Update app.py

app.py

@@ -1,132 +1,172 @@
 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.
 
-# Load Hugging Face token from environment variable
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-
-# Load models
-def load_models(models):
-    global models_loaded
-    models_loaded = {}
+# 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:
-        try:
-            print(f"Loading model: {model}")
-            m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
-            print(f"Successfully loaded model: {model}")
-            models_loaded[model] = m
-        except Exception as e:
-            print(f"Error loading model {model}: {e}")
-            models_loaded[model] = None
-
+        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_models(models)
-print("Models loaded.")
+load_fn(models)
+print("Models loaded successfully.")
 
-# Global variables
 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}")
 
-# Extend model choices to match the required number
+# Extend the choices list to ensure it contains 'num_models' elements
 def extend_choices(choices):
-    return choices[:num_models] + (['NA'] * (num_models - len(choices[:num_models])))
-
-# Function to perform inference asynchronously
-async def infer(model_name, prompt, seed, batch_size, priority, timeout):
+    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):
+    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:
-        kwargs = {"seed": seed, "batch_size": batch_size, "priority": priority}
-        print(f"Running inference for model: {model_name} with prompt: {prompt} and seed: {seed}")
-        
-        task = asyncio.create_task(
-            asyncio.to_thread(
-                models_loaded[model_name].fn,
-                prompt=prompt,
-                **kwargs,
-                token=HF_TOKEN
-            )
-        )
+        # Wait for the task to complete within the specified timeout
         result = await asyncio.wait_for(task, timeout=timeout)
-        return result
-    except Exception as e:
-        print(f"Inference failed for model {model_name}: {e}")
-        return None
+        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
 
-# Generate images for each model
-def generate_image(model_name, prompt, seed, batch_size, output_format, priority):
-    if model_name == 'NA' or models_loaded.get(model_name) is None:
-        print(f"Skipping model: {model_name} (Not available or NA)")
+# 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_name, prompt, seed, batch_size, priority, inference_timeout)
-        )
-        if result:
-            output_path = f"output.{output_format.lower()}"
-            result.save(output_path)
-            print(f"Image saved: {output_path}")
-            return output_path
-    except Exception as e:
-        print(f"Error generating image for model {model_name}: {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
 
-    return None
-
-# Gradio Interface
+# 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>Multi-Model Prompt-to-Image Generator</h1></center>")
-    
-    # Input area
-    with gr.Tab('Generate'):
-        txt_input = gr.Textbox(label='Your Prompt', lines=4)
-        gen_button = gr.Button('Generate Images')
-
+    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)
+        # Button to generate images
+        gen_button = gr.Button('Generate up to 6 images in up to 3 minutes total')
         with gr.Row():
-            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=starting_seed)
-            seed_rand = gr.Button("Randomize Seed 🎲")
-            batch_size = gr.Slider(label="Batch Size", minimum=1, maximum=10, step=1, value=1)
-            output_format = gr.Dropdown(["PNG", "JPEG"], label="Output Format", value="PNG")
-            priority = gr.Dropdown(["low", "medium", "high"], label="Priority", value="medium")
-
+            # 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)
+        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.")
 
-        # Outputs
         with gr.Row():
-            output_images = [gr.Image(label=f"Model {i+1}") for i in range(num_models)]
-        
-        # Model selection
-        with gr.Accordion("Model Selection", open=False):
-            model_choice = gr.CheckboxGroup(models, label=f"Choose up to {num_models} models", value=models[:num_models])
-        
-        # Generation logic
-        def generate_images(prompt, seed, batch_size, output_format, priority, selected_models):
-            results = []
-            selected_models = extend_choices(selected_models)
+            # 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]
             
-            for model in selected_models:
-                result = generate_image(model, prompt, seed, batch_size, output_format, priority)
-                results.append(result)
-
-            return results
-
-        gen_button.click(
-            fn=generate_images,
-            inputs=[txt_input, seed, batch_size, output_format, priority, model_choice],
-            outputs=output_images
-        )
+            # 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'):
+            # 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)
+            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)
+demo.launch(show_api=False, max_threads=400)
+print("Gradio interface launched successfully.")