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import re
from PIL import Image
import io
from dotenv import load_dotenv
import os
import requests
load_dotenv()
def generate_image_prompts(script):
# Split the script into sentences
sentences = re.split(r'(?<=[.!?]) +', script)
# Generate prompts for each sentence
prompts = []
for sentence in sentences:
if sentence.strip(): # Ensure the sentence is not empty
prompts.append(sentence.strip())
return prompts
def hf_pipeline(prompt, max_retries=5, delay=30):
retries = 0
while retries < max_retries:
response = requests.post(f"https://api-inference.huggingface.co/models/Shakker-Labs/AWPortrait-FL",
json={"inputs": prompt})
if response.status_code == 503:
print(f"Model is loading, retrying in {delay} seconds...")
retries += 1
time.sleep(delay)
elif response.status_code == 200:
return response.json()
else:
raise Exception(f"Failed to generate image. Status code: {response.status_code}, {response.text}")
raise Exception(f"Failed to generate image after {max_retries} retries.")
def generate_images(prompts):
try:
image_files = []
for idx, prompt in enumerate(prompts):
print(f"Generating image for prompt: {prompt}")
# Ensure the prompt is processed on the correct device
image = hf_pipeline(prompt).images[0]
filename = f"generated_image_{idx}.png"
image.save(filename)
image_files.append(filename)
return image_files
except Exception as e:
print(e)
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