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import gradio as gr
import numpy as np
import random
import os
import tempfile
import subprocess
import json
from PIL import Image, ImageOps
import pillow_heif # For HEIF/AVIF support
import io
# --- Constants ---
MAX_SEED = np.iinfo(np.int32).max
def setup_node_environment():
"""Setup Node.js environment and install required packages"""
try:
# Check if node is available
result = subprocess.run(['node', '--version'], capture_output=True, text=True)
if result.returncode != 0:
raise gr.Error("Node.js is not installed. Please install Node.js to use this feature.")
# Check if @huggingface/inference is installed, if not install it
package_check = subprocess.run(['npm', 'list', '@huggingface/inference'], capture_output=True, text=True)
if package_check.returncode != 0:
print("Installing @huggingface/inference package...")
install_result = subprocess.run(['npm', 'install', '@huggingface/inference'], capture_output=True, text=True)
if install_result.returncode != 0:
raise gr.Error(f"Failed to install @huggingface/inference: {install_result.stderr}")
return True
except FileNotFoundError:
raise gr.Error("Node.js or npm not found. Please install Node.js and npm.")
def create_js_inference_script(image_path, prompt, hf_token):
"""Create JavaScript inference script"""
js_code = f"""
const {{ InferenceClient }} = require("@huggingface/inference");
const fs = require("fs");
async function runInference() {{
try {{
const client = new InferenceClient("{hf_token}");
const data = fs.readFileSync("{image_path}");
const image = await client.imageToImage({{
provider: "replicate",
model: "black-forest-labs/FLUX.1-Kontext-dev",
inputs: data,
parameters: {{ prompt: "{prompt}" }},
}}, {{
billTo: "huggingface",
}});
// Convert blob to buffer
const arrayBuffer = await image.arrayBuffer();
const buffer = Buffer.from(arrayBuffer);
// Output as base64 for Python to read
const base64 = buffer.toString('base64');
console.log(JSON.stringify({{
success: true,
image_base64: base64,
content_type: image.type || 'image/jpeg'
}}));
}} catch (error) {{
console.log(JSON.stringify({{
success: false,
error: error.message
}}));
process.exit(1);
}}
}}
runInference();
"""
return js_code
def query_api_js(image_bytes, prompt, seed, guidance_scale, steps, progress_callback=None):
"""Send request using JavaScript HF Inference Client"""
# 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 environment.")
if progress_callback:
progress_callback(0.1, "Setting up Node.js environment...")
# Setup Node.js environment
setup_node_environment()
if progress_callback:
progress_callback(0.2, "Preparing image...")
# Create a temporary file for the image
with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file:
temp_file.write(image_bytes)
temp_image_path = temp_file.name
# Create temporary JavaScript file
with tempfile.NamedTemporaryFile(mode='w', suffix='.js', delete=False) as js_file:
js_code = create_js_inference_script(temp_image_path, prompt.replace('"', '\\"'), hf_token)
js_file.write(js_code)
js_file_path = js_file.name
try:
if progress_callback:
progress_callback(0.3, "Running JavaScript inference...")
# Run the JavaScript code
result = subprocess.run(
['node', js_file_path],
capture_output=True,
text=True,
timeout=300 # 5 minute timeout
)
if progress_callback:
progress_callback(0.8, "Processing result...")
if result.returncode != 0:
raise gr.Error(f"JavaScript inference failed: {result.stderr}")
# Parse the JSON output
try:
output = json.loads(result.stdout.strip())
except json.JSONDecodeError:
raise gr.Error(f"Failed to parse JavaScript output: {result.stdout}")
if not output.get('success'):
raise gr.Error(f"Inference error: {output.get('error', 'Unknown error')}")
if progress_callback:
progress_callback(0.9, "Decoding image...")
# Decode base64 image
import base64
image_data = base64.b64decode(output['image_base64'])
if progress_callback:
progress_callback(1.0, "Complete!")
return image_data
except subprocess.TimeoutExpired:
raise gr.Error("Inference timed out. Please try again.")
except Exception as e:
raise gr.Error(f"Error running JavaScript inference: {str(e)}")
finally:
# Clean up temporary files
try:
os.unlink(temp_image_path)
os.unlink(js_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:
# Register HEIF opener with PIL for AVIF/HEIF support
pillow_heif.register_heif_opener()
# 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 using JavaScript
result_bytes = query_api_js(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'}")
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] - HF Inference Client (JS)",
description="""<p style='text-align: center;'>
A simple chat UI for the <b>FLUX.1 Kontext [dev]</b> model using Hugging Face Inference Client via JavaScript.
<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>Requirements:</b> Node.js and npm must be installed. Uses HF_TOKEN environment variable.
</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() |