import gradio as gr import requests import io import random import os import time from PIL import Image, UnidentifiedImageError from deep_translator import GoogleTranslator import json import uuid from urllib.parse import quote import traceback # Project by Nymbo # --- Constants --- API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell" API_TOKEN = os.getenv("HF_READ_TOKEN") if not API_TOKEN: print("WARNING: HF_READ_TOKEN environment variable not set. API calls may fail.") headers = {"Authorization": f"Bearer {API_TOKEN}"} if API_TOKEN else {} timeout = 100 # seconds for API call timeout IMAGE_DIR = "temp_generated_images" # Directory to store temporary images ARINTELLI_REDIRECT_BASE = "https://arintelli.com/app/" # Your redirector URL # --- Ensure temporary directory exists --- try: os.makedirs(IMAGE_DIR, exist_ok=True) print(f"Confirmed temporary image directory exists: {IMAGE_DIR}") except OSError as e: print(f"ERROR: Could not create directory {IMAGE_DIR}: {e}") # This is critical, so raise an error to prevent app start if dir fails raise gr.Error(f"Fatal Error: Cannot create temporary image directory: {e}") # --- Get Absolute Path for allowed_paths --- # This needs to be done *before* calling launch() absolute_image_dir = os.path.abspath(IMAGE_DIR) print(f"Absolute path for allowed_paths: {absolute_image_dir}") # Function to query the API and return the generated image and download link def query(prompt, negative_prompt="", steps=30, cfg_scale=7, seed=-1, width=1024, height=1024): # Removed sampler and strength as they are not explicitly used in the payload below # Note: If the API endpoint *does* support sampler/strength, add them back to the payload if not prompt or not prompt.strip(): print("Empty prompt received.") # Return None for image and an informative message for the HTML component return None, "

Please enter a prompt.

" key = random.randint(0, 999) print(f"\n--- Generation {key} Started ---") # Translation try: translated_prompt = GoogleTranslator(source='auto', target='en').translate(prompt) except Exception as e: translated_prompt = prompt # Fallback to original if translation fails # Add suffix to prompt final_prompt = f"{translated_prompt} | ultra detail, ultra elaboration, ultra quality, perfect." print(f'Generation {key} prompt: {final_prompt}') # Prepare the payload for the API call payload = { "inputs": final_prompt, "negative_prompt": negative_prompt, # Assuming API accepts negative_prompt "steps": steps, "guidance_scale": cfg_scale, # API often uses guidance_scale "seed": seed if seed != -1 else random.randint(1, 1000000000), "parameters": { "width": width, "height": height, # If the API supports other params like 'steps', 'guidance_scale', they might belong here or top-level } # Removed 'is_negative' as negative_prompt is usually passed directly # Removed 'strength' and 'sampler' as they weren't in the target API structure } # API Call Section try: print(f"Sending request to API: {API_URL}") if not headers: print("WARNING: Authorization header is missing (HF_READ_TOKEN not set?)") return None, "

Configuration Error: API Token missing.

" response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout) response.raise_for_status() image_bytes = response.content if not image_bytes or len(image_bytes) < 100: print(f"Error: Received empty or very small response content (length: {len(image_bytes)}). Potential API issue.") return None, "

API returned invalid image data.

" try: image = Image.open(io.BytesIO(image_bytes)) except UnidentifiedImageError as img_err: print(f"Error: Could not identify or open image from API response bytes: {img_err}") return None, "

Failed to process image data from API.

" # --- Save image and create download link --- filename = f"{int(time.time())}_{uuid.uuid4().hex[:8]}.png" save_path = os.path.join(IMAGE_DIR, filename) absolute_save_path = os.path.abspath(save_path) try: print(f"Attempting to save image to: {absolute_save_path}") image.save(save_path, "PNG") if os.path.exists(save_path): file_size = os.path.getsize(save_path) if file_size < 100: print(f"WARNING: Saved file {save_path} is very small ({file_size} bytes). May indicate an issue.") else: print(f"CRITICAL ERROR: File NOT found at {save_path} (Absolute: {absolute_save_path}) immediately after saving!") return image, "

Internal Error: Failed to confirm image file save.

" # Determine space name (adjust logic if API_URL format differs) try: space_name = "greendra-flux-1-schnell-serverless" # A more robust way might involve getting the space ID from env vars if available except IndexError: print("WARNING: Could not reliably determine space name from API_URL. Using a default.") space_name = "unknown-flux-space" # Provide a fallback relative_file_url = f"/gradio_api/file={save_path}" encoded_file_url = quote(relative_file_url) arintelli_url = f"{ARINTELLI_REDIRECT_BASE}?download_url={encoded_file_url}&space_name={space_name}" print(f"Generation link: {arintelli_url}") # Use Gradio's primary button style for the link download_html = ( f'
' # Added margin-top f'' f'Download Image' f'' f'
' ) print(f"--- Generation {key} Completed Successfully ---") return image, download_html except (OSError, IOError) as save_err: print(f"CRITICAL ERROR: Failed to save image to {save_path} (Absolute: {absolute_save_path}): {save_err}") traceback.print_exc() return image, f"

Internal Error: Failed to save image file. Details: {save_err}

" except Exception as e: print(f"Error during link creation or unexpected save issue: {e}") traceback.print_exc() return image, "

Internal Error creating download link.

" # --- Exception Handling for API Call --- except requests.exceptions.Timeout: print(f"Error: Request timed out after {timeout} seconds.") return None, "

Request timed out. The model is taking too long.

" except requests.exceptions.HTTPError as e: status_code = e.response.status_code error_text = e.response.text try: error_data = e.response.json() error_text = error_data.get('error', error_text) if isinstance(error_text, dict) and 'message' in error_text: error_text = error_text['message'] except json.JSONDecodeError: pass print(f"Error: Failed API call. Status: {status_code}, Response: {error_text}") if status_code == 503: estimated_time = error_data.get("estimated_time") if 'error_data' in locals() and isinstance(error_data, dict) else None if estimated_time: error_message = f"Model is loading (503), please wait. Est. time: {estimated_time:.1f}s. Try again." else: error_message = f"Service unavailable (503). Model might be loading or down. Try again later." elif status_code == 400: error_message = f"Bad Request (400): Check parameters. API Error: {error_text}" elif status_code == 422: error_message = f"Validation Error (422): Input invalid. API Error: {error_text}" elif status_code == 401 or status_code == 403: error_message = f"Authorization Error ({status_code}): Check your API Token (HF_READ_TOKEN)." else: error_message = f"API Error: {status_code}. Details: {error_text}" return None, f"

{error_message}

" except Exception as e: print(f"An unexpected error occurred: {e}") traceback.print_exc() return None, f"

An unexpected error occurred: {e}

" # CSS to style the app css = """ #app-container { max-width: 800px; margin-left: auto; margin-right: auto; } textarea:focus { background: #0d1117 !important; } #download-link-container p { margin-top: 10px; font-size: 0.9em; } """ # Build the Gradio UI with Blocks with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app: gr.HTML("

FLUX.1-Schnell

") with gr.Column(elem_id="app-container"): with gr.Row(): with gr.Column(elem_id="prompt-container"): with gr.Row(): text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input") with gr.Row(): with gr.Accordion("Advanced Settings", open=False): negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input") with gr.Row(): width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32) height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32) steps = gr.Slider(label="Sampling steps", value=30, minimum=1, maximum=100, step=1) # Default updated based on query function default cfg = gr.Slider(label="CFG Scale (guidance_scale)", value=7, minimum=1, maximum=20, step=1) # Label updated # Removed strength and sampler sliders as they are not passed to query seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1, info="Set to -1 for random seed") with gr.Row(): text_button = gr.Button("Run", variant='primary', elem_id="gen-button") # --- Output Components --- with gr.Row(): image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery") with gr.Row(): # HTML component to display status messages or the download link download_link_display = gr.HTML(elem_id="download-link-container") # Bind the button to the query function text_button.click( query, # Ensure inputs match the query function definition inputs=[text_prompt, negative_prompt, steps, cfg, seed, width, height], # Outputs go to the image and HTML components outputs=[image_output, download_link_display] ) # Launch the Gradio app with allowed_paths print("Starting Gradio app...") app.launch( show_api=False, share=False, allowed_paths=[absolute_image_dir] # Added allowed_paths )