app.py

import streamlit as st
from transformers import pipeline
import textwrap
import numpy as np
import soundfile as sf
import tempfile
import os
from PIL import Image

# Initialize pipelines with caching to avoid reloading
@st.cache_resource
def load_pipelines():
    # Load pipeline for generating captions from images
    captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
    # Load pipeline for generating stories from text prompts
    storyer = pipeline("text-generation", model="aspis/gpt2-genre-story-generation")
    # Load pipeline for converting text to speech
    tts = pipeline("text-to-speech", model="facebook/mms-tts-eng")
    return captioner, storyer, tts

# Load the pipelines once and reuse them
captioner, storyer, tts = load_pipelines()

# Function to generate caption, story, and audio from an uploaded image
def generate_content(image):
    # Convert the uploaded image to a PIL image format
    pil_image = Image.open(image)
    
    # Generate a caption based on the image content
    caption = captioner(pil_image)[0]["generated_text"]
    st.write("**Caption:**", caption)

    # Create a prompt for generating a children's story
    prompt = (
        f"Write a funny, warm children's story for ages 3-10, 50–100 words, "
        f"in third-person narrative, that describes this scene exactly: {caption} "
        f"mention the exact place or venue within {caption}"
    )
    
    # Generate the story based on the prompt
    raw = storyer(
        prompt,
        max_new_tokens=150,
        temperature=0.7,
        top_p=0.9,
        no_repeat_ngram_size=2,
        return_full_text=False
    )[0]["generated_text"].strip()

    # Trim the generated story to a maximum of 100 words
    words = raw.split()
    story = " ".join(words[:100])
    st.write("**Story:**", story)

    # Split the story into chunks of 200 characters for text-to-speech processing
    chunks = textwrap.wrap(story, width=200)
    
    # Generate and concatenate audio for each text chunk
    audio = np.concatenate([tts(chunk)["audio"].squeeze() for chunk in chunks])

    # Save the concatenated audio to a temporary WAV file
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
        sf.write(temp_file.name, audio, tts.model.config.sampling_rate)
        temp_file_path = temp_file.name

    return caption, story, temp_file_path

# Streamlit UI for the application
st.title("Image to Children's Story and Audio")
st.markdown("""
    Upload an image below to generate a caption, a funny children's story, 
    and an audio narration based on the image. The story will be tailored 
    for children aged 3-10.
""")

# File uploader for image input
uploaded_image = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"], help="Supported formats: JPG, JPEG, PNG")

if uploaded_image is not None:
    # Display the uploaded image with a caption
    st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
    
    # Button to trigger content generation
    if st.button("Generate Story and Audio", help="Click to create the story and audio"):
        # Show a spinner while content is being generated
        with st.spinner("Generating your story and audio narration..."):
            caption, story, audio_path = generate_content(uploaded_image)
            # Display the audio player with the generated narration
            st.audio(audio_path, format="audio/wav")
            # Remove the temporary audio file after use
            os.remove(audio_path)