README.md

---
title: Intelligent Documentation Generator Agent
colorFrom: blue
colorTo: purple
sdk: gradio
python_version: 3.11
sdk_version: 5.49.1
app_file: app.py
short_description: Generate documentation and chat with code
tags:
- documentation
- code-assistant
- large-language-model
- fire-works-ai
models:
- accounts/fireworks/models/glm-4p6
pinned: false
---



# 🧠 Intelligent Documentation Generator Agent

Built with **GLM-4.6 on Fireworks AI** and **Gradio**

---

## 📘 Overview

The **Intelligent Documentation Generator Agent** automatically generates structured, multi-layer documentation and provides a chat interface to explore Python codebases.
This version is powered by **GLM-4.6** via the **Fireworks AI Inference API** and implemented in **Gradio**, offering a lightweight, interactive browser-based UI.

**Capabilities:**

* Analyze Python files from uploads, pasted code, or GitHub links
* Generate consistent, well-structured documentation (overview, API breakdown, usage examples)
* Chat directly with your code to understand logic, dependencies, and optimization opportunities

---

## ⚙️ Architecture

```
User (Upload / Paste / GitHub)
          │
          ▼
   Gradio UI (Tabs)
 ┌─────────────────────┐
 │  Documentation Tab  │──► Fireworks GLM-4.6 → Markdown Docs
 │  Chat Tab            │──► Fireworks GLM-4.6 → Q&A Responses
 └─────────────────────┘
```

---

## 🧩 Core Features

### 📄 Documentation Generator

* Input via:

  * Pasted Python code
  * Uploaded `.py` file
  * GitHub file link (supports automatic conversion to raw URL)
* Produces:

  * Overview and purpose
  * Key functions/classes with signatures
  * Dependencies and relationships
  * Example usage and improvement suggestions
* Outputs documentation in Markdown

### 💬 Code Chatbot

* Conversational Q&A with the analyzed code
* References exact functions and dependencies
* Maintains interactive chat history using Gradio’s `Chatbot` component
* Uses the same GLM-4.6 model context for accurate answers

---

## 🧱 Tech Stack

| Layer             | Technology                                      |
| ----------------- | ----------------------------------------------- |
| **Model**         | [GLM-4.6](https://fireworks.ai) on Fireworks AI |
| **UI Framework**  | [Gradio](https://gradio.app)                    |
| **Language**      | Python 3.9+                                     |
| **HTTP Requests** | `requests`                                      |
| **Deployment**    | Localhost / Containerized environments          |

---

## 🚀 Installation

### 1. Clone the Repository

```bash
git clone https://github.com/<your-username>/intelligent-doc-agent.git
cd intelligent-doc-agent
```

### 2. Install Dependencies

```bash
pip install gradio requests
```

### 3. Configure Fireworks API Key

Set your API key as an environment variable:

```bash
export FIREWORKS_API_KEY="your_fireworks_api_key"
```

> Alternatively, enter your API key directly in the UI when prompted.

### 4. Run the Application

```bash
python app_gradio.py
```

Then visit **[http://127.0.0.1:7860](http://127.0.0.1:7860)** in your browser.

---

## 💡 Usage Guide

### 🧠 Generate Documentation

1. Open the **📄 Generate Documentation** tab.
2. Choose an input mode:

   * Paste code into the text area
   * Upload a `.py` file
   * Enter a GitHub file link (e.g., `https://github.com/.../file.py`)
3. Click **🚀 Generate Documentation** to process your file.
4. View formatted Markdown output instantly.

### 💬 Chat with Code

1. Switch to the **💬 Chat with Code** tab.
2. Ask questions about your code (e.g., “What does this function do?” or “How can I improve performance?”).
3. The model responds contextually, referencing the uploaded file.

---

## 🧠 Model Integration Example

```python
payload = {
  "model": "accounts/fireworks/models/glm-4p6",
  "max_tokens": 4096,
  "temperature": 0.6,
  "messages": messages
}
response = requests.post(
  "https://api.fireworks.ai/inference/v1/chat/completions",
  headers={"Authorization": f"Bearer {FIREWORKS_API_KEY}"},
  data=json.dumps(payload)
)
print(response.json()["choices"][0]["message"]["content"])
```

---

## 📦 Project Structure

```
.
├── app.py          # Main Gradio interface
├── README.md              # Project documentation
└── requirements.txt       # Dependencies (gradio, requests)
```

---

## 🔮 Future Enhancements

* **Multi-file repository analysis** with hierarchical context summarization
* **Semantic vector store** (Chroma / Pinecone) for persistent knowledge retrieval
* **Multi-agent orchestration** using LangGraph or MCP protocols
* **Continuous documentation updates** via Git hooks or CI/CD pipelines

---

## 🧾 Example

**Input**

```python
def calculate_mean(numbers):
    return sum(numbers) / len(numbers)
```

**Output**

```markdown
### Function: calculate_mean
Computes the arithmetic mean of a numeric list.

**Parameters:**
- numbers (list): Sequence of numbers to average.

**Returns:**
- float: Mean of the list.

**Usage Example:**
>>> calculate_mean([1, 2, 3, 4])
2.5
```

**Chat Example**

> “How can I modify this to avoid division by zero errors?”

---

## 🔐 Best Practices

* Use **raw GitHub links** (`https://raw.githubusercontent.com/...`) for accurate file fetches.
* Limit input size (~4k tokens) for optimal latency and context accuracy.
* Keep your **API key** private — never commit it in source files.

---

## 🧭 License

Released under