# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Project Overview

Tiny Scribe is a transcript summarization tool with two interfaces:
1. **CLI tool** (`summarize_transcript.py`) - Standalone script for local use with SYCL/CPU acceleration
2. **Gradio web app** (`app.py`) - HuggingFace Spaces deployment with streaming UI

Both use llama-cpp-python to run GGUF quantized models (Qwen3, ERNIE, Granite, Gemma, etc.) and convert output to Traditional Chinese (zh-TW) via OpenCC.

## Development Commands

### Running the CLI

```bash
# Basic usage (default model: Qwen3-0.6B Q4_0)
python summarize_transcript.py -i ./transcripts/short.txt

# Specify model (format: repo_id:quantization)
python summarize_transcript.py -m unsloth/Qwen3-1.7B-GGUF:Q2_K_L

# Force CPU-only (disable SYCL)
python summarize_transcript.py -c
```

### Running the Gradio App

```bash
# Local development
pip install -r requirements.txt
python app.py
# Opens at http://localhost:7860
```

### Testing

No test suite exists in the root project. To test llama-cpp-python submodule:

```bash
cd llama-cpp-python
pip install ".[test]"
pytest tests/test_llama.py -v

# Single test
pytest tests/test_llama.py::test_function_name -v
```

### Docker Deployment

```bash
# Build locally
docker build -t tiny-scribe .

# Run
docker run -p 7860:7860 tiny-scribe
```

## Architecture

### Two Execution Paths

**CLI Path:**
```
User → summarize_transcript.py → Llama.from_pretrained() → GGUF model
                                      ↓
                                  Stream tokens → OpenCC (s2twp) → stdout
                                      ↓
                    parse_thinking_blocks() → thinking.txt + summary.txt
```

**Gradio Path:**
```
User upload → Gradio File → app.py:summarize_streaming()
                              ↓
                         Llama.create_chat_completion(stream=True)
                              ↓
                    Token-by-token yield → OpenCC → Two textboxes:
                              ↓                      - Thinking (raw stream)
                    parse_thinking_blocks()         - Summary (parsed output)
```

### Key Differences

| Feature | CLI (`summarize_transcript.py`) | Gradio (`app.py`) |
|---------|--------------------------------|-------------------|
| Model loading | On-demand per run | Global singleton (cached) |
| Model selection | CLI argument `repo_id:quant` | Dropdown with 10 models |
| Thinking tags | Supports both formats | Supports both formats + streaming |
| Reasoning toggle | Not supported | Qwen3: /think or /no_think |
| Inference settings | Hardcoded per run | Model-specific, dynamic UI |
| Output | Print to stdout + save files | Yield tuples for dual textboxes |
| GPU support | Configurable via `--cpu` flag | Hardcoded `n_gpu_layers=0` |
| Context window | 32K tokens | Per-model (32K-262K, capped at 32K) |

### Model Loading Pattern

Both scripts use `Llama.from_pretrained()` with HuggingFace Hub integration:

```python
llm = Llama.from_pretrained(
    repo_id="unsloth/Qwen3-0.6B-GGUF",
    filename="*Q4_K_M.gguf",      # Wildcard for flexible matching
    n_gpu_layers=0,                # 0=CPU, -1=all layers on GPU
    n_ctx=32768,                   # 32K context window
    seed=1337,                     # Reproducibility
    verbose=False,                 # Reduce log noise
)
```

**Important:** Always call `llm.reset()` after each completion to clear KV cache and ensure state isolation.

### Streaming Implementation

The Gradio app (`app.py`) implements real-time streaming with dual outputs:

1. **Raw stream** → `thinking_output` textbox (shows every token as generated)
2. **Parsed summary** → `summary_output` markdown (extracts content outside `<thinking>` tags)

Generator pattern:
```python
def summarize_streaming(...) -> Generator[Tuple[str, str], None, None]:
    for chunk in stream:
        content = chunk['choices'][0]['delta'].get('content', '')
        full_response += content

        # Show all tokens in thinking field
        current_thinking += content

        # Extract summary (content outside thinking tags)
        thinking_blocks, summary = parse_thinking_blocks(full_response)
        current_summary = summary

        # Yield both on every token
        yield (current_thinking, current_summary)
```

### Thinking Block Parsing

Models may wrap reasoning in special tags that should be separated from final output.

**Both versions now support both tag formats:**
- `<think>reasoning</think>` (common with Qwen models)
- `<thinking>reasoning</thinking>` (Claude-style)

Regex pattern:
```python
# Matches both <think> and <thinking> tags
pattern = r'<think(?:ing)?>(.*?)</think(?:ing)?>'
matches = re.findall(pattern, content, re.DOTALL)
thinking = '\n\n'.join(match.strip() for match in matches)
summary = re.sub(pattern, '', content, flags=re.DOTALL).strip()
```

The Gradio app also handles streaming mode with unclosed `<think>` tags for real-time display.

### Qwen3 Thinking Mode

Qwen3 models support a special "thinking mode" that generates `<think>...</think>` blocks for reasoning before the final answer.

**Implementation (llama.cpp/llama-cpp-python):**
- Add `/think` to system prompt or user message to enable thinking mode
- Add `/no_think` to disable thinking mode (faster, direct output)
- Most recent instruction takes precedence in multi-turn conversations

**Official Recommended Settings (from Unsloth):**

| Setting | Non-Thinking Mode | Thinking Mode |
|---------|------------------|---------------|
| Temperature | 0.7 | 0.6 |
| Top_P | 0.8 | 0.95 |
| Top_K | 20 | 20 |
| Min_P | 0.0 | 0.0 |

**Important Notes:**
- **DO NOT use greedy decoding** in thinking mode (causes endless repetitions)
- In thinking mode, model generates `<think>...</think>` block before final answer
- For non-thinking mode, empty `<think></think>` tags are purposely used

**Current Implementation:**
The Gradio app (`app.py`) implements this via:
- `enable_reasoning` checkbox (models with `supports_toggle: true`)
- Dynamic system prompt: `你是一個有助的助手，負責總結轉錄內容。{reasoning_mode}`
- Where `reasoning_mode = "/think"` or `/no_think"` based on toggle

### Chinese Text Conversion

All outputs are converted from Simplified to Traditional Chinese (Taiwan standard):

```python
from opencc import OpenCC
converter = OpenCC('s2twp')  # s2twp = Simplified → Traditional (Taiwan + phrases)
traditional = converter.convert(simplified)
```

Applied token-by-token during streaming to maintain real-time display.

## HuggingFace Spaces Deployment

The Gradio app is optimized for HF Spaces Free Tier (2 vCPUs):

- **Models**: 10 models available (100M to 1.7B parameters), default: Qwen3-0.6B Q4_K_M (~400MB)
- **Dockerfile**: Uses prebuilt llama-cpp-python wheel (skips 10-min compilation)
- **Context limits**: Per-model context windows (32K to 262K tokens), capped at 32K for CPU performance

See `DEPLOY.md` for full deployment instructions.

### Deployment Workflow

The `deploy.sh` script ensures meaningful commit messages:

```bash
./deploy.sh "Add new model: Gemma-3 270M"
```

The script:
1. Checks for uncommitted changes
2. Prompts for commit message if not provided
3. Warns about generic/short messages
4. Shows commits to be pushed
5. Confirms before pushing
6. Verifies commit message was preserved on remote

### Docker Optimization

The Dockerfile avoids building llama-cpp-python from source by using a prebuilt wheel:

```dockerfile
RUN pip install --no-cache-dir \
    https://huggingface.co/Luigi/llama-cpp-python-wheels-hf-spaces-free-cpu/resolve/main/llama_cpp_python-0.3.22-cp310-cp310-linux_x86_64.whl
```

This reduces build time from 10+ minutes to ~2 minutes.

## Git Submodule

The `llama-cpp-python/` directory is a Git submodule tracking upstream development:

```bash
# Initialize after clone
git submodule update --init --recursive

# Update to latest
cd llama-cpp-python
git pull origin main
cd ..
git add llama-cpp-python
git commit -m "Update llama-cpp-python submodule"
```

## Model Format

CLI model argument format: `repo_id:quantization`

Examples:
- `unsloth/Qwen3-0.6B-GGUF:Q4_0` → Searches for `*Q4_0.gguf`
- `unsloth/Qwen3-1.7B-GGUF:Q2_K_L` → Searches for `*Q2_K_L.gguf`

The `:` separator is parsed in `summarize_transcript.py:128-130`.

## Error Handling Notes

When modifying streaming logic:
- **Always** handle `'choices'` key presence in chunks
- **Always** check for `'delta'` in choice before accessing `'content'`
- Gradio error handling: Yield error messages in the summary field, keep thinking field intact
- File upload: Validate file existence and encoding before reading

## Model Registry

The Gradio app (`app.py:32-155`) includes a model registry (`AVAILABLE_MODELS`) with:

1. **Model metadata** (repo_id, filename, max context)
2. **Model-specific inference settings** (temperature, top_p, top_k, repeat_penalty)
3. **Feature flags** (e.g., `supports_toggle` for Qwen3 reasoning mode)

Each model has optimized defaults. The UI updates inference controls when model selection changes.

### Available Models

| Key | Model | Params | Max Context | Quant |
|-----|-------|--------|-------------|-------|
| `falcon_h1_100m` | Falcon-H1 100M | 100M | 32K | Q8_0 |
| `gemma3_270m` | Gemma-3 270M | 270M | 32K | Q8_0 |
| `ernie_300m` | ERNIE-4.5 0.3B | 300M | 131K | Q8_0 |
| `granite_350m` | Granite-4.0 350M | 350M | 32K | Q8_0 |
| `lfm2_350m` | LFM2 350M | 350M | 32K | Q8_0 |
| `bitcpm4_500m` | BitCPM4 0.5B | 500M | 128K | q4_0 |
| `hunyuan_500m` | Hunyuan 0.5B | 500M | 256K | Q8_0 |
| `qwen3_600m_q4` | Qwen3 0.6B | 600M | 32K | Q4_K_M |
| `falcon_h1_1.5b_q4` | Falcon-H1 1.5B | 1.5B | 32K | Q4_K_M |
| `qwen3_1.7b_q4` | Qwen3 1.7B | 1.7B | 32K | Q4_K_M |

### Adding a New Model

1. Add entry to `AVAILABLE_MODELS` in `app.py`:
```python
"model_key": {
    "name": "Human-Readable Name",
    "repo_id": "org/model-name-GGUF",
    "filename": "*Quantization.gguf",
    "max_context": 32768,
    "supports_toggle": False,  # For Qwen3 /think mode
    "inference_settings": {
        "temperature": 0.6,
        "top_p": 0.95,
        "top_k": 20,
        "repeat_penalty": 1.05,
    },
},
```

2. Set `DEFAULT_MODEL_KEY` to the new key if it should be default

## Common Modifications

### Changing the Default Model

**CLI:** Use `-m` argument at runtime

**Gradio app:** Change `DEFAULT_MODEL_KEY` in `app.py:157`

### Adjusting Context Window

**CLI:** Change `n_ctx` in `summarize_transcript.py:23`

**Gradio app:** The app dynamically calculates `n_ctx` based on input size and model limits. To change the global cap, modify `MAX_USABLE_CTX` in `app.py:29`.

Values:
- 32768 (current) = handles ~24KB text input
- 8192 = faster, lower memory, ~6KB text
- 131072 = very slow on CPU, ~100KB text

### GPU Acceleration

**CLI:** Remove `-c` flag (defaults to SYCL/CUDA if available)

**Gradio app:** Change `app.py:206`:
```python
n_gpu_layers=-1,  # Use all GPU layers
```

Note: HF Spaces Free Tier has no GPU access.