Update app.py

app.py

@@ -1,120 +1,172 @@
 import gradio as gr
-import torch
-import asyncio
-import os
 from random import randint
-from threading import RLock
-from pathlib import Path
 from all_models import models
+
 from externalmod import gr_Interface_load, randomize_seed
 
-# Create a lock for thread safety
-lock = RLock()
+import asyncio
+import os
+from threading import RLock
 
-# Load Hugging Face token from environment variable
-HF_TOKEN = os.getenv("HF_TOKEN")
+# 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.
 
-# Function to load models with optimized settings
+# 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:
+        if model not in models_load.keys():
             try:
-                print(f"Loading model: {model}")
-                m = gr_Interface_load(
-                    f'models/{model}',
-                    hf_token=HF_TOKEN,
-                    torch_dtype=torch.float16  # Reduce memory usage
-                )
-                m.enable_model_cpu_offload()  # Offload to CPU when not in use
-                models_load[model] = m
-            except Exception as e:
-                print(f"Error loading model {model}: {e}")
-                models_load[model] = None
-
+                # 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.")
 
-# Constants
 num_models = 6
-starting_seed = randint(1941, 2024)
-MAX_SEED = 3999999999
+
+# 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}")
 
-# Update UI components
+# Extend the choices list to ensure it contains 'num_models' elements
 def extend_choices(choices):
-    return choices[:num_models] + ['NA'] * (num_models - len(choices))
+    print(f"Extending choices: {choices}")
+    extended = choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
+    print(f"Extended choices: {extended}")
+    return extended
 
+# Update the image boxes based on selected models
 def update_imgbox(choices):
-    choices_extended = extend_choices(choices)
-    return [gr.Image(None, label=m, visible=(m != 'NA')) for m in choices_extended]
-
-# Async inference function
-async def infer(model_str, prompt, seed=1):
-    if model_str not in models_load or models_load[model_str] is None:
-        print(f"Model {model_str} is unavailable.")
-        return None
-
-    kwargs = {"seed": seed}
+    print(f"Updating image boxes with choices: {choices}")
+    choices_plus = extend_choices(choices[:num_models])
+    imgboxes = [gr.Image(None, label=m, visible=(m != 'NA')) for m in choices_plus]
+    print(f"Updated image boxes: {imgboxes}")
+    return imgboxes
+
+# Asynchronous function to perform inference on a given model
+async def infer(model_str, prompt, seed=1, 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
     try:
-        print(f"Running inference for model: {model_str} with prompt: '{prompt}'")
-        result = await asyncio.to_thread(models_load[model_str].fn, prompt=prompt, **kwargs, token=HF_TOKEN)
-
-        if result:
-            with lock:
-                png_path = "image.png"
-                result.save(png_path)
-                return str(Path(png_path).resolve())
-    except torch.cuda.OutOfMemoryError:
-        print(f"CUDA memory error for {model_str}. Try reducing image size.")
-    except Exception as e:
-        print(f"Error during inference for {model_str}: {e}")
-
+        # 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"
+            result.save(png_path)
+            image = str(Path(png_path).resolve())
+            print(f"Result saved as image: {image}")
+        return image
+    print(f"No result for model: {model_str}")
     return None
 
-# Synchronous wrapper
+# Function to generate an image based on the given model, prompt, and seed
 def gen_fnseed(model_str, prompt, seed=1):
     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}")
         loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-        result = loop.run_until_complete(infer(model_str, prompt, seed))
-    except Exception as e:
-        print(f"Error generating image for {model_str}: {e}")
+        result = loop.run_until_complete(infer(model_str, prompt, seed, inference_timeout))
+    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
 
-# Gradio UI
+# Create the Gradio Blocks interface with a custom theme
 print("Creating Gradio interface...")
 with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
     gr.HTML("<center><h1>Compare-6</h1></center>")
-
     with gr.Tab('Compare-6'):
+        # Text input for user prompt
         txt_input = gr.Textbox(label='Your prompt:', lines=4)
-        gen_button = gr.Button('Generate up to 6 images')
-        seed = gr.Slider(label="Seed (0 to MAX)", minimum=0, maximum=MAX_SEED, value=starting_seed)
-        seed_rand = gr.Button("Randomize Seed 🎲")
-
+        # 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_rand.click(randomize_seed, None, [seed], queue=False)
-
-        output = [gr.Image(label=m) for m in models[:num_models]]
-        current_models = [gr.Textbox(m, visible=False) for m in models[:num_models]]
-
-        for m, o in zip(current_models, output):
-            gen_button.click(gen_fnseed, inputs=[m, txt_input, seed], outputs=[o], 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.")
+
+        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
         with gr.Accordion('Model selection'):
-            model_choice = gr.CheckboxGroup(models, label=f'Choose up to {num_models} models')
+            # 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.change(update_imgbox, model_choice, output)
             model_choice.change(extend_choices, model_choice, current_models)
-
-demo.queue(default_concurrency_limit=20, max_size=50)  # Adjusted for better stability
-demo.launch(show_api=False)
+            print("Model selection setup complete.")
+        with gr.Row():
+            # Placeholder HTML to add additional UI elements if needed
+            gr.HTML(
+)
+
+# 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.")