camera-llm 0.1.1

A local application to feed images/videos from a webcam into local LLMs for analysis and conversations.

Camera → Local LLM Inference App

Description

A Python desktop app that captures images or short video clips from a webcam, optionally crops them, and sends them in-memory (no external file needed to be saved previously) to a local LLM via LM Studio for conversational analysis.

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Project Structure (Main Files)

Camera_LLM_Inference/
└── camera_llm/
       ├── __init__.py
       ├── camera_thread.py         # QThread for OpenCV camera capture
       ├── chat_session.py          # ChatSession dataclass + JSON serialisation
       ├── chat_store.py            # Read/write chat sessions to chats/*.json
       ├── cli.py                   # Entry point handling CLI and launching app (app initialization)
       ├── llm_client.py            # OpenAI client → LM Studio, in-memory encode
       ├── main_window.py           # Main window that aids navigation among the 7 screens
       ├── styles.py                # Global stylesheet + design tokens     
       └── screens/
           ├── __init__.py
           ├── screen1_home.py          # Dashboard + saved chats panel
           ├── screen2_capture.py       # Live camera feed, capture/record controls
           ├── screen3_crop.py          # Rubber-band crop with dimming overlay
           ├── screen4_model_select.py  # LM Studio URL + model dropdown
           ├── screen5_chat.py          # Chatbot with streaming, thumbnails, bubbles
           ├── screen6_save.py          # Name & save the session
           └── screen7_done.py          # Confirmation + auto-redirect home
└── chats/                           # Auto-created at runtime for saved sessions
├── pyproject.toml                   # Package metadata + entry points
├── requirements.txt                 # pip dependencies

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How to Run

cd "c:\Users\kurei\Documents\Machine_Deep Learning\Camera_LLM_Inference"
pip install -e .
camera-llm run

Prerequisites

1. Webcam connected or IP camera with reachable IP address (e.g. http://[IP_ADDRESS])
2. LM Studio running with a vision model loaded (e.g. LLaVA, Qwen-VL)
3. LM Studio local server started (default: http://localhost:1234)

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Optional (download distribution zip file)

Alternatively, you can Download the Distribution Zip File to Run as Standalone Application. This saves you the time of downloading the repo and installing the other dependencies, but it also takes around 2.5 GB of disk space (in addition to less frequent updates). Once downloaded, unzip the file then navigate to "\CameraLLMInference\CameraLLMInference.exe" to run the application.

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User Flow

graph TD;
    S1["Screen 1: Home"] -->|Image| S2I["Screen 2: Camera (Image)"];
    S1 -->|Video| S2V["Screen 2: Camera (Video)"];
    S2I -->|Capture| S3["Screen 3: Crop"];
    S2V -->|Stop recording| S3;
    S3 -->|Use Full / Apply Crop| S4["Screen 4: Model Select"];
    S4 -->|Analyse| S5["Screen 5: Chat"];
    S5 -->|Finish| S6["Screen 6: Save?"];
    S6 -->|Save / Skip| S7["Screen 7: Done"];
    S7 -->|3s auto| S1;
    S1 -->|Open saved chat| S5P["Screen 5: Chat (past chat)"];
    S5P -->|Continue Chatting| S5;

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Change Camera Feed Guide

1. If you are using a standard USB webcam, you can just type 0, 1, 2, etc. and hit Enter. It will connect as a regular USB camera.
2. If you want to use a smartphone, download a free app like IP Webcam (Android) or similar apps for iOS that broadcast your camera over WiFi. Start the server to use as a WiFi camera
3. Note the URL that will be set (ex: http://192.168.1.100:8080).
4. Paste that exact URL into the 'Camera' text box and press Enter.

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Key Design Decisions

Decision	Choice	Rationale
GUI framework	PySide6	Robust threading (QThread), rich widget set, no licensing issues
Camera	OpenCV in QThread	Non-blocking — GUI stays responsive at 30 fps
In-memory encoding	cv2.imencode → base64	No file ever touches disk; data goes straight to the LLM API
Video → LLM	Sample up to 8 frames	Local VLMs don't accept video — sending evenly-spaced frames approximates it
LLM API	openai client → localhost:1234	LM Studio is OpenAI-compatible; swapping to a cloud provider later is trivial
Chat persistence	JSON files in chats/	Simple, portable, human-readable

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Features

• 7 Screens (Home, Capture, Crop, Model Select, Chat, Save, Done)
• Image and Video Capture (With Rotate Feed Capability)
• LLM Chat with Streaming
• Save/Load Chat Sessions (Chats are listed with the last modified date and time)
• Model Selection (LLM URL + Model Dropdown)
• Delete or Rename Saved Chats

[!CAUTION] Remember to load the same model that was used when first starting the chat (referenced in the title)

[!NOTE] The video input feature works by sending multiple frames from the video to the LLM in order to process as multiple images. The multiple frames (images) are sent as a panel (chained images) so the LLM can interpret it as one image (full context). This is done primarily due to employing a local LLM.

What Was Verified

• ✅ All dependencies install cleanly
• ✅ All module imports resolve without errors
• ✅ App launches and renders Screen 1 correctly
• ✅ Fixed pyqtdarktheme API for v0.1.7 (load_stylesheet instead of setup_theme)

Physical Testing Passed

• 📷 Camera feed with a physical webcam
• 🎬 Video recording and frame sampling
• 🤖 End-to-end LLM chat (requires LM Studio with a vision model running)
• 💾 Save/load chat sessions
• 📱 Verify smartphone can be used as webcam (App used in Testing: IP Webcam for Android)

⏳ In Progress

• More robust UI

[!IMPORTANT] Some dummy chats are present already under the Saved Chats section (chats/ folder). This was collected during testing and left to provide various samples. As such, they can be safely deleted.