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from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText
def generate_caption(image_path):
image = Image.open(image_path)
processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = AutoModelForImageTextToText.from_pretrained("Salesforce/blip-image-captioning-base")
inputs = processor(image, return_tensors="pt")
output = model.generate(**inputs)
caption = processor.decode(output[0], skip_special_tokens=True)
return caption
from transformers import pipeline
def generate_story(caption):
# 使用文本生成 pipeline
generator = pipeline("text-generation", model="openai-community/gpt2")
# Use a pipeline as a high-level helper
prompt = f"由以下图片得到的描述: '{caption}'，请根据这个描述生成一个完整的童话故事，故事至少100个单词。"
result = generator(prompt, max_length=300, num_return_sequences=1)
story = result[0]['generated_text']
# 添加字数判断，必要时进行调整或循环生成直到满足条件
if len(story.split()) < 100:
# 可以进行递归调用或其他逻辑扩充文本
story += " " + generate_story(caption)
return story
from gtts import gTTS
def text_to_speech(text, output_file="output.mp3"):
tts = gTTS(text=text, lang='en') # 注意可根据需要选择语言或使用中文
tts.save(output_file)
return output_file
import streamlit as st
from PIL import Image
def main():
st.title("儿童故事生成应用")
st.write("上传一张图片，我们将根据图片生成有趣的故事，并转换成语音播放给你听！!!")
uploaded_file = st.file_uploader("选择一张图片", type=["png", "jpg", "jpeg"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption="上传的图片", use_container_width=True)
# 调用图像描述函数
caption = generate_caption(uploaded_file)
st.write("图片描述:", caption)
# 生成故事
story = generate_story(caption)
st.write("生成的故事:", story)
# 文字转语音
audio_file = text_to_speech(story)
st.audio(audio_file, format="audio/mp3")
if __name__ == "__main__":
main() |