Docs: Polish README user guide

README.md

@@ -3,7 +3,7 @@
 ![Demo](demo_thumb.png)
 
 ## Overview
-Annotation Assistant is a state-of-the-art **Vision-Language Object Detection** tool. It combines the power of **Qwen-VL (4B)** with a premium, user-friendly interface to make labeled data creation effortless.
+Annotation Assistant is a state-of-the-art **Vision-Language Object Detection** tool. It combines the power of **Qwen-VL (4B)** with a user-friendly interface to make labeled data creation effortless.
 
 Unlike standard detection tools, this assistant is **conversational**. You can refine detections naturally (e.g., *"Also find the cup"*), and the AI intelligently merges new findings with existing ones.
 
@@ -15,25 +15,37 @@ The Assistant remembers what it has already found.
 *   **Example**: If you say *"Find the laptop"* and then *"Find the remaining objects"*, it understands what "remaining" means because it knows the laptop is already detected.
 
 ### 🎯 **Smart Refinement Logic**
-We implemented a custom **Weighted Merge Algorithm** to handle updates:
+I implemented a custom **Weighted Merge Algorithm** to handle updates:
 *   **Refinement**: If you draw a better box for `"shirt"` over an existing one (>80% overlap), it **replaces** the old one.
-*   **distinct Objects**: If you seek a second `"shirt"` elsewhere (low overlap), it **adds** it as a new object.
+*   **Distinct Objects**: If you seek a second `"shirt"` elsewhere (low overlap), it **adds** it as a new object.
 *   Result: NO duplicate ghost boxes, NO accidental deletions.
 
 ### 👁️ **Explainable AI (Reasoning)**
 Don't just trust the box. The Assistant provides a **Reasoning Stream** explaining *why* it detected an object.
 *   *Example*: "Detected silver laptop due to distinct Apple logo and metallic finish."
 
-### 🎨 **Premium "Hero" Interface**
-*   **Single-Column Layout**: Your image takes center stage.
-*   **Dynamic Resizing**: Use the slider to scale the view from 300px to 1500px without losing layout structure.
-*   **Visuals**: Deep Space gradient theme, glassmorphism metrics, and auto-centering.
-
-## Quick Start
-1.  **Upload**: Drag & Drop your image into the central hub.
-2.  **Prompt**: Type what you're looking for (e.g., *"Find all branded items"*).
-3.  **Refine**: Chat with the AI to fix mistakes or add more items.
-4.  **Download**: Export your data as **COCO JSON** or download a **ZIP of cropped images**.
+## How to Run
+
+### ☁️ Option 1: Google Colab (Recommended for Free GPU)
+1.  Open the `Colab_Runner.ipynb` file in Google Colab.
+2.  Upload `app.py`, `utils.py`, and `requirements.txt` to the Colab files area.
+3.  Add your **Ngrok Authtoken** in the designated cell.
+4.  Run all cells. The app will launch via a public URL.
+
+### 💻 Option 2: Local System (Requires GPU)
+1.  **Clone the Repo**:
+    ```bash
+    git clone https://github.com/devsingh02/Pixel-Prompt-Annotator.git
+    cd Pixel-Prompt-Annotator
+    ```
+2.  **Install Dependencies**:
+    ```bash
+    pip install -r requirements.txt
+    ```
+3.  **Run the App**:
+    ```bash
+    streamlit run app.py
+    ```
 
 ---
 *Built with Streamlit, Qwen-VL, and ❤️.*