'll get packaged up as a Docker thing, but for now while I'm iterating fast, a single-file Python service does the job.

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Built with 🦙 by Gramps at the Llama Chile Shop