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import os
import gradio as gr
import google.generativeai as genai
from PIL import Image
import io
import base64
import time
# Configuration optimized for Hugging Face
MAX_RETRIES = 3
TIMEOUT = 10 # Keep under 20s Hugging Face limit
# Simplified style instructions for faster processing
STYLE_INSTRUCTIONS = {
"General": "Describe as Flux prompt focusing on key elements",
"Vector": "Clean vector style with sharp lines and B&W",
"Realistic": "Photorealistic details with accurate textures",
}
def optimize_image(img):
"""Fast image optimization for web"""
img = img.convert("RGB")
img.thumbnail((512, 512)) # Reduce image size
return img
def generate_prompt(image, api_key, style):
for attempt in range(MAX_RETRIES):
try:
start_time = time.time()
# Configure API
genai.configure(api_key=api_key)
model = genai.GenerativeModel('gemini-pro-vision')
# Optimize image
img = optimize_image(image)
img_byte_arr = io.BytesIO()
img.save(img_byte_arr, format='JPEG', quality=85)
# Timeout control
if time.time() - start_time > TIMEOUT - 2:
raise TimeoutError("Image processing too slow")
response = model.generate_content([
STYLE_INSTRUCTIONS[style],
{"mime_type": "image/jpeg", "data": img_byte_arr.getvalue()}
], request_options={"timeout": TIMEOUT})
return response.text
except Exception as e:
if attempt == MAX_RETRIES - 1:
return f"Error: {str(e)}"
time.sleep(1)
# Hugging Face optimized interface
with gr.Blocks() as app:
gr.Markdown("## 🚀 HF-Friendly Flux Generator")
with gr.Row():
api_key = gr.Textbox(label="Gemini Key", type="password")
style = gr.Dropdown(list(STYLE_INSTRUCTIONS), label="Style")
image_input = gr.Image(type="pil", label="Upload Design")
generate_btn = gr.Button("Generate", variant="primary")
output = gr.Textbox(label="Prompt", interactive=False)
generate_btn.click(
generate_prompt,
inputs=[image_input, api_key, style],
outputs=output,
concurrency_limit=3 # Better HF resource management
)
# HF-specific launch settings
app.queue(concurrency_count=3, max_size=20).launch(debug=True) |