Allow choice from samples

app.py

@@ -22,10 +22,22 @@ def load_model(repo_id):
     detection_model = tf.saved_model.load(saved_model_dir)
     return detection_model
 
-def predict(pilimg):
+# List of sample images in 'test_samples' folder
+sample_images = [file for file in os.listdir('test_samples') if file.endswith(('.png', '.jpg', '.jpeg'))]
+sample_images.sort()  # Optional: sort the file names
+
+def predict(uploaded_img, use_sample, sample_choice):
+    if use_sample:
+        # Use the selected sample image
+        pilimg = Image.open(os.path.join('test_samples', sample_choice))
+    else:
+        # Use the uploaded image
+        pilimg = uploaded_img
+
     image_np = pil_image_as_numpy_array(pilimg)
     return predict_combined_models(image_np, detection_model_fin, detection_model_ini)
 
+
 def predict_combined_models(image_np, model1, model2):
     # Process with first model
     results1 = model1(image_np)
@@ -79,7 +91,11 @@ detection_model_ini = load_model(REPO_ID2)
 # Gradio interface
 gr.Interface(
     fn=predict, 
-    inputs=gr.Image(type="pil"), 
+    inputs=[
+        gr.Image(type="pil"),  # Image upload
+        gr.Checkbox(label="Use a sample image instead"),  # Checkbox to choose a sample
+        gr.Dropdown(choices=sample_images, label="Select a Sample Image")  # Dropdown to select a sample
+    ], 
     outputs=gr.Image(type="pil"),
     title="Gramophone & Veena Detection with TensorFlow Models",
     description="This app uses TensorFlow models to detect objects in images. Upload an image to see the detected gramophone and veena. Bounding boxes labeled Run1 = - Model 2, Run2= - Model 1"