app.py

import gradio as gr
import requests
import io
import random
import os
import time
from PIL import Image
from deep_translator import GoogleTranslator
import json
from typing import Callable, List, Any, Literal # Added for type hinting Examples

# Project by Nymbo

API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
API_TOKEN = os.getenv("HF_READ_TOKEN") # Your main token for primary use
# Consider adding more read tokens if you have them for resilience
ADDITIONAL_TOKENS = [t for t in os.getenv("HF_EXTRA_TOKENS", "").split(',') if t] # Example: HF_EXTRA_TOKENS="token1,token2"
ALL_TOKENS = [API_TOKEN] + ADDITIONAL_TOKENS
if not API_TOKEN:
    print("Warning: HF_READ_TOKEN is not set. API calls may fail.")
    # Optional: raise an error or use a dummy token if needed
    # raise ValueError("HF_READ_TOKEN environment variable is required.")

timeout = 100

# Function to query the API and return the generated image
# Added type hints for clarity
def query(
    prompt: str,
    negative_prompt: str = "(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",
    steps: int = 35,
    cfg_scale: float = 7.0,
    sampler: str = "DPM++ 2M Karras", # Matched default in UI
    seed: int = -1,
    strength: float = 0.7,
    width: int = 1024,
    height: int = 1024
) -> Image.Image | None:

    if not prompt: # Simplified check
        gr.Warning("Prompt cannot be empty.")
        return None
    if not ALL_TOKENS:
        gr.Error("No Hugging Face API tokens available.")
        return None

    key = random.randint(0, 9999)
    start_time = time.time()

    # Rotate through available tokens
    selected_api_token = random.choice(ALL_TOKENS)
    headers = {"Authorization": f"Bearer {selected_api_token}"}

    translated_prompt = prompt
    try:
        # Simple check if likely Russian (Cyrillic) - adjust if needed
        if any('\u0400' <= char <= '\u04FF' for char in prompt):
             translated_prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
             print(f'\033[1mGeneration {key} translation:\033[0m {translated_prompt}')
        else:
             # Assume English or other non-Russian if no Cyrillic detected
             pass
    except Exception as e:
        print(f"Translation failed: {e}. Using original prompt.")
        # Decide if you want to raise an error or just proceed
        # gr.Warning(f"Translation failed: {e}. Using original prompt.")

    # Add some extra flair to the prompt
    enhanced_prompt = f"{translated_prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mGeneration {key} starting:\033[0m {enhanced_prompt}')

    # Prepare the payload for the API call, including width and height
    payload = {
        "inputs": enhanced_prompt,
        "negative_prompt": negative_prompt, # Use the negative prompt input
        # Note: The underlying API might not support all parameters directly.
        # Check the specific model card for supported parameters.
        # We send common ones; the model API will use what it understands.
        "parameters": {
            "num_inference_steps": steps,
            "guidance_scale": cfg_scale,
            "seed": seed if seed != -1 else random.randint(1, 2**32 - 1), # Use larger seed range
            "strength": strength, # Often used for img2img, may not apply here
            "width": width,
            "height": height,
            # Sampler might not be directly controllable via this endpoint structure
            # The model often uses its default or recommended sampler.
            # Sending it anyway in case future API versions support it.
            "scheduler": sampler,
        }
    }
    # Clean up payload if values are None or default where API expects omission
    if seed == -1:
        del payload["parameters"]["seed"] # Let API choose random if -1
    if not negative_prompt:
         # If empty string, some APIs prefer omitting the key entirely
        del payload["negative_prompt"]


    print(f"Payload for {key}: {json.dumps(payload, indent=2)}")

    # Send the request to the API and handle the response
    try:
        response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
        response.raise_for_status() # Raises HTTPError for bad responses (4xx or 5xx)

        # Convert the response content into an image
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        end_time = time.time()
        print(f'\033[1mGeneration {key} completed in {end_time - start_time:.2f}s!\033[0m')
        return image

    except requests.exceptions.Timeout:
        print(f"Error: Request timed out after {timeout} seconds.")
        raise gr.Error(f"Request timed out ({timeout}s). Model might be busy. Try again later.")
    except requests.exceptions.RequestException as e:
        status_code = e.response.status_code if e.response is not None else "N/A"
        error_content = e.response.text if e.response is not None else str(e)
        print(f"Error: API request failed. Status: {status_code}, Content: {error_content}")
        if status_code == 503:
            # Check for specific error messages if available
            if "is currently loading" in error_content:
                 raise gr.Error("Model is loading. Please wait a moment and try again.")
            else:
                 raise gr.Error("Model service unavailable (503). It might be overloaded or down. Try again later.")
        elif status_code == 400:
             raise gr.Error(f"Bad Request (400). Check parameters. API response: {error_content[:200]}")
        elif status_code == 429:
             raise gr.Error("Too many requests (429). Rate limit hit. Please wait.")
        else:
            raise gr.Error(f"API Error ({status_code}). Response: {error_content[:200]}")
    except (OSError, json.JSONDecodeError, IOError) as e:
         # Catch potential issues with image decoding or other file errors
         print(f"Error processing response or image: {e}")
         raise gr.Error(f"Failed to process the image response: {e}")
    except Exception as e: # Catch any other unexpected errors
        print(f"An unexpected error occurred: {e}")
        import traceback
        traceback.print_exc() # Print full traceback to console logs
        raise gr.Error(f"An unexpected error occurred: {e}")


# --- Gradio UI ---

# CSS to style the app
css = """
#app-container {
    max-width: 960px; /* Slightly wider */
    margin-left: auto;
    margin-right: auto;
}
/* Add more styling if desired */
"""

# Default negative prompt can be reused
default_negative_prompt = "(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"

# Define the examples
# Each inner list corresponds to the inputs of the 'query' function IN ORDER:
# [prompt, negative_prompt, steps, cfg_scale, sampler, seed, strength, width, height]
example_list = [
    [
        "Epic cinematic shot of a medieval knight kneeling in a misty forest, volumetric lighting, hyperrealistic photo, 8k",
        default_negative_prompt, 40, 7.5, "DPM++ 2M Karras", 12345, 0.7, 1024, 1024
    ],
    [
        "Studio Ghibli style illustration of a cozy bakery storefront on a rainy day, warm lighting, detailed",
        default_negative_prompt, 30, 6.0, "Euler a", 54321, 0.7, 1024, 1024
    ],
    [
        "Macro photograph of a dewdrop on a spider web, intricate details, shallow depth of field, natural lighting",
        "blurry, unfocused, cartoon", 50, 8.0, "DPM++ 2M Karras", -1, 0.7, 1024, 1024 # Random seed
    ],
    [
        "Steampunk astronaut exploring an alien jungle landscape, brass and copper details, vibrant bioluminescent plants, wide angle",
        default_negative_prompt, 35, 7.0, "DPM++ SDE Karras", 98765, 0.7, 1216, 832 # Different aspect ratio
    ],
    [
        "Abstract geometric art, vibrant contrasting colors, sharp edges, minimalistic design, 4k wallpaper",
        "photorealistic, noisy, cluttered", 25, 5.0, "Euler", -1, 0.7, 1024, 1024
    ],
     [
        "Кот в очках читает книгу у камина", # Example in Russian for translation testing
        default_negative_prompt, 35, 7.0, "DPM++ 2M Karras", 11223, 0.7, 1024, 1024
    ]
]


# Build the Gradio UI with Blocks
# Use the custom theme if it's available, otherwise fallback to default
try:
    theme = gr.themes.Base.load('Nymbo/Nymbo_Theme')
except Exception:
    print("Could not load Nymbo/Nymbo_Theme, using default theme.")
    theme = gr.themes.Default()

with gr.Blocks(theme=theme, css=css) as app:
    # Add a title to the app
    gr.HTML("<center><h1>FLUX.1-Dev Image Generator</h1></center>")

    # Container for all the UI elements
    with gr.Column(elem_id="app-container"):
        # Add a text input for the main prompt
        with gr.Row():
            with gr.Column(scale=3): # Give prompt more width
                text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here (English or Russian)", lines=3, elem_id="prompt-text-input") # Increased lines

            with gr.Column(scale=1): # Negative prompt column
                 negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What to avoid...", value=default_negative_prompt, lines=3, elem_id="negative-prompt-text-input")

        # Accordion for advanced settings
        with gr.Accordion("Advanced Settings", open=False):
             with gr.Row():
                 width = gr.Slider(label="Width", value=1024, minimum=256, maximum=1216, step=64) # Adjusted steps/min
                 height = gr.Slider(label="Height", value=1024, minimum=256, maximum=1216, step=64) # Adjusted steps/min
             with gr.Row():
                 steps = gr.Slider(label="Sampling steps", value=35, minimum=10, maximum=100, step=1) # Adjusted min
                 cfg = gr.Slider(label="CFG Scale", value=7.0, minimum=1.0, maximum=20.0, step=0.5) # Added step
                 strength = gr.Slider(label="Strength (Img2Img)", value=0.7, minimum=0.0, maximum=1.0, step=0.01, info="Primarily for Image-to-Image tasks, may have limited effect here.") # Added step and info
             with gr.Row():
                 seed = gr.Slider(label="Seed (-1 for random)", value=-1, minimum=-1, maximum=2**32 - 1, step=1) # Increased max seed
                 method = gr.Radio(
                     label="Sampling method (Scheduler)",
                     value="DPM++ 2M Karras",
                     choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"],
                     info="Note: Model API might use its default scheduler regardless of selection."
                 )

        # Add a button to trigger the image generation
        with gr.Row():
            text_button = gr.Button("Generate Image", variant='primary', elem_id="gen-button")

        # Image output area to display the generated image
        with gr.Row():
            image_output = gr.Image(type="pil", label="Generated Image", elem_id="gallery") # Changed label


        # --- Add Examples Component Here ---
        with gr.Row():
             gr.Examples(
                 examples=example_list,
                 # List ALL components that correspond to the example list order
                 inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height],
                 outputs=image_output, # The component to display the output
                 fn=query, # The function to run TO GENERATE the examples for caching
                 cache_examples=True, # << ENABLE CACHING HERE
                 label="Examples (Click to Run & View Cached Result)", # Customize label
                 examples_per_page=6 # Adjust how many show per page
                 # run_on_click=True # Optional: Set True if you want clicking to trigger 'query' again, even if cached. Usually False when caching.
             )

        # Bind the main button to the query function
        # Ensure the order matches the function definition (excluding self if it were a class method)
        text_button.click(
            query,
            inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height],
            outputs=image_output,
            api_name="generate_image" # Optional: Set API name for potential external calls
        )


# Launch the Gradio app
# share=False is correct for Hugging Face Spaces deployment
# show_api=False is fine unless you specifically need to expose the API endpoint documentation
# debug=True can be useful for development but remove for production/sharing
app.launch(show_api=False, share=False)