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app.py

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+import streamlit as st
+import numpy as np
+import cv2
+import tensorflow as tf
+from PIL import Image
+from keras import backend as K
+from keras.metrics import Precision, Recall
+from vit_keras import vit, utils, layers
+
+# Page configuration
+st.set_page_config(
+    page_title="Breast Cancer Classification",
+    page_icon="🏥",
+    layout="centered"
+)
+
+# Cache the model loading
+@st.cache_resource
+def load_model():
+    try:
+        model = tf.keras.models.load_model(
+            'model.h5',
+            custom_objects={
+                'ClassToken': layers.ClassToken,
+                'f1_score': f1_score
+            },
+            compile=False
+        )
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        return None
+
+def f1_score(y_true, y_pred):
+    y_true = K.round(y_true)
+    y_pred = K.round(y_pred)
+    
+    tp = K.sum(y_true * y_pred)
+    fp = K.sum((1 - y_true) * y_pred)
+    fn = K.sum(y_true * (1 - y_pred))
+    
+    precision = tp / (tp + fp + K.epsilon())
+    recall = tp / (tp + fn + K.epsilon())
+    
+    return 2 * precision * recall / (precision + recall + K.epsilon())
+
+def process_image(image):
+    img_array = np.array(image)
+    image_pred = cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB)
+    resized_image = cv2.resize(image_pred, (224, 224))
+    return np.array([resized_image], dtype='float32') / 255.0
+
+# App header
+st.header('🔬 Breast Cancer Classification using Ultrasound')
+
+# Load model
+model = load_model()
+
+if model:
+    model.compile(
+        optimizer='adam',
+        loss='binary_crossentropy',
+        metrics=['accuracy', f1_score]
+    )
+
+    # File uploader
+    uploaded_file = st.file_uploader(
+        "Upload an ultrasound image",
+        type=['jpg', 'jpeg', 'png'],
+        help="Supported formats: JPG, JPEG, PNG"
+    )
+
+    if uploaded_file:
+        try:
+            # Display image
+            col1, col2 = st.columns(2)
+            with col1:
+                image = Image.open(uploaded_file)
+                st.image(image, caption='Uploaded Ultrasound Image', use_column_width=True)
+            
+            # Process and predict
+            with st.spinner('Analyzing image...'):
+                processed_image = process_image(image)
+                predictions = model.predict(processed_image, verbose=0)
+                predicted_label_index = tf.argmax(predictions, axis=-1).numpy()[0]
+            
+            # Display results
+            with col2:
+                st.subheader("Analysis Results")
+                labels = {'Benign': 0, 'Malignant': 1}
+                result = list(labels.keys())[list(labels.values()).index(predicted_label_index)]
+                confidence = float(predictions[0][predicted_label_index]) * 100
+                
+                st.metric("Prediction", result)
+                st.metric("Confidence", f"{confidence:.2f}%")
+                
+                if predicted_label_index == 1:
+                    st.error("⚠️ Please consult with your doctor as results indicate potential malignancy.")
+                else:
+                    st.success("✅ Results suggest benign characteristics.")
+
+        except Exception as e:
+            st.error(f"Error processing image: {str(e)}")
+else:
+    st.error("Failed to load the model. Please check if model.h5 exists in the correct location.")
+
+

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:62f040b88a6fcd237762656ef0d993a3f2588925fd1118045fdb1286a6cd2946
+size 630297216

requirements.txt

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+tensorflow
+tensorflow-addons
+tensorflow-estimator
+streamlit
+opencv-python
+numpy
+keras
+vit-keras