gradio's cache_example = False

app.py

@@ -12,13 +12,13 @@ warnings.filterwarnings('ignore')
 def process_image_and_generate_output(image, model_selection):
     if image is None:
         return "Please select an image", None
-
-    if model_selection == ('Basic Model (Trained only for 15 epochs without any hyperparameter tuning, utilizing '
-                           'inception v3)'):
+    # (Trained only for 15 epochs without any hyperparameter tuning, utilizing inception v3)'
+    #                            (SOTA model for Image captioning)
+    if model_selection == ('Basic Model'):
         result = inference_script.evaluate(image)
         pred_caption = ' '.join(result).rsplit(' ', 1)[0]
         pred_caption = pred_caption.replace('<unk>', '')
-    elif model_selection == 'ViT-GPT2 (SOTA model for Image captioning)':
+    elif model_selection == 'ViT-GPT2':
         result = vit_gpt2.predict_step(image)
         pred_caption = result[0]
     else:
@@ -40,12 +40,12 @@ def process_image_and_generate_output(image, model_selection):
 
 
 # sample_images = [
-#     [os.path.join(os.path.dirname(__file__), "sample_images/1.jpg")],
-#     [os.path.join(os.path.dirname(__file__), "sample_images/2.jpg")],
-#     [os.path.join(os.path.dirname(__file__), "sample_images/3.jpg")],
-#     [os.path.join(os.path.dirname(__file__), "sample_images/4.jpg")],
-#     [os.path.join(os.path.dirname(__file__), "sample_images/5.jpg")],
-#     [os.path.join(os.path.dirname(__file__), "sample_images/6.jpg")]
+#     [os.path.join(os.path.dirname(__file__), "sample_images/1.jpg"), 'Basic Model'],
+#     [os.path.join(os.path.dirname(__file__), "sample_images/2.jpg"), 'Basic Model'],
+#     [os.path.join(os.path.dirname(__file__), "sample_images/3.jpg"), 'Basic Model'],
+#     [os.path.join(os.path.dirname(__file__), "sample_images/4.jpg"), "ViT-GPT2"],
+#     [os.path.join(os.path.dirname(__file__), "sample_images/5.jpg"), "ViT-GPT2"],
+#     [os.path.join(os.path.dirname(__file__), "sample_images/6.jpg"), "ViT-GPT2"]
 # ]
 
 sample_images = [
@@ -62,15 +62,15 @@ sample_images = [
 image_input = gr.Image(label="Upload Image")
 
 # Create a dropdown to choose the model
-model_selection_input = gr.Radio(["Basic Model (Trained only for 15 epochs without any hyperparameter "
-                                  "tuning, utilizing inception v3)",
-                                  "ViT-GPT2 (SOTA model for Image captioning)"],
+model_selection_input = gr.Radio(["Basic Model",
+                                  "ViT-GPT2"],
                                  label="Choose Model")
 
 iface = gr.Interface(fn=process_image_and_generate_output,
                      inputs=[image_input, model_selection_input],
                      outputs=["text", "audio"],
                      examples=sample_images,
+                     cache_examples=False,
                      allow_flagging='never',
                      title="Eye For Blind | Image Captioning & TTS",
                      description="To be added")