Update app.py

app.py

@@ -3,6 +3,7 @@ import tensorflow as tf
 import numpy as np
 from tensorflow.keras.preprocessing import image
 from PIL import Image
+import os
 
 # Load the trained model
 model = tf.keras.models.load_model("my_keras_model.h5")
@@ -23,13 +24,9 @@ def predict_image(img):
 
     return f"Prediction: {predicted_class} (Confidence: {prediction[0][0]:.2f})"
 
-# Preloaded images for testing
-sample_images = [
-    ("fracture1.jpg", "Fractured Example"),
-    ("fracture2.jpg", "Fractured Example"),
-    ("normal1.jpg", "Normal Example"),
-    ("normal2.jpg", "Normal Example"),
-]
+# Get image paths dynamically
+sample_images_dir = "samples"
+sample_images = [os.path.join(sample_images_dir, f) for f in os.listdir(sample_images_dir) if f.endswith(('.jpg', '.png'))]
 
 # Define Gradio Interface
 interface = gr.Interface(
@@ -38,11 +35,7 @@ interface = gr.Interface(
     outputs=gr.Textbox(),
     examples=sample_images,  # Preloaded images for testing
     title="Bone Fracture Detection",
-    description="""
-    <h1 style='color: blue;'>Bone Fracture Detection Model</h1>
-    <p>This AI model predicts whether a given X-ray image shows a fracture or not.</p>
-    <p><b>Upload an image</b> or select from the provided samples to get a prediction.</p>
-    """,
+    description="Upload an X-ray image or select a sample image to check for fractures."
 )
 
 # Launch the Gradio app