Update README.md

README.md

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-# 📸 Image Caption Generator
-
-![Python](https://img.shields.io/badge/Python-3.11.9-blue)
-![TensorFlow](https://img.shields.io/badge/TensorFlow-2.10-orange)
-![Gradio](https://img.shields.io/badge/Gradio-Deployed-green)
-
-A Generative AI project that automatically describes the content of an image using Deep Learning. It combines **Computer Vision (InceptionV3)** and **Natural Language Processing (LSTM)** to generate accurate, human-like captions.
-
-## 🚀 Live Demo
-The model is deployed and running live! You can test it with your own images here:
-**[👉 Click here to try the Live App on Hugging Face](https://huggingface.co/spaces/RupamG/Image_Captioning_System)**
-
 ---
-
-## 🧠 Technical Architecture
-This project uses an **Encoder-Decoder** architecture:
-
-1.  **Image Encoder (InceptionV3):**
-    * We use a pre-trained InceptionV3 model (trained on ImageNet) to extract high-level visual features from images.
-    * The last classification layer is removed, leaving us with a feature vector of shape `(2048,)`.
-2.  **Sequence Decoder (LSTM):**
-    * The extracted image features are passed to an LSTM (Long Short-Term Memory) network.
-    * The LSTM learns to generate a sequence of words (caption) based on the image features and the previous words generated.
-
-**Model Pipeline:**
-`Input Image` ➡️ `InceptionV3` ➡️ `Feature Vector` ➡️ `LSTM` ➡️ `Predicted Caption`
-
----
-
-## 📂 Dataset
-The model was trained on the **Flickr8k Dataset**, which consists of:
-* **8,000 images** (6,000 training, 1,000 val, 1,000 test).
-* **5 captions per image** (Total 40,000 captions).
-
-*> **Note:** Due to size constraints, the raw dataset is not included in this repository. You can download it from [Kaggle](https://www.kaggle.com/adityajn105/flickr8k) and place it in the `src/` folder.*
-
----
-
-## 🛠️ Installation & Setup
-To run this project locally on your machine:
-
-1.  **Clone the repository:**
-    ```bash
-    git clone https://github.com/Marshal-GG/Advanced-Image-Captioning-System.git
-    cd Advanced-Image-Captioning-System
-    ```
-
-2.  **Install dependencies:**
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-3.  **Download the Data:**
-    * Download Flickr8k images and `Flickr8k.token.txt`.
-    * Place them in the `src/` folder (or update paths in the notebook).
-
-4.  **Run the Training Notebook:**
-    * Open `main.ipynb` to see the data preprocessing, model training, and evaluation steps.
-
-5.  **Run the App:**
-    ```bash
-    python app.py
-    ```
-
----
-
-## 📊 Results
-* **Metric:** The model effectiveness is evaluated using qualitative analysis (visual inspection).
-* **Sample Output:**
-    * *Input:* Image of a 2 dogs running on grass.
-    * ![App Screenshot](demo_screenshot.png)
-    * *Output:* "Two dogs are playing together in the grass"
-
----
-
-## 🤝 Connect
-If you have any questions about this project or want to discuss Generative AI, feel free to connect!
-* **LinkedIn:** [https://www.linkedin.com/in/rupam-g/]
-* **Email:** [marshalgcom@gmail.com]
-
+title: Image Captioning System
+emoji: 😻
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 6.2.0
+app_file: app.py
+pinned: false
+short_description: Built a Deep Learning model to generate descriptive captions
+---