Update app.py

app.py

@@ -7,261 +7,113 @@ 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.")
-
+API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
+API_TOKEN = os.getenv("HF_READ_TOKEN")
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
 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.")
+def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
+    if prompt == "" or prompt is None:
         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.")
+    key = random.randint(0, 999)
+    
+    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    
+    # Translate the prompt from Russian to English if necessary
+    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
+    print(f'\033[1mGeneration {key} translation:\033[0m {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}')
-
+    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
+    print(f'\033[1mGeneration {key}:\033[0m {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.
+        "inputs": prompt,
+        "is_negative": is_negative,
+        "steps": steps,
+        "cfg_scale": cfg_scale,
+        "seed": seed if seed != -1 else random.randint(1, 1000000000),
+        "strength": strength,
         "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,
+            "width": width,  # Pass the width to the API
+            "height": height  # Pass the height to the API
         }
     }
-    # 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
+    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
+    if response.status_code != 200:
+        print(f"Error: Failed to get image. Response status: {response.status_code}")
+        print(f"Response content: {response.text}")
+        if response.status_code == 503:
+            raise gr.Error(f"{response.status_code} : The model is being loaded")
+        raise gr.Error(f"{response.status_code}")
+    
     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')
+        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
         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 ---
+    except Exception as e:
+        print(f"Error when trying to open the image: {e}")
+        return None
 
 # CSS to style the app
 css = """
 #app-container {
-    max-width: 960px; /* Slightly wider */
+    max-width: 800px;
     margin-left: auto;
     margin-right: auto;
 }
-/* Add more styling if desired */
+textarea:focus {
+    background: #0d1117 !important;
+}
 """
 
-# 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:
+with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
     # Add a title to the app
-    gr.HTML("<center><h1>FLUX.1-Dev Image Generator</h1></center>")
-
+    gr.HTML("<center><h1>FLUX.1-Schnell</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."
-                 )
+            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")
+                
+                # Accordion for advanced settings
+                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=4, minimum=1, maximum=100, step=1)
+                        cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
+                        strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
+                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random
+                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
 
         # Add a button to trigger the image generation
         with gr.Row():
-            text_button = gr.Button("Generate Image", variant='primary', elem_id="gen-button")
-
+            text_button = gr.Button("Run", 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
-        )
-
+            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
+        
+        # Bind the button to the query function with the added width and height inputs
+        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)
 
 # 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)