Update README.md

README.md

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 title: Embryo Classifier
-emoji: 🔥
-colorFrom: green
-colorTo: indigo
+emoji: 🐢
+colorFrom: blue
+colorTo: pink
 sdk: streamlit
 sdk_version: 1.36.0
 app_file: app.py
 pinned: false
 license: apache-2.0
+short_description: Embryo Quality Identifier
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Embryo Quality Assessment: A Tandem of Machine Learning and Medicine
+Overview
+This project leverages the power of machine learning to assist in the medical field, specifically in the assessment of embryo quality during in vitro fertilization (IVF) procedures. By using a trained machine learning model, we aim to provide embryologists with accurate and reliable assessments of embryo quality, thereby increasing the chances of successful pregnancies and reducing the emotional and financial burden on prospective parents.
+
+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
+Python 3.x
+TensorFlow
+Streamlit
+Pillow
+NumPy
+Setup
+Install the required packages:
+
+bash
+Copy code
+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:
+
+If the model is not saved, you can save it using TensorFlow's tf.saved_model.save method.
+
+Usage
+Run the Streamlit app:
+
+bash
+Copy code
+streamlit run app.py
+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.
+
+View the Results:
+
+The app will display the uploaded image, classify its quality, and show the confidence scores for each quality category.
+
+
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference