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---
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# docs/QUICKSTART.md
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````markdown
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# Quickstart
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This guide will get you up and running with **AnyCoder** in minutes.
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## 1. Clone the Repository
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```bash
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git clone https://github.com/your-org/anycoder.git
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cd anycoder
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````
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## 2. Install Dependencies
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Make sure you have Python 3.9+ installed.
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```bash
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pip install --upgrade pip
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pip install -r requirements.txt
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```
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## 3. Set Environment Variables
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```bash
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export HF_TOKEN=<YOUR_HUGGINGFACE_TOKEN>
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export OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
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export GEMINI_API_KEY=<YOUR_GEMINI_API_KEY>
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```
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## 4. Run the App Locally
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```bash
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python app.py
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```
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Open [http://localhost:7860](http://localhost:7860) in your browser to access the UI.
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## 5. Explore Features
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* **Model selector**: Choose from Groq, OpenAI, Gemini, Fireworks, and HF models.
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* **Input**: Enter prompts, upload files or images for context.
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* **Generate**: View code, preview, and conversation history.
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---
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# docs/API\_REFERENCE.md
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````markdown
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# API Reference
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This document describes the public Python modules and functions available in AnyCoder.
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## `models.py`
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### `ModelInfo` dataclass
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```python
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@dataclass
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class ModelInfo:
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name: str
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id: str
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description: str
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default_provider: str = "auto"
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````
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### `AVAILABLE_MODELS: List[ModelInfo]`
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A list of supported models with metadata.
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### `find_model(identifier: str) -> Optional[ModelInfo]`
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Lookup a model by name 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: Optional[str]=None, max_tokens: int=4096) -> str`
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Send a one-shot chat completion request.
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### `stream_chat_completion(model_id: str, messages: List[Dict[str,str]], provider: Optional[str]=None, max_tokens: int=4096) -> Generator[str]`
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Stream partial generation results.
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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 an HF InferenceClient with provider routing logic.
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---
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# docs/ARCHITECTURE.md
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```markdown
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# Architecture Overview
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Below is a high-level diagram of AnyCoder's components and data flow:
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```
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```
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+------------+
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| User |
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+-----+------+
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|
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v
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+---------+----------+
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| Gradio UI (app.py)|
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+---------+----------+
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+------------------------+------------------------+
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| | |
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v v v
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models.py inference.py plugins.py
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```
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(model registry) (routing & chat\_completion) (extension points)
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\| | |
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+---------------------+ +------------------------+
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|
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v
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hf\_client.py deploy.py
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(HF/OpenAI/Gemini/etc routing) (HF Spaces integration)
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```
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- **UI Layer** (`app.py` + Gradio): handles inputs, outputs, and state.
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- **Model Registry** (`models.py`): metadata-driven list of supported models.
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- **Inference Layer** (`inference.py`, `hf_client.py`): abstracts provider selection and API calls.
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- **Extensions** (`plugins.py`): plugin architecture for community or custom integrations.
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- **Deployment** (`deploy.py`): Helpers to preview in an iframe or push to Hugging Face Spaces.
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This separation ensures modularity, testability, and easy extensibility.
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```
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