Update app.py

app.py

@@ -1,51 +1,44 @@
 import gradio as gr
 from random import randint
 from all_models import models
-
 from externalmod import gr_Interface_load, randomize_seed
-
 import asyncio
 import os
 from threading import RLock
 
 # Create a lock to ensure thread safety when accessing shared resources
 lock = RLock()
+
 # Load Hugging Face token from environment variable, if available
-HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
+HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None  # If private or gated models aren't used, ENV setting is unnecessary.
 
 # Function to load all models specified in the 'models' list
 def load_fn(models):
     global models_load
     models_load = {}
-    
+
     # Iterate through all models to load them
     for model in models:
         if model not in models_load.keys():
             try:
-                # Log model loading attempt
                 print(f"Attempting to load model: {model}")
-                # Load model interface using externalmod function
                 m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
                 print(f"Successfully loaded model: {model}")
             except Exception as error:
-                # In case of an error, print it and create a placeholder interface
                 print(f"Error loading model {model}: {error}")
                 m = gr.Interface(lambda: None, ['text'], ['image'])
-            # Update the models_load dictionary with the loaded model
             models_load.update({model: m})
 
-# Load all models defined in the 'models' list
 print("Loading models...")
 load_fn(models)
 print("Models loaded successfully.")
 
-num_models = 19
+num_models = 6
 
 # Set the default models to use for inference
 default_models = models[:num_models]
 inference_timeout = 600
 MAX_SEED = 3999999999
-# Generate a starting seed randomly between 1941 and 2024
 starting_seed = randint(1941, 2024)
 print(f"Starting seed: {starting_seed}")
 
@@ -65,31 +58,35 @@ def update_imgbox(choices):
     return imgboxes
 
 # Asynchronous function to perform inference on a given model
-async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
+async def infer(model_str, prompt, seed=1, batch_size=1, output_format="PNG", priority="medium", timeout=inference_timeout):
     from pathlib import Path
     kwargs = {}
     noise = ""
     kwargs["seed"] = seed
-    # Create an asynchronous task to run the model inference
-    print(f"Starting inference for model: {model_str} with prompt: '{prompt}' and seed: {seed}")
-    task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
-                               prompt=f'{prompt} {noise}', **kwargs, token=HF_TOKEN))
-    await asyncio.sleep(0)  # Allow other tasks to run
+    kwargs["batch_size"] = batch_size
+    kwargs["priority"] = priority
+    print(f"Starting inference for model: {model_str} with prompt: '{prompt}' and seed: {seed}, batch_size: {batch_size}, priority: {priority}")
+    task = asyncio.create_task(
+        asyncio.to_thread(
+            models_load[model_str].fn,
+            prompt=f'{prompt} {noise}',
+            **kwargs,
+            token=HF_TOKEN
+        )
+    )
+    await asyncio.sleep(0)
     try:
-        # Wait for the task to complete within the specified timeout
         result = await asyncio.wait_for(task, timeout=timeout)
         print(f"Inference completed for model: {model_str}")
     except (Exception, asyncio.TimeoutError) as e:
-        # Handle any exceptions or timeout errors
         print(f"Error during inference for model {model_str}: {e}")
         if not task.done():
             task.cancel()
             print(f"Task cancelled for model: {model_str}")
         result = None
-    # If the task completed successfully, save the result as an image
     if task.done() and result is not None:
         with lock:
-            png_path = "image.png"
+            png_path = f"image.{output_format.lower()}"
             result.save(png_path)
             image = str(Path(png_path).resolve())
             print(f"Result saved as image: {image}")
@@ -98,21 +95,20 @@ async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
     return None
 
 # Function to generate an image based on the given model, prompt, and seed
-def gen_fnseed(model_str, prompt, seed=1):
+def gen_fnseed(model_str, prompt, seed=1, batch_size=1, output_format="PNG", priority="medium"):
     if model_str == 'NA':
         print(f"Model is 'NA', skipping generation.")
         return None
     try:
-        # Create a new event loop to run the asynchronous inference function
-        print(f"Generating image for model: {model_str} with prompt: '{prompt}' and seed: {seed}")
+        print(f"Generating image for model: {model_str} with prompt: '{prompt}', seed: {seed}, batch_size: {batch_size}, priority: {priority}")
         loop = asyncio.new_event_loop()
-        result = loop.run_until_complete(infer(model_str, prompt, seed, inference_timeout))
+        result = loop.run_until_complete(
+            infer(model_str, prompt, seed, batch_size=batch_size, output_format=output_format, priority=priority)
+        )
     except (Exception, asyncio.CancelledError) as e:
-        # Handle any exceptions or cancelled tasks
         print(f"Error during generation for model {model_str}: {e}")
         result = None
     finally:
-        # Close the event loop
         loop.close()
         print(f"Event loop closed for model: {model_str}")
     return result
@@ -122,51 +118,41 @@ print("Creating Gradio interface...")
 with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
     gr.HTML("<center><h1>Multi-models-prompt-to-image-generation</h1></center>")
     with gr.Tab('Compare-6'):
-        # Text input for user prompt
         txt_input = gr.Textbox(label='Your prompt:', lines=4)
-        # Button to generate images
         gen_button = gr.Button('Generate up to 6 images in up to 3 minutes total')
         with gr.Row():
-            # Slider to select a seed for reproducibility
-            seed = gr.Slider(label="Use a seed to replicate the same image later (maximum 3999999999)", minimum=0, maximum=MAX_SEED, step=1, value=starting_seed, scale=3)
-            # Button to randomize the seed
-            seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary", scale=1)    
-        # Set up click event to randomize the seed
+            seed = gr.Slider(label="Seed (max 3999999999)", minimum=0, maximum=MAX_SEED, step=1, value=starting_seed, scale=3)
+            seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary", scale=1)
         seed_rand.click(randomize_seed, None, [seed], queue=False)
-        print("Seed randomization button set up.")
-        # Button click to start generation
-        gen_button.click(lambda s: gr.update(interactive=True), None)
-        print("Generation button set up.")
+
+        # Add batch size slider
+        batch_size_slider = gr.Slider(label="Batch Size", minimum=1, maximum=10, step=1, value=1)
+        output_format_dropdown = gr.Dropdown(["PNG", "JPEG"], label="Output Format", value="PNG")
+        priority_dropdown = gr.Dropdown(["low", "medium", "high"], label="Model Priority", value="medium")
 
         with gr.Row():
-            # Create image output components for each model
             output = [gr.Image(label=m, min_width=480) for m in default_models]
-            # Create hidden textboxes to store the current models
             current_models = [gr.Textbox(m, visible=False) for m in default_models]
             
-            # Set up generation events for each model and output image
             for m, o in zip(current_models, output):
                 print(f"Setting up generation event for model: {m.value}")
-                gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fnseed,
-                                  inputs=[m, txt_input, seed], outputs=[o], concurrency_limit=None, queue=False)
-                # The commented stop button could be used to cancel the generation event
-                #stop_button.click(lambda s: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
-        # Accordion to allow model selection
+                gen_event = gr.on(
+                    triggers=[gen_button.click, txt_input.submit],
+                    fn=gen_fnseed,
+                    inputs=[m, txt_input, seed, batch_size_slider, output_format_dropdown, priority_dropdown],
+                    outputs=[o],
+                    concurrency_limit=None,
+                    queue=False
+                )
         with gr.Accordion('Model selection'):
-            # Checkbox group to select up to 'num_models' different models
-            model_choice = gr.CheckboxGroup(models, label=f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
-            # Update image boxes and current models based on model selection
+            model_choice = gr.CheckboxGroup(models, label=f'Choose up to {int(num_models)} different models!', value=default_models, interactive=True)
             model_choice.change(update_imgbox, model_choice, output)
             model_choice.change(extend_choices, model_choice, current_models)
-            print("Model selection setup complete.")
         with gr.Row():
-            # Placeholder HTML to add additional UI elements if needed
-            gr.HTML(
-)
+            gr.HTML("<p>Additional UI elements can go here</p>")
 
-# Queue settings for handling multiple concurrent requests
 print("Setting up queue...")
 demo.queue(default_concurrency_limit=200, max_size=200)
 print("Launching Gradio interface...")
 demo.launch(show_api=False, max_threads=400)
-print("Gradio interface launched successfully.")
+print("Gradio interface launched successfully.")