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app.py

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+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load the saved model
+model = tf.saved_model.load('saved_model/embryo_classifier')
+
+# Define image size (should match the input size of your model)
+IMG_SIZE = (300, 300)
+
+# Function to preprocess the image
+def preprocess_image(image):
+    image = image.resize(IMG_SIZE, Image.ANTIALIAS)
+    inp_numpy = np.array(image)[None]
+    inp = tf.constant(inp_numpy, dtype='float32')
+    return inp
+
+# Streamlit interface
+st.title("Embryo Quality Assessment")
+
+st.write("Upload an embryo image to classify its quality.")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert('RGB')
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    
+    st.write("Classifying...")
+    
+    # Preprocess the image
+    processed_image = preprocess_image(image)
+    
+    # Make predictions
+    class_scores = model(processed_image)[0].numpy()
+    predicted_class = class_scores.argmax()
+    
+    # Display the results
+    classes = ['Low Quality', 'Medium Quality', 'High Quality']  # Adjust according to your classes
+    st.write(f"Prediction: {classes[predicted_class]}")
+    st.write(f"Confidence: {np.max(class_scores) * 100:.2f}%")

requirements.txt

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+streamlit
+tensorflow
+Pillow
+numpy