app.py

import gradio as gr
import numpy as np
import random
import os
import tempfile
from PIL import Image, ImageOps
import pillow_heif  # For HEIF/AVIF support
import io
import fal_client
import base64

# --- Constants ---
MAX_SEED = np.iinfo(np.int32).max

def load_client():
    """Initialize the FAL Client through HF"""
    # Register HEIF opener with PIL for AVIF/HEIF support
    pillow_heif.register_heif_opener()
    
    # Get token from environment variable
    hf_token = os.getenv("HF_TOKEN")
    if not hf_token:
        raise gr.Error("HF_TOKEN environment variable not found. Please add your Hugging Face token to the Space settings.")
    
    # Set the HF token for fal_client to use HF routing
    os.environ["FAL_KEY"] = hf_token
    return True

def query_api(image_bytes, prompt, seed, guidance_scale, steps, progress_callback=None):
    """Send request using fal_client"""
    
    load_client()
    
    if progress_callback:
        progress_callback(0.1, "Submitting request...")
    
    # Convert image bytes to base64
    image_base64 = base64.b64encode(image_bytes).decode('utf-8')
    
    # Create a temporary file for the image
    with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file:
        temp_file.write(image_bytes)
        temp_file_path = temp_file.name
    
    def on_queue_update(update):
        if isinstance(update, fal_client.InProgress):
            for log in update.logs:
                print(f"FAL Log: {log['message']}")
                if progress_callback:
                    progress_callback(0.5, f"Processing: {log['message'][:50]}...")
    
    try:
        if progress_callback:
            progress_callback(0.3, "Connecting to FAL API...")
        
        # Use fal_client.subscribe following the pattern you provided
        result = fal_client.subscribe(
            "fal-ai/flux-kontext/dev",
            arguments={
                "prompt": prompt,
                "image_url": f"data:image/png;base64,{image_base64}",
                "seed": seed,
                "guidance_scale": guidance_scale,
                "num_inference_steps": steps,
            },
            with_logs=True,
            on_queue_update=on_queue_update,
        )
        
        print(f"FAL Result: {result}")
        
        if progress_callback:
            progress_callback(0.9, "Processing result...")
        
        # Handle the result
        if isinstance(result, dict):
            if 'images' in result and len(result['images']) > 0:
                # Get the first image
                image_info = result['images'][0]
                if isinstance(image_info, dict) and 'url' in image_info:
                    # Download image from URL
                    import requests
                    img_response = requests.get(image_info['url'])
                    if img_response.status_code == 200:
                        if progress_callback:
                            progress_callback(1.0, "Complete!")
                        return img_response.content
                    else:
                        raise gr.Error(f"Failed to download result image: {img_response.status_code}")
                elif isinstance(image_info, str):
                    # Direct URL
                    import requests
                    img_response = requests.get(image_info)
                    if img_response.status_code == 200:
                        if progress_callback:
                            progress_callback(1.0, "Complete!")
                        return img_response.content
            elif 'image' in result:
                # Single image field
                if isinstance(result['image'], dict) and 'url' in result['image']:
                    import requests
                    img_response = requests.get(result['image']['url'])
                    if img_response.status_code == 200:
                        if progress_callback:
                            progress_callback(1.0, "Complete!")
                        return img_response.content
                elif isinstance(result['image'], str):
                    # Could be URL or base64
                    if result['image'].startswith('http'):
                        import requests
                        img_response = requests.get(result['image'])
                        if img_response.status_code == 200:
                            if progress_callback:
                                progress_callback(1.0, "Complete!")
                            return img_response.content
                    else:
                        # Assume base64
                        try:
                            if progress_callback:
                                progress_callback(1.0, "Complete!")
                            return base64.b64decode(result['image'])
                        except:
                            pass
            elif 'url' in result:
                # Direct URL in result
                import requests
                img_response = requests.get(result['url'])
                if img_response.status_code == 200:
                    if progress_callback:
                        progress_callback(1.0, "Complete!")
                    return img_response.content
        
        # If we get here, the result format is unexpected
        raise gr.Error(f"Unexpected result format from FAL API: {result}")
        
    except Exception as e:
        raise gr.Error(f"FAL API error: {str(e)}")
    finally:
        # Clean up temporary file
        try:
            os.unlink(temp_file_path)
        except:
            pass

# --- Core Inference Function for ChatInterface ---
def chat_fn(message, chat_history, seed, randomize_seed, guidance_scale, steps, progress=gr.Progress()):
    """
    Performs image generation or editing based on user input from the chat interface.
    """
    prompt = message["text"]
    files = message["files"]

    if not prompt and not files:
        raise gr.Error("Please provide a prompt and/or upload an image.")

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    if files:
        print(f"Received image: {files[0]}")
        try:
            # Try to open and convert the image
            input_image = Image.open(files[0])
            # Convert to RGB if needed (handles RGBA, P, etc.)
            if input_image.mode != "RGB":
                input_image = input_image.convert("RGB")
            # Auto-orient the image based on EXIF data
            input_image = ImageOps.exif_transpose(input_image)
            
            # Convert PIL image to bytes
            img_byte_arr = io.BytesIO()
            input_image.save(img_byte_arr, format='PNG')
            img_byte_arr.seek(0)
            image_bytes = img_byte_arr.getvalue()
            
        except Exception as e:
            raise gr.Error(f"Could not process the uploaded image: {str(e)}. Please try uploading a different image format (JPEG, PNG, WebP).")
            
        progress(0.1, desc="Processing image...")
    else:
        # For text-to-image, we need a placeholder image or handle differently
        # FLUX.1 Kontext is primarily an image-to-image model
        raise gr.Error("This model (FLUX.1 Kontext) requires an input image. Please upload an image to edit.")

    try:
        # Make API request
        result_bytes = query_api(image_bytes, prompt, seed, guidance_scale, steps, progress_callback=progress)
        
        # Try to convert response bytes to PIL Image
        try:
            image = Image.open(io.BytesIO(result_bytes))
        except Exception as img_error:
            print(f"Failed to open image: {img_error}")
            print(f"Image bytes type: {type(result_bytes)}, length: {len(result_bytes) if hasattr(result_bytes, '__len__') else 'unknown'}")
            
            # Try to decode as base64 if direct opening failed
            try:
                import base64
                decoded_bytes = base64.b64decode(result_bytes)
                image = Image.open(io.BytesIO(decoded_bytes))
            except:
                raise gr.Error(f"Could not process API response as image. Response length: {len(result_bytes) if hasattr(result_bytes, '__len__') else 'unknown'}")
        
        progress(1.0, desc="Complete!")
        return gr.Image(value=image)
        
    except gr.Error:
        # Re-raise gradio errors as-is
        raise
    except Exception as e:
        raise gr.Error(f"Failed to generate image: {str(e)}")

# --- UI Definition using gr.ChatInterface ---

seed_slider = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
randomize_checkbox = gr.Checkbox(label="Randomize seed", value=False)
guidance_slider = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=2.5)
steps_slider = gr.Slider(label="Steps", minimum=1, maximum=30, value=28, step=1)

demo = gr.ChatInterface(
    fn=chat_fn,
    title="FLUX.1 Kontext [dev] - FAL Client",
    description="""<p style='text-align: center;'>
    A simple chat UI for the <b>FLUX.1 Kontext [dev]</b> model using FAL AI client through Hugging Face.
    <br>
    <b>Upload an image</b> and type your editing instructions (e.g., "Turn the cat into a tiger", "Add a hat").
    <br>
    This model specializes in understanding context and making precise edits to your images.
    <br>
    Find the model on <a href='https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev' target='_blank'>Hugging Face</a>.
    <br>
    <b>Note:</b> Uses HF_TOKEN environment variable through HF inference providers.
    </p>""",
    multimodal=True,
    textbox=gr.MultimodalTextbox(
        file_types=["image"],
        placeholder="Upload an image and type your editing instructions...",
        render=False
    ),
    additional_inputs=[
        seed_slider,
        randomize_checkbox,
        guidance_slider,
        steps_slider
    ],
    theme="soft"
)

if __name__ == "__main__":
    demo.launch()