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()