Readme Updates

README.md

@@ -1,58 +1,14 @@
-# Image Captioning with SOTA Models
+---
+title: Captioning
+emoji: 🐨
+colorFrom: gray
+colorTo: red
+sdk: docker
+pinned: false
+license: mit
+---
 
-This project provides a unified API for Image Captioning using various State-of-the-Art (SOTA) models as well as a custom ResNet+GPT2 implementation.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
-## Supported Models
 
-1.  **BLIP (Bootstrapping Language-Image Pre-training)**
-    *   Model: `Salesforce/blip-image-captioning-large`
-    *   Status: **Default** (Best Performance)
-    *   Description: Produces highly accurate and detailed captions.
 
-2.  **ViT-GPT2**
-    *   Model: `nlpconnect/vit-gpt2-image-captioning`
-    *   Status: Available
-    *   Description: Uses Vision Transformer (ViT) encoder and GPT-2 decoder.
-
-3.  **ResNet50 + GPT-2 (Custom)**
-    *   Model: Custom implementation trained from scratch.
-    *   Status: Legacy / Experimental
-    *   Description: Good for learning purposes or custom datasets.
-
-## Installation
-
-1.  Clone the repository.
-2.  Install dependencies:
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-## Configuration
-
-Edit `config/config.py` to select the model:
-
-```python
-class Config:
-    # ...
-    MODEL_TYPE = "blip" # Options: "blip", "vit_gpt2", "resnet_gpt2"
-```
-
-## Running the API
-
-Start the FastAPI server:
-
-```bash
-python main.py --mode api
-```
-
-Open your browser at `http://localhost:8001` to use the drag-and-drop interface.
-
-## Training (ResNet+GPT2 only)
-
-To train the custom model:
-
-1.  Set `MODEL_TYPE = "resnet_gpt2"` in config.
-2.  Run:
-    ```bash
-    python main.py --mode train
-    ```