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"""
Trouter-Imagine-1 Image Generator
Apache 2.0 License
A Gradio-based image generation interface using OpenTrouter/Trouter-Imagine-1
"""
import gradio as gr
import requests
import io
from PIL import Image
import base64
import json
import os
from datetime import datetime
# Configuration
API_URL = "https://api-inference.huggingface.co/models/OpenTrouter/Trouter-Imagine-1"
HF_TOKEN = os.getenv("HF_TOKEN") # Set this in your Hugging Face Space secrets
class ImageGenerator:
"""Main class for handling image generation requests"""
def __init__(self, api_url, token):
self.api_url = api_url
self.headers = {"Authorization": f"Bearer {token}"} if token else {}
self.generation_history = []
def generate_image(self, prompt, negative_prompt="", width=1024, height=1024,
num_inference_steps=30, guidance_scale=7.5, seed=-1):
"""
Generate an image from a text prompt
Args:
prompt: Text description of the desired image
negative_prompt: Things to avoid in the generation
width: Image width in pixels
height: Image height in pixels
num_inference_steps: Number of denoising steps (more = better quality but slower)
guidance_scale: How closely to follow the prompt (higher = more strict)
seed: Random seed for reproducibility (-1 for random)
Returns:
PIL Image object or error message
"""
try:
# Prepare the payload
payload = {
"inputs": prompt,
"parameters": {
"negative_prompt": negative_prompt,
"width": width,
"height": height,
"num_inference_steps": num_inference_steps,
"guidance_scale": guidance_scale,
}
}
# Add seed if specified
if seed != -1:
payload["parameters"]["seed"] = seed
# Make the API request
response = requests.post(
self.api_url,
headers=self.headers,
json=payload,
timeout=120
)
# Check for errors
if response.status_code != 200:
error_msg = f"Error {response.status_code}: {response.text}"
return None, error_msg
# Convert response to image
image = Image.open(io.BytesIO(response.content))
# Save to history
self.generation_history.append({
"timestamp": datetime.now().isoformat(),
"prompt": prompt,
"negative_prompt": negative_prompt,
"parameters": {
"width": width,
"height": height,
"steps": num_inference_steps,
"guidance": guidance_scale,
"seed": seed
}
})
success_msg = f"✅ Image generated successfully! ({width}x{height}px)"
return image, success_msg
except requests.exceptions.Timeout:
return None, "⏱️ Request timed out. Try reducing image size or steps."
except Exception as e:
return None, f"❌ Error: {str(e)}"
def get_history_summary(self):
"""Get a summary of generation history"""
if not self.generation_history:
return "No generations yet."
summary = f"Total generations: {len(self.generation_history)}\n\n"
summary += "Recent generations:\n"
for i, entry in enumerate(self.generation_history[-5:], 1):
summary += f"\n{i}. {entry['timestamp']}\n"
summary += f" Prompt: {entry['prompt'][:50]}...\n"
return summary
# Initialize the generator
generator = ImageGenerator(API_URL, HF_TOKEN)
def generate_wrapper(prompt, negative_prompt, width, height, steps, guidance, seed, progress=gr.Progress()):
"""Wrapper function for Gradio interface with progress tracking"""
if not prompt.strip():
return None, "⚠️ Please enter a prompt!"
progress(0, desc="Initializing...")
progress(0.3, desc="Sending request to model...")
image, message = generator.generate_image(
prompt=prompt,
negative_prompt=negative_prompt,
width=int |