Update app.py

app.py

@@ -3,41 +3,56 @@ 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.LANCZOS)  # Updated to use Image.LANCZOS
+    image = image.resize(IMG_SIZE, Image.LANCZOS)
     inp_numpy = np.array(image)[None]
     inp = tf.constant(inp_numpy, dtype='float32')
     return inp
 
-# Streamlit interface
+
+st.set_page_config(page_title="Embryo Quality Assessment", layout="wide")
+
 st.title("Embryo Quality Assessment")
+st.write("""
+Upload an embryo image to classify its quality. The model will predict the quality of the embryo as either Low, Medium, or High.
+""")
 
-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)
+    resized_image = image.resize((150, 150))  
+    st.image(resized_image, caption='Uploaded Image.', use_column_width=False)
     
     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}%")
+    st.write(f"**Prediction:** {classes[predicted_class]}")
+    st.write(f"**Confidence:** {np.max(class_scores) * 100:.2f}%")
+    
+
+    st.write("**Confidence scores for all classes:**")
+    for i, score in enumerate(class_scores):
+        st.write(f"{classes[i]}: {score * 100:.2f}%")
+
+
+st.markdown("""
+---
+*Created by [Your Name](https://your-link.com)*
+""")