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from transformers import BlipProcessor, BlipForConditionalGeneration
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from PIL import Image
import gradio as gr
import torch
# 1. 이미지 캡셔닝 모델 로드 (BLIP)
caption_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
caption_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
# 2. 동화 생성 모델 (Flan-T5)
story_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
story_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
story_generator = pipeline("text2text-generation", model=story_model, tokenizer=story_tokenizer)
# 3. 동화 생성 함수
def generate_fairytale(image):
# (1) 이미지 → 캡션
inputs = caption_processor(images=image, return_tensors="pt").to(caption_model.device)
outputs = caption_model.generate(**inputs)
caption = caption_processor.decode(outputs[0], skip_special_tokens=True).strip()
# (2) 캡션이 짧으면 보완
if len(caption.split()) < 5:
caption += ". They look like magical characters from a fantasy story."
# (3) 프롬프트 설정
prompt = f"""
Write a magical and imaginative fairytale for children based on the following image description.
Description: "{caption}"
Your story should:
- Be at least 3 paragraphs long
- Start with "Once upon a time"
- Include fantasy, adventure, or mystery elements
- Be creative and heartwarming
Story:
"""
# (4) 동화 생성 (길이 충분히 늘리기)
story = story_generator(prompt, max_length=500, do_sample=True)[0]['generated_text']
return caption, story
# 4. Gradio 인터페이스
interface = gr.Interface(
fn=generate_fairytale,
inputs=gr.Image(type="pil", label="🖼️ Upload an Image"),
outputs=[
gr.Textbox(label="📌 Image Description"),
gr.Textbox(label="📖 Generated Fairytale")
],
title="🌟 AI Fairytale Generator from Image",
description="Upload an image and get a rich fairytale story created from it!",
theme="soft"
)
# 5. 실행
interface.launch(share=True, debug=True, inbrowser=True)
# from transformers import BlipProcessor, BlipForConditionalGeneration
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
# from PIL import Image
# import gradio as gr
# import torch
# # 1. 이미지 설명 생성 모델 (BLIP)
# caption_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
# caption_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
# # 2. 영어 동화 생성 모델 (FLAN-T5)
# story_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
# story_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
# # 3. 동화 생성 함수
# def generate_fairytale(image):
# # 1. 이미지 → 텍스트 설명 생성
# inputs = caption_processor(images=image, return_tensors="pt").to(caption_model.device)
# outputs = caption_model.generate(**inputs)
# caption = caption_processor.decode(outputs[0], skip_special_tokens=True).strip()
# # 2. 프롬프트 구성 (중복 방지 조건 추가)
# prompt = f"""Write a magical and imaginative children's story based on the following image description.
# Description: "{caption}"
# The story should be at least 3 paragraphs and must not repeat any sentences.
# Story:"""
# # 3. 텍스트 생성
# input_ids = story_tokenizer(prompt, return_tensors="pt").input_ids.to(story_model.device)
# output_ids = story_model.generate(
# input_ids,
# max_length=600, # 더 길고 풍부한 텍스트
# num_beams=4, # 빔 탐색
# no_repeat_ngram_size=3, # 반복 방지
# repetition_penalty=1.3, # 반복 패널티
# early_stopping=True,
# do_sample=False # 확정적 결과
# )
# story = story_tokenizer.decode(output_ids[0], skip_special_tokens=True)
# return caption, story
# # 4. Gradio UI 인터페이스
# interface = gr.Interface(
# fn=generate_fairytale,
# inputs=gr.Image(type="pil", label="📷 Upload an Image"),
# outputs=[
# gr.Textbox(label="📌 Image Description"),
# gr.Textbox(label="📖 Generated Fairytale")
# ],
# title="🌟 AI Fairytale Generator from Image",
# description="Upload an image and get a rich fairytale story created from it!",
# theme="default"
# )
# # 5. 실행
# interface.launch(share=True)
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