app.py

# import part
import streamlit as st
from transformers import pipeline
from gtts import gTTS
import io

# function part
# img2text
def img2text(url):
    image_to_text_model = pipeline("image-to-text",
                                   model="Salesforce/blip-image-captioning-base")
    text = image_to_text_model(url)[0]["generated_text"]
    return text

# text2story
def text2story(text):
    # 使用 Hugging Face 的 text-generation 模型生成故事
    story_pipeline = pipeline("text-generation", model="agentica-org/DeepScaleR-1.5B-Preview")
    result = story_pipeline(text, max_length=200, num_return_sequences=1)
    story_text = result[0]['generated_text']
    return story_text

# text2audio
def text2audio(story_text):
    # 使用 gTTS 将文本转换为音频
    tts = gTTS(text=story_text, lang='en')
    # 创建一个内存中的字节流对象，用于存储音频数据
    audio_file = io.BytesIO()
    # 将音频数据写入字节流
    tts.write_to_fp(audio_file)
    # 将文件指针移动到文件开头，以便后续读取
    audio_file.seek(0)
    return {'audio': audio_file, 'sampling_rate': 16000}  # 返回音频数据和采样率

# main part
st.set_page_config(page_title="Your Image to Audio Story",
                   page_icon="🦜")
st.header("Turn Your Image to Audio Story")
uploaded_file = st.file_uploader("Select an Image...")

if uploaded_file is not None:
    print(uploaded_file)
    bytes_data = uploaded_file.getvalue()
    with open(uploaded_file.name, "wb") as file:
        file.write(bytes_data)

    st.image(uploaded_file, caption="Uploaded Image",
             use_column_width=True)

    # Stage 1: Image to Text
    st.text('Processing img2text...')
    scenario = img2text(uploaded_file.name)
    st.write(scenario)

    # Stage 2: Text to Story
    st.text('Generating a story...')
    story = text2story(scenario)
    st.write(story)

    # Stage 3: Story to Audio data
    st.text('Generating audio data...')
    audio_data = text2audio(story)

    # Play button
    if st.button("Play Audio"):
        st.audio(audio_data['audio'],
                 format="audio/wav",
                 start_time=0,
                 sample_rate=audio_data['sampling_rate'])