Update app.py

app.py

@@ -2,19 +2,13 @@ import gradio as gr
 from random import randint
 from all_models import models
 from datetime import datetime
-from concurrent.futures import ThreadPoolExecutor
+from concurrent.futures import ThreadPoolExecutor, TimeoutError
+
+
+
 import numpy as np
 import time
 import requests
-#attempting to remove logged user input for the respect of users privacy of hugginface
-#thats right you heard me. logs happen here. and ive seen your input, 
-#AND, i can see it live (i think it may just be intrinsic in this hugginface network)
-#BUT, im really not interested one bit in your input, at all, and im not here to judge others (ive always suspected this was happening, and im just now becoming fully aware and i havent found many solutions yet)
-#SO, im trying my best to help protect our privacy 
-#PLEASE bare with me and if you have questions or suggestions or help
-#OR EVEN if you just want to leave a comment about it
-#please let me know (keep in mind, im a 0.32_ on the scale of (1 - 10) for python literacy  )
-#BUT KEEP IN MIND ---- this data helps me select the VERY BEST, and most stable models for US ALL to use. and thats my over all goal. (and the good news is, i have no idea who you are or where you are. the only thing i know, is whats currently being prompted and which models succedded and that some of you, smh just aint right lol. just try n play nice )
 now2 = 0
 index_tracker = 0  # Index tracker for the current model
 model_scores = {model: 0 for model in models}  # Dictionary to track scores for each model
@@ -53,22 +47,37 @@ def update_imgbox(choices):
 executor = ThreadPoolExecutor(max_workers=num_models)
 
 
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 def gen_fn(model_str, prompt):
-    global index_tracker, model_scores, processed_models_count  # Declare all global variables
+    global index_tracker, model_scores, processed_models_count  # Declare global variables
+
+    
+    
+    
     if model_str == 'NA':
         return None
-    try:
+
+
+    
+    def generate_image():
+        """Inner function to generate the image."""
         index_tracker = (index_tracker + 1) % len(models)
         current_model_index = index_tracker
         current_model_name = models[current_model_index]
         combined_prompt = f"{prompt} {randint(0, 9999)}"
-        response = models_load[model_str](f"{combined_prompt}")
- 
-        if isinstance(response, gr.Image):
-            return response
-        elif isinstance(response, tuple):
-            return None
-        elif isinstance(response, str):  # If the response is a path or URL, pass it as a string
+        response = models_load[model_str](f'{combined_prompt}')
+        return current_model_name, response
+
+    try:
+        # Run the image generation task with a timeout
+        future = executor.submit(generate_image)
+        current_model_name, response = future.result(timeout=8)  # Set timeout in seconds
+
+        if isinstance(response, str):  # If the response is valid
             if processed_models_count == 0:
                 print(f"**************")
                 print(f"{prompt}")
@@ -84,7 +93,25 @@ def gen_fn(model_str, prompt):
                 processed_models_count = 0
             return response
 
-            
+        else:
+            processed_models_count += 1
+            return None
+
+    except TimeoutError:
+        if processed_models_count == 0:
+                print(f"**************")
+                print(f"{prompt}")
+                print(f"{prompt}")
+                print(f"{prompt}")
+                print(f"**************")
+        print(f"TTT n:{processed_models_count} x:{current_model_index}  r[{model_scores[current_model_name]}] {model_str}")
+        processed_models_count += 1
+        if processed_models_count == len(models):
+                print("\nCycle Complete! Updated Scores:")
+                print(model_scores)
+                processed_models_count = 0
+        return None
+
     except Exception as e:
         if processed_models_count == 0:
                 print(f"**************")
@@ -99,6 +126,14 @@ def gen_fn(model_str, prompt):
                 print(model_scores)
                 processed_models_count = 0
         return None
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 def make_me():
     with gr.Row():
@@ -150,3 +185,31 @@ demo.queue()
 demo.queue = False
 demo.config["queue"] = False
 demo.launch(max_threads=400)
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