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| 1 |
+
<!-- ARCHITECTURE.md -->
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| 2 |
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| 3 |
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# Shasha AI — Architecture Overview
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A high‑level view of the components and data flow in the Shasha AI code‑generation platform.
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## 1. Core Layers
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### 1.1 Frontend (Gradio UI)
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- **app.py**: Defines the Gradio Blocks UI — input panels (prompt, file, website, image), model selector, language selector, buttons, and output panes (Code, Preview, History).
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- **static/**: Holds `index.html`, `index.js`, `style.css` for any transformers.js demos and custom styling.
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- **assets/**: Images, logos, and other static assets.
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### 1.2 Backend Services
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#### 1.2.1 Inference Routing
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- **hf_client.py**
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- `get_inference_client(model_id, provider)`
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- Wraps HF/OpenAI/Gemini/Groq providers into a unified interface.
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- Automatically selects/falls back based on model prefix and available credentials.
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- **inference.py**
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- `chat_completion(...)` and `stream_chat_completion(...)`
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- Encapsulates request/response logic, streaming vs. blocking, and error handling.
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#### 1.2.2 Model Registry
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- **models.py**
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- `ModelInfo` dataclass & `AVAILABLE_MODELS` registry
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- Central source of truth for supported models, identifiers, descriptions, and default providers.
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- Helper `find_model()` for lookup by name or ID.
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#### 1.2.3 Prompt & History Management
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- **constants.py**
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- System prompts for HTML/transformers.js modes, search/replace tokens, Gradio language support.
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- **utils.py**
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- `history_to_messages()`, `remove_code_block()`, multimodal image encoding, parsing transformers.js outputs, search/replace utilities.
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#### 1.2.4 Deployment & Project Import
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- **deploy.py**
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- `send_to_sandbox()` → live HTML preview in iframe
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- `load_project_from_url()` → import existing HF Spaces (app.py/index.html)
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- `deploy_to_spaces*()` → create/update HF Space via Hub API
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## 2. Extensions & Plugins
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- **plugins.py** (future)
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- Plugin discovery/loading for community‑created extensions (e.g., DB runners, snippet libraries).
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- **auth.py** (future)
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- OAuth flows for GitHub, Google Drive, Slack — enable private file loading.
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## 3. Project Structure
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.
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├── app.py # Gradio application
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├── constants.py # System prompts & app‑wide constants
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├── hf_client.py # Inference client factory
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├── models.py # Model registry & metadata
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├── inference.py # Chat completion wrappers
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├── utils.py # Helpers: history, code parsing, multimodal
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├── deploy.py # Sandbox preview & HF Spaces import/deploy
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├── plugins.py # (planned) Plugin architecture
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├── auth.py # (planned) OAuth integrations
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├── notebooks/ # Demo Jupyter notebooks
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├── tests/ # pytest suites & UI smoke tests
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├── ci.yaml # CI pipeline for lint/test/deploy
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├── docs/
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│ ├── QUICKSTART.md
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│ ├── ARCHITECTURE.md
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│ └── API_REFERENCE.md
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└── static/ # transformers.js demos & CSS/JS assets
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yaml
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Copy
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Edit
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## 4. Data Flow
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1. **User Interaction**: User enters prompt / uploads file / selects model & language.
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2. **Preprocessing**:
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- File → `extract_text_from_file()`
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- Website → `extract_website_content()`
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- Image → `process_image_for_model()`
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3. **Message Assembly**:
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- System prompt + history → OpenAI‑style message list
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- Enhanced via optional web search (Tavily)
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4. **Inference Call**:
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- `get_inference_client()` → select provider & billing
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- `chat_completion()` or streaming
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5. **Postprocessing**:
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- Parse code blocks / transformers.js multi‑file output
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- Apply search/replace to existing code if editing
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6. **UI Update**:
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- Code pane
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- Live preview iframe (`send_to_sandbox`)
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- Chat history pane
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---
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```markdown
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<!-- API_REFERENCE.md -->
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# Shasha AI — API Reference
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This document describes the public interfaces provided by each module.
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---
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## `hf_client.py`
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### `get_inference_client(model_id: str, provider: str = "auto") → InferenceClient`
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Creates and configures a Hugging Face Hub client for chat completions.
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- **model_id**: HF model ID or external provider prefix (e.g. `"openai/gpt-4"`, `"gemini/pro"`, `"moonshotai/Kimi-K2-Instruct"`).
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- **provider**: Override provider; one of `auto`, `groq`, `openai`, `gemini`, `fireworks`.
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- **Returns**: `InferenceClient` instance with proper API key & billing target.
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---
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## `models.py`
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### `ModelInfo`
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Dataclass representing model metadata.
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- **name**: Human‑readable model name.
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- **id**: Model identifier for API calls.
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- **description**: Short description.
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- **default_provider**: Preferred inference provider.
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### `AVAILABLE_MODELS: List[ModelInfo]`
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Registry of all supported models.
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### `find_model(identifier: str) → Optional[ModelInfo]`
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Lookup model by name (case‑insensitive) or ID.
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---
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## `inference.py`
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### `chat_completion(model_id: str, messages: List[Dict[str, str]], provider: str = None, max_tokens: int = 4096) → str`
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Synchronously sends a chat completion request.
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- **messages**: List of `{"role": "...", "content": "..."}`
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- **provider**: Optional override; defaults to model’s `default_provider`.
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- **Returns**: Response content string.
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### `stream_chat_completion(model_id: str, messages: List[Dict[str, str]], provider: str = None, max_tokens: int = 4096) → Generator[str]`
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Streams a chat completion, yielding incremental content chunks.
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---
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## `utils.py`
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### `history_to_messages(history: History, system: str) → List[Dict]`
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Converts internal history list to OpenAI‑style messages.
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### `remove_code_block(text: str) → str`
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Strips markdown code fences from AI output and returns raw code.
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### `parse_transformers_js_output(text: str) → Dict[str, str]`
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Extracts `index.html`, `index.js`, `style.css` from a multi‑file markdown output.
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### `format_transformers_js_output(files: Dict[str, str]) → str`
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Formats a dict of file contents into a single combined string with section headers.
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*(Other utilities: multimodal image processing, search/replace, history rendering)*
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---
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## `deploy.py`
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### `send_to_sandbox(code: str) → str`
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Wraps HTML code in a base64 data‑URI iframe for live preview.
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### `load_project_from_url(url: str) → Tuple[str, str]`
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Fetches `app.py` or `index.html` from a public HF Space URL.
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*(Also: HF Spaces deploy helpers: `deploy_to_spaces()`, `deploy_to_spaces_static()`, `deploy_to_user_space()`)*
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---
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## `app.py`
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### `generation_code(query, image, file, website_url, _setting, _history, _current_model, enable_search, language, provider) → Tuple[str, History, str, List[Dict]]`
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Main generation handler bound to the “Generate” button.
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- **Returns**:
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1. `code_str`: Generated (or edited) source code
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2. `new_history`: Updated prompt/response history
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3. `sandbox_html`: Live preview HTML iframe string
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4. `chat_msgs`: Chatbot‑style history for the UI
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---
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_For more examples, see the Jupyter notebooks in_ `notebooks/` and the quick‑start guide in `QUICKSTA
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