Update app.py

app.py

@@ -3,20 +3,16 @@ from tensorflow.keras.models import load_model
 import numpy as np
 from PIL import Image
 
-
 model = load_model('xray_image_classifier_model.keras')
 
 def predict(image):
-
     img = image.resize((150, 150))
     img_array = np.array(img) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
-
     prediction = model.predict(img_array)
     predicted_class = 'Pneumonia' if prediction > 0.5 else 'Normal'
     return predicted_class
 
-
 css = """
     .gradio-container {
         background-color: #f5f5f5;
@@ -54,6 +50,7 @@ css = """
         text-align: center;
     }
 """
+
 description = """
 **Automated Pneumonia Detection via Chest X-ray Classification**
 
@@ -66,7 +63,7 @@ This model leverages deep learning techniques to classify chest X-ray images as
 - Flask and Gradio for deployment and user interaction
 
 **Sample Images:**  
-To test the model, select one of the sample images provided below. Click on an image and then press the "Execute Classification" button to receive the results.
+To test the model, select one of the sample images provided below. Click on an image and then press the "Initiate Diagnostic Analysis" button to receive the results.
 """
 
 examples = [
@@ -74,7 +71,6 @@ examples = [
     ["samples/pneumonia_xray1.png"],
 ]
 
-# Gradio interface set up instructions
 with gr.Blocks(css=css) as interface:
     gr.Markdown("<h1>Automated Pneumonia Detection via Chest X-ray Classification</h1>")
     gr.Markdown("<p>Upload an X-ray image to detect pneumonia.</p>")
@@ -86,7 +82,8 @@ with gr.Blocks(css=css) as interface:
     submit_btn = gr.Button("Initiate Diagnostic Analysis", elem_classes=["gr-button"])
     submit_btn.click(fn=predict, inputs=image_input, outputs=output)
 
-    
     gr.Examples(examples=examples, inputs=image_input)
+    
+    gr.Markdown(description)
 
 interface.launch()