Update app.py

app.py

@@ -1,99 +1,123 @@
 import gradio as gr
-from random import randint
-from all_models import models
-
-from externalmod import gr_Interface_load, randomize_seed
-
-import asyncio
+import requests
+import io
+import random
 import os
+import time
+from PIL import Image
+import json
 from threading import RLock
-lock = RLock()
-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.
-
-
-def load_fn(models):
-    global models_load
-    models_load = {}
-    
-    for model in models:
-        if model not in models_load.keys():
-            try:
-                m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
-            except Exception as error:
-                print(error)
-                m = gr.Interface(lambda: None, ['text'], ['image'])
-            models_load.update({model: m})
 
+# Project by Nymbo
 
-load_fn(models)
-
-
-num_models = 6
-
-default_models = models[:num_models]
-inference_timeout = 600
-MAX_SEED=3999999999
-starting_seed = randint(1941, 2024)
-
-def extend_choices(choices):
-    return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
+# Base API URL for Hugging Face inference
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
+# Retrieve the API token from environment variables
+API_TOKEN = os.getenv("HF_READ_TOKEN")
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+# Timeout for requests
+timeout = 100
 
+lock = RLock()
 
-def update_imgbox(choices):
-    choices_plus = extend_choices(choices[:num_models])
-    return [gr.Image(None, label=m, visible=(m!='NA')) for m in choices_plus]
+# Function to query the Hugging Face API for image generation
+def query(prompt, model, negative_prompt, steps, cfg_scale, sampler, seed, strength, width, height):
+    # Debug log to indicate function start
+    print("Starting query function...")
+    # Print the parameters for debugging purposes
+    print(f"Prompt: {prompt}")
+    print(f"Model: {model}")
+    print(f"Parameters - Steps: {steps}, CFG Scale: {cfg_scale}, Seed: {seed}, Strength: {strength}, Width: {width}, Height: {height}")
+
+    # Check if the prompt is empty or None
+    if prompt == "" or prompt is None:
+        print("Prompt is empty or None. Exiting query function.")  # Debug log
+        return None
 
-async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
-    from pathlib import Path
-    kwargs = {}
-    noise = ""
-    kwargs["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)
+    # Randomly select an API token from available options to distribute the load
+    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN"), os.getenv("HF_READ_TOKEN_2"), os.getenv("HF_READ_TOKEN_3"), os.getenv("HF_READ_TOKEN_4"), os.getenv("HF_READ_TOKEN_5")])
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    print(f"Selected API token: {API_TOKEN}")  # Debug log
+
+    # Enhance the prompt with additional details for better quality
+    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
+    print(f'Generation: {prompt}')  # Debug log
+
+    # Set the API URL based on the selected model
+    if model == 'Stable Diffusion XL':
+        API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
+    # Add more model options as needed
+    print(f"API URL set to: {API_URL}")  # Debug log
+
+    # Define the payload for the request
+    payload = {
+        "inputs": prompt,
+        "negative_prompt": negative_prompt,
+        "steps": steps,  # Number of sampling steps
+        "cfg_scale": cfg_scale,  # Scale for controlling adherence to prompt
+        "seed": seed if seed != -1 else random.randint(1, 1000000000),  # Random seed for reproducibility
+        "strength": strength,  # How strongly the model should transform the image
+        "parameters": {
+            "width": width,  # Width of the generated image
+            "height": height  # Height of the generated image
+        }
+    }
+    print(f"Payload: {json.dumps(payload, indent=2)}")  # Debug log
+
+    # Make a request to the API to generate the image
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
+        print(f"Response status code: {response.status_code}")  # Debug log
+    except requests.exceptions.RequestException as e:
+        # Log any request exceptions and raise an error for the user
+        print(f"Request failed: {e}")  # Debug log
+        raise gr.Error(f"Request failed: {e}")
+
+    # Check if the response status is not successful
+    if response.status_code != 200:
+        print(f"Error: Failed to retrieve image. Response status: {response.status_code}")  # Debug log
+        print(f"Response content: {response.text}")  # Debug log
+        if response.status_code == 400:
+            raise gr.Error(f"{response.status_code}: Bad Request - There might be an issue with the input parameters.")
+        elif response.status_code == 401:
+            raise gr.Error(f"{response.status_code}: Unauthorized - Please check your API token.")
+        elif response.status_code == 403:
+            raise gr.Error(f"{response.status_code}: Forbidden - You do not have permission to access this model.")
+        elif response.status_code == 404:
+            raise gr.Error(f"{response.status_code}: Not Found - The requested model could not be found.")
+        elif response.status_code == 503:
+            raise gr.Error(f"{response.status_code}: The model is being loaded. Please try again later.")
+        else:
+            raise gr.Error(f"{response.status_code}: An unexpected error occurred.")
+    
     try:
-        result = await asyncio.wait_for(task, timeout=timeout)
-    except (Exception, asyncio.TimeoutError) as e:
-        print(e)
-        print(f"Task timed out: {model_str}")
-        if not task.done(): task.cancel()
-        result = None
-    if task.done() and result is not None:
-        with lock:
-            png_path = "image.png"
-            result.save(png_path)
-            image = str(Path(png_path).resolve())
+        # Attempt to read the image from the response content
+        image_bytes = response.content
+        image = Image.open(io.BytesIO(image_bytes))
+        print(f'Generation completed! ({prompt})')  # Debug log
         return image
-    return None
+    except Exception as e:
+        # Handle any errors that occur when opening the image
+        print(f"Error while trying to open image: {e}")  # Debug log
+        return None
 
+# Custom CSS to hide the footer in the interface
+css = """
+* {}
+footer {visibility: hidden !important;}
+"""
 
-def gen_fnseed(model_str, prompt, seed=1):
-    if model_str == 'NA':
-        return None
-    try:
-        loop = asyncio.new_event_loop()
-        result = loop.run_until_complete(infer(model_str, prompt, seed, inference_timeout))
-    except (Exception, asyncio.CancelledError) as e:
-        print(e)
-        print(f"Task aborted: {model_str}")
-        result = None
-    finally:
-        loop.close()
-    return result
-
-with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
-    gr.HTML("<h1>Compare 6</h1>")
-    with gr.Tab('Compare-6'): 
+print("Initializing Gradio interface...")  # Debug log
+
+# Define the Gradio interface
+with gr.Blocks(theme='Nymbo/Nymbo_Theme') as demo:
+    # Tab for basic settings
+    with gr.Tab('Basic Settings'):
         txt_input = gr.Textbox(label='Your prompt:', lines=4)
-        gen_button = gr.Button('Generate up to 6 images in up to 3 minutes total')
-        with gr.Row():
-            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)
-            seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary", scale=1)    
-        seed_rand.click(randomize_seed, None, [seed], queue=False)
-        #stop_button = gr.Button('Stop', variant = 'secondary', interactive = False)
-               
-        gen_button.click(lambda s: gr.update(interactive = True), None)
+        model = gr.Radio(label="Select a model", value="Stable Diffusion XL", choices=["Stable Diffusion XL", "Stable Diffusion 3", "FLUX.1 [Schnell]", "RealVisXL v4.0", "Duchaiten Real3D NSFW XL", "Tempest v0.1"], interactive=True)
+        gen_button = gr.Button('Generate Image')
         
+    # Tab for advanced settings
     with gr.Tab("Advanced Settings"):
         with gr.Row():
             # Textbox for specifying elements to exclude from the image
@@ -119,34 +143,9 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
             # Radio buttons for selecting the sampling method
             method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
 
-        gr.HTML(
-        """
-            <div style="text-align: center; max-width: 1200px; margin: 0 auto;">
-              <div>
-                <body>
-                <div class="center"><p style="margin-bottom: 10px; color: #000000;">Scroll down to see more images and select models.</p>
-                </div>
-                </body>
-              </div>
-            </div>
-        """
-               )
-        with gr.Row():
-            output = [gr.Image(label = m, min_width=480) for m in default_models]
-            current_models = [gr.Textbox(m, visible = False) for m in default_models]
-                        
-            for m, o in zip(current_models, output):
-                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)
-                #stop_button.click(lambda s: gr.update(interactive = False), None, stop_button, cancels = [gen_event])
-        with gr.Accordion('Model selection'):
-            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)
-            #model_choice = gr.CheckboxGroup(models, label = f'Choose up to {num_models} different models from the 2 available! Untick them to only use one!', value = default_models, multiselect = True, max_choices = num_models, interactive = True, filterable = False)
-            model_choice.change(update_imgbox, model_choice, output)
-            model_choice.change(extend_choices, model_choice, current_models)
-        with gr.Row():
-            gr.HTML(
-)
+    # Set up button click event to call the query function
+    gen_button.click(query, inputs=[txt_input, model, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=gr.Image(type="pil", label="Generated Image"))
 
-demo.queue(default_concurrency_limit=200, max_size=200)
+print("Launching Gradio interface...")  # Debug log
+# Launch the Gradio interface without showing the API or sharing externally
 demo.launch(show_api=False, max_threads=400)