Update app.py

app.py

@@ -2,9 +2,8 @@ import gradio as gr
 from gradio_client import Client
 from PIL import Image
 import os
-import traceback
-import random
 import time
+import traceback
 
 # Create Client instances for the repositories
 clients = [
@@ -15,13 +14,15 @@ clients = [
 # Counter for image filenames to avoid overwriting
 count = 0
 
+# Global counter for selecting clients in order
+client_index = 0
+
 # Gradio Interface Function to handle image generation
 def infer_gradio(prompt: str):
-    global count
-    random.seed(time.time())
-    # Randomly choose a client
-    client = random.choice(clients)
-  
+    global count, client_index
+    # Select the current client based on the client_index
+    client = clients[client_index]
+    
     # Prepare the inputs for the prediction
     inputs = {
         "prompt": prompt,
@@ -32,8 +33,8 @@ def infer_gradio(prompt: str):
         # Send the request to the model and receive the result (image URL or file path)
         result = client.predict(inputs, api_name="/infer")
         
-        # Assuming the result is a URL or path, use it to open the image
-        image = Image.open(result)  # If the result is a URL, ensure you download it first
+        # Open the resulting image
+        image = Image.open(result)
         
         # Create a unique filename to save the image
         filename = f"img_{count:08d}.jpg"
@@ -46,6 +47,9 @@ def infer_gradio(prompt: str):
         print(f"Saved image as {filename}")
         
         # Return the image to be displayed in Gradio
+        # Update the client_index to use the next client in the next call
+        client_index = (client_index + 1) % len(clients)  # Cycle through clients
+        
         return image
     
     except Exception as e: