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| import streamlit as st | |
| from transformers import pipeline | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| def main(): | |
| # Prepare pipeline | |
| classifier = pipeline("text-classification", model="lori0330/BART_FineTuned_ZeroShotClassification") | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| painter = DiffusionPipeline.from_pretrained( | |
| "cagliostrolab/animagine-xl-3.1", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| ) | |
| painter.to('cuda') | |
| # Edit the space | |
| st.title("Brief Report Generator") | |
| st.write("Copy the text here:") | |
| user_input = st.text_input("") | |
| # Check input | |
| if user_input: | |
| result_1 = classifier(user_input) | |
| label = result_1[0]['label'] | |
| st.write(f"Label of this text: {label}") | |
| result_2 = summarizer(user_input, max_length=100, min_length=30, do_sample=False) | |
| summary = result_2[0]['summary_text'] | |
| st.write(f"The summary of this text:\n{summary}") | |
| description = f"This is mainly about {label}: {summary}" | |
| negative_prompt = "nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]" | |
| image = painter( | |
| description, | |
| negative_prompt=negative_prompt, | |
| width=832, | |
| height=1216, | |
| guidance_scale=7, | |
| num_inference_steps=28 | |
| ).images[0] | |
| st.write(f"The attached image:\n") | |
| st.image(image) | |
| if __name__ == "__main__": | |
| main() |