app.py

from transformers import pipeline
import streamlit as st
from PIL import Image


# 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):
    text_to_story_model = pipeline("text-generation", model="distilbert/distilgpt2") 
    if isinstance(text, list):  
        text = " ".join(text)  # Ensure input is a single string
    story_text = text_to_story_model(text, max_length=100, num_return_sequences=1)
    return story_text[0]['generated_text'] 
    
    #text_to_story_model = pipeline("text-generation",model="distilbert/distilgpt2")
    #story_text = text_to_story_model(text,max_length=100,num_return_sequences=1)
    #return story_text

# text2audio
def text2audio(story_text):
    text_to_audio_model = pipeline("text-to-speech", model="facebook/mms-tts-eng")
    audio_data = text_to_audio_model(story_text)
    #audio_data = Audio(speech_output['audio'], rate=speech_output['sampling_rate'])
    return audio_data


#main part
st.set_page_config(page_title="Your Image to Audio Story",
                   page_icon="🦜")
st.header("Turn Your Image to 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'])
        #st.audio("kids_playing_audio.wav")

        

#pipe_caption = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")

#st.title("AI story telling for kids")  # Establish title

#uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])

#if uploaded_file is not None:  # Check if a file is uploaded
   # image = Image.open(uploaded_file)
   # st.image(image, caption="Uploaded Image", use_column_width=True)
#else:
   # st.warning("Please upload an image.")