Parallelize client calls (#51)

- Parallelize client calls (7aeaca1a0c09df26fa15ce290eb9bc2dbe47effd)


Co-authored-by: hysts <[email protected]>

app.py

@@ -5,6 +5,7 @@ import os
 import gradio as gr
 import torch
 from gradio_client import Client
+from gradio_client.client import Job
 
 DESCRIPTION = "# Comparing image captioning models"
 ORIGINAL_SPACE_INFO = """\
@@ -25,28 +26,31 @@ torch.hub.download_url_to_file(
 )
 
 
-def generate_caption_git(image_path: str) -> str:
+def generate_caption_git(image_path: str, return_job: bool = False) -> str | Job:
     try:
         client = Client("library-samples/image-captioning-with-git")
-        return client.predict(image_path, api_name="/caption")
+        fn = client.submit if return_job else client.predict
+        return fn(image_path, api_name="/caption")
     except Exception:
         gr.Warning("The GIT-large Space is currently unavailable. Please try again later.")
         return ""
 
 
-def generate_caption_blip(image_path: str) -> str:
+def generate_caption_blip(image_path: str, return_job: bool = False) -> str | Job:
     try:
         client = Client("library-samples/image-captioning-with-blip")
-        return client.predict(image_path, "A picture of", api_name="/caption")
+        fn = client.submit if return_job else client.predict
+        return fn(image_path, "A picture of", api_name="/caption")
     except Exception:
         gr.Warning("The BLIP-large Space is currently unavailable. Please try again later.")
         return ""
 
 
-def generate_caption_blip2_opt(image_path: str) -> str:
+def generate_caption_blip2_opt(image_path: str, return_job: bool = False) -> str | Job:
     try:
         client = Client("merve/BLIP2-with-transformers")
-        return client.predict(
+        fn = client.submit if return_job else client.predict
+        return fn(
             image_path,
             "Beam search",
             1,  # temperature
@@ -59,10 +63,11 @@ def generate_caption_blip2_opt(image_path: str) -> str:
         return ""
 
 
-def generate_caption_blip2_t5xxl(image_path: str) -> str:
+def generate_caption_blip2_t5xxl(image_path: str, return_job: bool = False) -> str | Job:
     try:
         client = Client("hysts/BLIP2-with-transformers")
-        return client.predict(
+        fn = client.submit if return_job else client.predict
+        return fn(
             image_path,
             "Beam search",
             1,  # temperature
@@ -79,10 +84,11 @@ def generate_caption_blip2_t5xxl(image_path: str) -> str:
         return ""
 
 
-def generate_caption_instructblip(image_path: str) -> str:
+def generate_caption_instructblip(image_path: str, return_job: bool = False) -> str | Job:
     try:
         client = Client("library-samples/InstructBLIP")
-        return client.predict(
+        fn = client.submit if return_job else client.predict
+        return fn(
             image_path,
             "Describe the image.",
             "Beam search",
@@ -100,24 +106,26 @@ def generate_caption_instructblip(image_path: str) -> str:
         return ""
 
 
-def generate_caption_fuyu(image_path: str) -> str:
+def generate_caption_fuyu(image_path: str, return_job: bool = False) -> str | Job:
     try:
         client = Client("adept/fuyu-8b-demo")
-        return client.predict(image_path, "Generate a coco style caption.", fn_index=3)
+        fn = client.submit if return_job else client.predict
+        return fn(image_path, "Generate a coco style caption.", fn_index=3)
     except Exception:
         gr.Warning("The Fuyu-8B Space is currently unavailable. Please try again later.")
         return ""
 
 
 def generate_captions(image_path: str) -> tuple[str, str, str, str, str, str]:
-    return (
-        generate_caption_git(image_path),
-        generate_caption_blip(image_path),
-        generate_caption_blip2_opt(image_path),
-        generate_caption_blip2_t5xxl(image_path),
-        generate_caption_instructblip(image_path),
-        generate_caption_fuyu(image_path),
-    )
+    jobs = [
+        generate_caption_git(image_path, return_job=True),
+        generate_caption_blip(image_path, return_job=True),
+        generate_caption_blip2_opt(image_path, return_job=True),
+        generate_caption_blip2_t5xxl(image_path, return_job=True),
+        generate_caption_instructblip(image_path, return_job=True),
+        generate_caption_fuyu(image_path, return_job=True),
+    ]
+    return tuple(job.result() if job else "" for job in jobs)
 
 
 with gr.Blocks(css="style.css") as demo: