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README.md

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+# 🧠 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          |
+
 ---
-title: Intelligent Documentation Generator Agent
-emoji: 😻
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: Intelligent Documentation Generator Agent
+
+## 🚀 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.
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 💡 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_gradio.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