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| import time | |
| import utilities as u | |
| import card_generator as card | |
| from PIL import Image | |
| import fal_client | |
| from pathlib import Path | |
| import tempfile | |
| import os | |
| import base64 | |
| import io | |
| import logging | |
| import requests | |
| import json | |
| # Set up logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| start_time = time.time() | |
| temp_image_path = "./image_temp/" | |
| def preview_and_generate_image(num_images, sd_prompt, user_input_template): | |
| print(f"num_images: {num_images}") | |
| print(f"sd_prompt: {sd_prompt}") | |
| print(f"user_input_template: {user_input_template}") | |
| num_images = int(4) | |
| sd_prompt = f"magnum opus, blank card, no text, blank textbox at top for title, mid for details and bottom for description, detailed high quality animal properties borders, {sd_prompt}" | |
| try: | |
| # Save the image to a temporary file | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file: | |
| user_input_template[0].save(temp_file.name, format="PNG") | |
| temp_path = temp_file.name | |
| logger.info(f"Image saved to temporary file: {temp_path}") | |
| # Upload the file using fal_client | |
| url = fal_client.upload_file(temp_path) | |
| logger.info(f"Image uploaded. URL: {url}") | |
| # Remove the temporary file | |
| os.unlink(temp_path) | |
| request_handle = fal_client.submit( | |
| "fal-ai/flux-lora/image-to-image", | |
| arguments={ | |
| "num_inference_steps": 35, | |
| "prompt": sd_prompt, | |
| "num_images": num_images, | |
| "image_url": url, | |
| "strength": 0.85 | |
| } | |
| ) | |
| logger.info(f"Type of request_handle: {type(request_handle)}") | |
| logger.info(f"Content of request_handle: {request_handle}") | |
| # Get the result from the SyncRequestHandle | |
| result = request_handle.get() | |
| logger.info(f"Type of result: {type(result)}") | |
| logger.info(f"Content of result: {json.dumps(result, indent=2)}") | |
| # Extract the image URLs from the result | |
| image_urls = [img['url'] for img in result.get('images', [])] | |
| logger.info(f"Extracted image URLs: {image_urls}") | |
| if not image_urls: | |
| logger.warning("No images were generated.") | |
| return [] | |
| # Download and process the images | |
| images = [] | |
| for i, url in enumerate(image_urls): | |
| try: | |
| response = requests.get(url) | |
| response.raise_for_status() # Raises an HTTPError for bad responses | |
| img = Image.open(io.BytesIO(response.content)) | |
| images.append((img, f"Generated Image {i+1}")) # Add a tuple with image and caption | |
| logger.info(f"Successfully downloaded and processed image {i+1}") | |
| except Exception as e: | |
| logger.error(f"Error processing image {i+1} from URL {url}: {str(e)}") | |
| if not images: | |
| logger.warning("No images could be downloaded and processed.") | |
| return [] | |
| logger.info(f"Returning {len(images)} processed images") | |
| return images | |
| except Exception as e: | |
| logger.error(f"Error during API call or processing: {str(e)}") | |
| logger.exception("Full traceback:") | |
| return [] | |