Update README.md

README.md

@@ -19,26 +19,28 @@ Model Description
 The model is based on EfficientNet, a state-of-the-art Convolutional Neural Network (CNN) architecture. It is trained to classify embryo images into two categories, originally labeled as "0" and "1", which you can map to Low Quality and High Quality, respectively. The model utilizes advanced image processing techniques to analyze and predict the quality of embryos based on provided images.
 
 Features
+
 Predict the quality of embryos from uploaded images.
 Provide confidence scores for each quality category.
 User-friendly interface built with Streamlit for easy interaction.
 Installation
+
 Requirements
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 Python 3.x
 TensorFlow
 Streamlit
 Pillow
 NumPy
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+
 Setup
-Install the required packages:
 
-bash
-Copy code
+Install the required packages:
 pip install streamlit tensorflow pillow numpy
+
 Clone the repository:
 
-bash
-Copy code
 git clone https://github.com/yourusername/embryo-quality-assessment.git
 cd embryo-quality-assessment
 Ensure the trained model is saved in the saved_model/embryo_classifier directory:
@@ -48,9 +50,9 @@ If the model is not saved, you can save it using TensorFlow's tf.saved_model.sav
 Usage
 Run the Streamlit app:
 
-bash
-Copy code
+
 streamlit run app.py
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 Upload an Embryo Image:
 
 Open your web browser and navigate to the provided URL (usually http://localhost:8501). Upload an embryo image in JPG, JPEG, or PNG format.