Update README.md

README.md

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-# Generating MNIST digits using Conditional GAN
-
-This is a simple implementation of Conditional Generative Adversarial Networks (GAN) for generating MNIST digits.
-
-![cover](demos/gen_all_digits.png)
-
-I use simple BCE loss function for calculating the loss and Adam optimizer (lr=0.0001) for training.
-
-## Architecture
-
-- The **generator** is series of Linear layers with BatchNorm and ReLU activations.
-- The **discriminator** is a series of Linear layers with BatchNorm andLeakyReLU activations.
-- The Conditioning class is appended to the noise vector as a one-hot vector.
-
-## Huggingface Space
-
-You can try generating digits using this model on Huggingface Space.
-https://huggingface.co/spaces/gholamreza/Conditional-GAN-MNIST
-
-![Huggingface Space](demos/gradio_app.png)
-
-## Training History
-
-![losses_plot](demos/losses.png)
-
-Visit https://github.com/gholamrezadar/GAN-MNIST for a simpler version of this code and more details.
+---
+title: Conditional GAN MNIST
+emoji: 💻
+colorFrom: purple
+colorTo: green
+sdk: gradio
+sdk_version: 5.7.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: This is a simple implementation of Conditional Generative Ad
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference