initial

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,61 @@
+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import io
+
+# Load your pre-trained model
+model = tf.keras.models.load_model('model.keras')
+
+# Define the image preprocessing function
+# Define the image preprocessing function
+def preprocess_image(image):
+    # Convert image to RGB if it's grayscale
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+    
+    # Resize and preprocess the image
+    image = image.resize((224, 224))  # Adjust the size as per your model's requirement
+    image_array = np.array(image) / 255.0  # Normalize the image
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    return image_array
+
+# Define the class labels
+class_labels = ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusion', 
+                 'Emphysema', 'Fibrosis', 'Infiltration', 'Mass', 
+                 'Nodule', 'Pleural_Thickening', 'Pneumothorax']
+# class_labels = ["Class1", "Class2", "Class3", "Class4", "Class5"]  # Update with actual class names
+
+# Streamlit app
+st.title("Image Classification with Grad-CAM")
+
+st.write("Upload an image to get predictions:")
+
+# Upload image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Read and display the image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    # Preprocess the image
+    preprocessed_image = preprocess_image(image)
+    
+    # Make predictions
+    predictions = model.predict(preprocessed_image)[0]
+    
+    # Get top 3 predictions with probability greater than 0.5
+    top_predictions = [(label, prob) for label, prob in zip(class_labels, predictions) if prob > 0.5]
+    top_predictions = sorted(top_predictions, key=lambda x: x[1], reverse=True)[:3]
+    
+    # Display results
+    st.write("Predictions with probability greater than 0.5:")
+    for label, prob in top_predictions:
+        st.write(f"{label}: {prob*100:.2f}%")
+        percentage = int(prob * 100)
+        # st.progress(prob)
+        st.progress(percentage)
+
+    if not top_predictions:
+        st.write("No predictions with probability greater than 0.5.")

model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fb2bf1ac2b5c0692ab11f325750c71af7bd4417cd4571f00fe29293bb056a09
+size 88710372