- Add examples

- Switch to async client

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
-from upstash_vector import Index
-from datasets import load_dataset
+from upstash_vector import AsyncIndex
 from transformers import AutoFeatureExtractor, AutoModel
+from datasets import load_dataset
 
-index = Index.from_env()
+index = AsyncIndex.from_env()
 
 model_ckpt = "google/vit-base-patch16-224-in21k"
 extractor = AutoFeatureExtractor.from_pretrained(model_ckpt)
@@ -16,7 +16,7 @@ with gr.Blocks() as demo:
         """
         # Find Your Twins
 
-        Upload your face and find the most similar people from BounharAbdelaziz/Face-Aging-Dataset dataset using Google's VIT model. Powered by [Upstash Vector](https://upstash.com) where all the image embeddings are stored 🚀 
+        Upload your face and find the most similar people from [Face Aging Dataset](https://huggingface.co/datasets/BounharAbdelaziz/Face-Aging-Dataset) using Google's [VIT](https://huggingface.co/google/vit-base-patch16-224-in21k) model. The task of finding most similar vectors is powered by [Upstash Vector](https://upstash.com) 🚀. Check our blog post *here*.
         """
     )
 
@@ -28,14 +28,25 @@ with gr.Blocks() as demo:
                 output_image = gr.Gallery(height=800)
 
 
-        @input_image.upload(inputs=input_image, outputs=output_image)
-        def find_similar_faces(image):
+        @input_image.change(inputs=input_image, outputs=output_image)
+        async def find_similar_faces(image):
+            if image is None:
+                return None
             inputs = extractor(images=image, return_tensors="pt")
             outputs = model(**inputs)
             embed = outputs.last_hidden_state[0][0]
-            result = index.query(vector=embed.tolist(), top_k=4)
+            result = await index.query(vector=embed.tolist(), top_k=4)
             return [dataset["train"][int(vector.id)]["image"] for vector in result]
 
+
+        gr.Examples(
+            examples=[dataset["train"][6]["image"]],
+            inputs=input_image,
+            outputs=output_image,
+            fn=find_similar_faces,
+            cache_examples=False,
+        )
+
     with gr.Tab("Advanced"):
         with gr.Row():
             with gr.Column(scale=1):
@@ -46,14 +57,14 @@ with gr.Blocks() as demo:
                 adv_output_image = gr.Gallery(height=1000)
 
 
-        @adv_input_image.upload(
-            inputs=[adv_input_image, adv_image_count], outputs=[adv_output_image]
-        )
-        def find_similar_faces(image, count):
+        @adv_input_image.upload(inputs=[adv_input_image, adv_image_count], outputs=[adv_output_image])
+        async def find_similar_faces(image, count):
             inputs = extractor(images=image, return_tensors="pt")
             outputs = model(**inputs)
             embed = outputs.last_hidden_state[0][0]
-            result = index.query(vector=embed.tolist(), top_k=max(1, min(19, count)))
+            result = await index.query(
+                vector=embed.tolist(), top_k=max(1, min(19, count))
+            )
             return [dataset["train"][int(vector.id)]["image"] for vector in result]
 
 if __name__ == "__main__":