Create app.py

app.py

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+import streamlit as st
+import mahotas as mh
+import pandas as pd
+import plotly.express as px
+
+# Define a list of medical conditions
+conditions = [
+    {"name": "Depression", "test_for": "Patient Health Questionnaire-9 (PHQ-9)"},
+    {"name": "Anxiety", "test_for": "Generalized Anxiety Disorder-7 (GAD-7)"},
+    {"name": "Diabetes", "test_for": "Hemoglobin A1C test"},
+    {"name": "Hypertension", "test_for": "Blood pressure measurement"},
+    {"name": "Asthma", "test_for": "Pulmonary function test"},
+    {"name": "Cancer", "test_for": "Biopsy or imaging tests (e.g., CT scan, MRI)"},
+    {"name": "Arthritis", "test_for": "X-ray, MRI, or ultrasound"},
+    {"name": "Heart disease", "test_for": "Electrocardiogram (ECG)"},
+    {"name": "Obesity", "test_for": "Body mass index (BMI)"},
+    {"name": "Substance use disorder", "test_for": "Substance Abuse Subtle Screening Inventory (SASSI)"}
+]
+
+# Define a function to process images using Mahotas
+def process_image(image):
+    # Convert the image to grayscale
+    grayscale_image = mh.colors.rgb2gray(image)
+    # Apply a Gaussian filter to the image to reduce noise
+    filtered_image = mh.gaussian_filter(grayscale_image, 4)
+    # Threshold the image to create a binary image
+    binary_image = filtered_image > mh.otsu(filtered_image)
+    # Compute the connected components in the binary image
+    labels, num_labels = mh.label(binary_image)
+    # Compute the size of each connected component
+    sizes = mh.labeled.labeled_size(labels)
+    # Sort the sizes in descending order
+    sorted_sizes = sorted(sizes, reverse=True)
+    # Return the top 10 sizes
+    return sorted_sizes[:10]
+
+# Define the Streamlit app
+def app():
+    # Add a title to the app
+    st.title("Mahotas Demo")
+
+    # Add a sidebar to the app
+    st.sidebar.title("Medical Conditions")
+    selected_condition = st.sidebar.selectbox("Select a condition", [c["name"] for c in conditions])
+
+    # Get the selected condition
+    condition = next(c for c in conditions if c["name"] == selected_condition)
+
+    # Display the selected condition
+    st.header(condition["name"])
+    st.write("Test for:", condition["test_for"])
+
+    # Load an example medical image
+    image = mh.imread("https://i.imgur.com/kPQoD8C.jpg")
+
+    # Process the image using Mahotas
+    sizes = process_image(image)
+
+    # Display the top 10 connected component sizes
+    df = pd.DataFrame({"Size": sizes})
+    st.write(df)
+
+    # Create a sunburst chart using Plotly
+    fig = px.sunburst(
+        df,
+        path=["Size"],
+        values="Size",
+        color="Size",
+        color_continuous_scale="blues"
+    )
+    st.plotly_chart(fig)
+
+if __name__ == "__main__":
+    app()