Update README.md

README.md

@@ -1,17 +1,116 @@
 ---
-title: SQL Assistant Prod
-emoji: 💬
+title: Autonomous SQL Agent
+emoji: 💬 
 colorFrom: yellow
 colorTo: purple
 sdk: gradio
-sdk_version: 5.42.0
+sdk_version: 4.19.2
 app_file: app.py
 pinned: false
+license: mit
+short_description: 'An autonomous SQL agent, based on Qwen 2.5 (fine-tuned). '
 hf_oauth: true
 hf_oauth_scopes:
 - inference-api
-license: mit
-short_description: 'An autonomous SQL agent, based on Qwen 2.5 (fine-tuned). '
+models:
+  - manuelaschrittwieser/Qwen2.5-1.5B-SQL-Assistant-Prod
+  - Qwen/Qwen2.5-1.5B-Instruct
+tags:
+  - agent
+  - sql
+  - text-to-sql
+  - qwen
+  - qlora
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+# Autonomous SQL Assistant Agent
+
+## 📋 System Overview
+
+The **Autonomous SQL Assistant** is a demonstrative AI agent designed to bridge the gap between natural language inquiries and database execution. Unlike standard "Text-to-SQL" generators that strictly output code, this agent operates within a closed-loop environment: it **generates** syntax, **executes** it against a live database, and **retrieves** the actual data for the user.
+
+The system is powered by **Qwen 2.5 (1.5B)**, fine-tuned via **QLoRA** on the `b-mc2/sql-create-context` dataset to ensure high fidelity in SQL syntax generation.
+
+**[🔗 View Source Code & Documentation](https://github.com/MANU-de/Autonomous-SQL-Agent)**
+
+---
+
+## 🏗️ Technical Architecture
+
+The application runs on a lightweight CPU environment and consists of three core components:
+
+### 1. The Inference Engine
+*   **Model:** [manuelaschrittwieser/Qwen2.5-1.5B-SQL-Assistant-Prod](https://huggingface.co/manuelaschrittwieser/Qwen2.5-1.5B-SQL-Assistant-Prod)
+*   **Optimization:** The model runs in full FP32 precision (CPU optimized).
+*   **Role:** Translates user intent (e.g., *"Who earns the most?"*) into executable SQLite syntax, utilizing the provided schema context.
+
+### 2. The Execution Sandbox
+*   **Database:** A transient **SQLite** instance.
+*   **Schema:** `employees` (id, name, department, salary, hire_date).
+*   **Lifecycle:** The database is re-instantiated upon every application restart/build to ensure a clean state for testing.
+
+### 3. The Agent Logic
+The `SQLAgent` class orchestrates the workflow:
+1.  **Ingest:** Receives natural language prompt.
+2.  **Contextualize:** Injects the `CREATE TABLE` schema into the system prompt.
+3.  **Generate:** produces the SQL query.
+4.  **Act:** Connects to the SQLite cursor, executes the query, and fetches results.
+5.  **Sanitize:** Catches execution errors (e.g., syntax errors) and reports them for debugging.
+
+---
+
+## 💻 Usage Instructions
+
+### Interface Guide
+The interface is a chat-based UI. You act as the user querying the HR database.
+
+*   **Input:** Type natural language questions regarding the `employees` table.
+*   **Output:** The agent provides a two-part response:
+    1.  **"Brain" (Internal Monologue):** The generated SQL query.
+    2.  **"Result" (Data):** The raw tuples returned from the database.
+
+### Example Queries
+Try copying these prompts to test the agent's capabilities:
+
+| Complexity | Query |
+| :--- | :--- |
+| **Simple** | *Show me the names of all employees in Sales.* |
+| **Conditional** | *Who earns more than 60000?* |
+| **Aggregation** | *Count how many employees work in the Engineering department.* |
+| **Logic** | *List employees hired after 2020.* |
+
+---
+
+## ⚙️ Local Reproduction
+
+To run this Space locally on your machine (requires Python 3.10+):
+
+1.  **Clone the Repository:**
+    ```bash
+    git clone https://huggingface.co/spaces/manuelaschrittwieser/sql-assistant-prod
+    cd sql-assistant-prod
+    ```
+
+2.  **Install Dependencies:**
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3.  **Launch Application:**
+    ```bash
+    python app.py
+    ```
+
+---
+
+## ⚠️ Limitations & Scope
+
+*   **Inference Latency:** As this demo runs on **CPU Basic** hardware, generating the SQL query may take 2-10 seconds depending on server load.
+*   **Sandbox Restrictions:** Database modifications (INSERT/DROP) are possible but will persist only until the application restarts.
+*   **Hallucinations:** While fine-tuned, the model may occasionally generate invalid SQL for highly complex queries not covered in the training distribution.
+
+---
+
+## 📜 License
+
+This project is open-source and available under the **MIT License**.