readme updated

README.md

@@ -1,3 +1,14 @@
+<table>
+  <tr>
+    <td align="center">Input (B&W)</td>
+    <td align="center">Colorized Output</td>
+  </tr>
+  <tr>
+    <td><img src="DeOldify/test_images/1910Finland.jpg" alt="Input Black and White Image" height=500/></td>
+    <td><img src="DeOldify/result_images/1910Finland.jpg" alt="Colorized Output Image" height=500/></td>
+  </tr>
+</table>
+
 # Image Colorization Application
 
 This application provides a web-based interface for colorizing black and white images using DeOldify technology. It consists of a FastAPI backend for processing images and a Streamlit frontend for a user-friendly interface.
@@ -14,39 +25,50 @@ This application provides a web-based interface for colorizing black and white i
 
 ### Prerequisites
 
-- Python 3.7 or higher
+- Python 3.7 or higher ( preferred 3.10 )
 - CUDA-capable GPU (recommended for faster processing)
-- Git
+- Git, Git-Lfs
 
 ### Setup Instructions
 
 1. **Clone the DeOldify repository inside your project directory:**
 
 ```bash
-git clone https://github.com/jantic/DeOldify.git
+git clone https://huggingface.co/sayed99/Image-Colorizer
 ```
 
-2. **Install the requirements:**
+2. **Set up the environment:**
 
-```bash
-pip install -r requirements.txt
-```
+You can set up the environment using either Conda (recommended) or pip.
 
-This installs both the dependencies for our application and most of the dependencies needed for DeOldify.
+- **Using Conda (Recommended):**
 
-3. **Download the pre-trained model weights:**
+```bash
+conda env create -f environment.yml
+conda activate deoldify-env # Or whatever you named your environment
+```
 
-- Artistic model: [ColorizeArtistic_gen.pth](https://data.deepai.org/deoldify/ColorizeArtistic_gen.pth)
-- Stable model: [ColorizeStable_gen.pth](https://www.dropbox.com/s/axsd2g85uyixaho/ColorizeStable_gen.pth?dl=0)
+- **Using Pip:**
 
-4. **Create a models directory and place the downloaded weights:**
+Make sure you have a compatible Python environment (3.7+).
 
 ```bash
-mkdir -p DeOldify/models
-# Move the downloaded .pth files to the DeOldify/models directory
+pip install -r requirements.txt
 ```
 
-> **IMPORTANT**: The application expects the DeOldify directory to be in the same directory as the app.py and streamlit_app.py files.
+This installs both the dependencies for our application and most of the dependencies needed for DeOldify.
+
+## Directory Structure
+
+- `DeOldify/`: Cloned repository containing the core DeOldify library.
+- `models/`: Contains the downloaded `.pth` model weights.
+- `input_images/`: Default location for uploaded images (can be configured).
+- `output_images/` or `result_images/`: Default location for single colorized images.
+- `multiple_renders/`: Default location for images generated using the multiple render factors feature.
+- `app.py`: FastAPI backend server.
+- `streamlit_app.py`: Streamlit frontend application.
+- `requirements.txt`: Pip dependencies.
+- `environment.yml`: Conda environment specification.
 
 ## Running the Application
 
@@ -66,25 +88,6 @@ streamlit run streamlit_app.py
 
 This will open the web interface in your browser, typically at http://localhost:8501
 
-## Troubleshooting
-
-### Common Issues
-
-#### ModuleNotFoundError: No module named 'deoldify'
-
-This error indicates that the application cannot find the DeOldify module. Make sure:
-
-- The DeOldify repository is cloned in the same directory as app.py
-- You're running the commands from the root directory of the project, not from inside the DeOldify directory
-
-#### CUDA/GPU-related errors
-
-If you encounter CUDA errors:
-
-- Make sure your GPU drivers are up to date
-- Try running with a lower render factor
-- Edit app.py to change `DeviceId.GPU0` to `DeviceId.CPU` if you don't have a GPU
-
 ## API Documentation
 
 Once the FastAPI server is running, you can access the API documentation at:
@@ -118,7 +121,7 @@ The web application provides an intuitive interface for:
 ## Performance Tips
 
 - If you encounter CUDA out-of-memory errors, try lowering the render factor
-- For best results with the artistic model, a render factor of 35-40 is recommended
+- For best results with the artistic model, a render factor of 10-15 is recommended
 - For the stable model, a render factor of 20-25 typically works well
 
 ## License