Use gradio_client.file

app.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 import gradio as gr
 import torch
-from gradio_client import Client
+from gradio_client import Client, file
 
 DESCRIPTION = "# Comparing image captioning models"
 ORIGINAL_SPACE_INFO = """\
@@ -26,7 +26,7 @@ torch.hub.download_url_to_file(
 def generate_caption_git(image_path: str) -> str:
     try:
         client = Client("hysts/image-captioning-with-git")
-        return client.predict(image_path, api_name="/caption")
+        return client.predict(file(image_path), api_name="/caption")
     except Exception:
         gr.Warning("The GIT-large Space is currently unavailable. Please try again later.")
         return ""
@@ -35,7 +35,7 @@ def generate_caption_git(image_path: str) -> str:
 def generate_caption_blip(image_path: str) -> str:
     try:
         client = Client("hysts/image-captioning-with-blip")
-        return client.predict(image_path, "A picture of", api_name="/caption")
+        return client.predict(file(image_path), "A picture of", api_name="/caption")
     except Exception:
         gr.Warning("The BLIP-large Space is currently unavailable. Please try again later.")
         return ""
@@ -45,7 +45,7 @@ def generate_caption_blip2_opt(image_path: str) -> str:
     try:
         client = Client("merve/BLIP2-with-transformers")
         return client.predict(
-            image_path,
+            file(image_path),
             "Beam search",
             1,  # temperature
             1,  # length penalty
@@ -61,7 +61,7 @@ def generate_caption_blip2_t5xxl(image_path: str) -> str:
     try:
         client = Client("hysts/BLIP2")
         return client.predict(
-            image_path,
+            file(image_path),
             "Beam search",
             1,  # temperature
             1,  # length penalty
@@ -81,7 +81,7 @@ def generate_caption_instructblip(image_path: str) -> str:
     try:
         client = Client("hysts/InstructBLIP")
         return client.predict(
-            image_path,
+            file(image_path),
             "Describe the image.",
             "Beam search",
             5,  # beam size
@@ -101,7 +101,11 @@ def generate_caption_instructblip(image_path: str) -> str:
 def generate_caption_fuyu(image_path: str) -> str:
     try:
         client = Client("adept/fuyu-8b-demo")
-        return client.predict(image_path, "Generate a coco style caption.\n", fn_index=3)
+        return client.predict(
+            file(image_path),
+            "Generate a coco style caption.\n",
+            fn_index=3,
+        )
     except Exception:
         gr.Warning("The Fuyu-8B Space is currently unavailable. Please try again later.")
         return ""