Create app.py

app.py

@@ -0,0 +1,98 @@
+import streamlit as st
+from PIL import Image, ImageOps
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from keras.models import load_model
+
+# Load the Keras model
+model = load_model('gastrointestinal_model.h5', compile=False)
+
+# Load class names
+class_names = ['Normal', 'Ulcerative Colitis', 'Polyp', 'Esophagitis']
+
+# Function to create plot
+def create_plot(prediction, class_names):
+    df = pd.DataFrame(prediction, index=class_names, columns=['Confidence'])
+    df = df.sort_values(by='Confidence', ascending=False)
+    plt.figure(figsize=(8, 5))
+    plt.bar(df.index, df['Confidence'], color='blue')
+    plt.xlabel('Class')
+    plt.ylabel('Confidence Score')
+    plt.title('Classification Confidence Scores')
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    return plt
+
+# Function to predict gastrointestinal conditions
+def predict_gastrointestinal(img):
+    size = (224, 224)
+    image_PIL = ImageOps.fit(img, size, Image.LANCZOS)
+    image_array = np.asarray(image_PIL)
+    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+    data = np.expand_dims(normalized_image_array, axis=0)
+    
+    prediction = model.predict(data)[0]
+    class_index = np.argmax(prediction)
+    predicted_class = class_names[class_index]
+    confidence_scores = prediction * 100
+    
+    # Create plot
+    plot = create_plot(confidence_scores, class_names)
+    
+    return predicted_class, plot
+
+# Streamlit app
+st.title("Gastrointestinal Classification Web App")
+
+uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    st.write("Classifying...")
+
+    predicted_class, plot = predict_gastrointestinal(image)
+    st.write(f"Prediction: {predicted_class}")
+    st.pyplot(plot)
+
+# Sample images
+st.markdown("### Sample Images")
+if st.button('Normal Sample'):
+    image = Image.open('normal_sample.jpg')
+    st.image(image, caption='Normal Sample Image', use_column_width=True)
+    st.write("Classifying...")
+    predicted_class, plot = predict_gastrointestinal(image)
+    st.write(f"Prediction: {predicted_class}")
+    st.pyplot(plot)
+
+if st.button('Ulcerative Colitis Sample'):
+    image = Image.open('ulcerative_colitis_sample.jpg')
+    st.image(image, caption='Ulcerative Colitis Sample Image', use_column_width=True)
+    st.write("Classifying...")
+    predicted_class, plot = predict_gastrointestinal(image)
+    st.write(f"Prediction: {predicted_class}")
+    st.pyplot(plot)
+
+if st.button('Polyp Sample'):
+    image = Image.open('polyp_sample.jpg')
+    st.image(image, caption='Polyp Sample Image', use_column_width=True)
+    st.write("Classifying...")
+    predicted_class, plot = predict_gastrointestinal(image)
+    st.write(f"Prediction: {predicted_class}")
+    st.pyplot(plot)
+
+if st.button('Esophagitis Sample'):
+    image = Image.open('esophagitis_sample.jpg')
+    st.image(image, caption='Esophagitis Sample Image', use_column_width=True)
+    st.write("Classifying...")
+    predicted_class, plot = predict_gastrointestinal(image)
+    st.write(f"Prediction: {predicted_class}")
+    st.pyplot(plot)
+
+# Educational content
+st.markdown("### Learn More About Gastrointestinal Conditions")
+st.markdown("""
+    - [Gastrointestinal Disorders Overview](https://www.mayoclinic.org/diseases-conditions/gastrointestinal-disorders/symptoms-causes/syc-20375441)
+    - [Preventing Gastrointestinal Conditions](https://www.niddk.nih.gov/health-information/digestive-diseases)
+    """)