app.py

import streamlit as st
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration, pipeline
from gtts import gTTS
import os
import tempfile

@st.cache_resource
def load_models():
    processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
    blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
    gpt2_pipeline = pipeline("text-generation", model="gpt2")
    return processor, blip_model, gpt2_pipeline

processor, blip_model, gpt2 = load_models()

st.title("🖼️📖 Storyteller for Kids")
st.write("Upload an image and let the app create and read a magical story just for kids!")

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

if uploaded_file:
    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, caption="Uploaded Image", use_column_width=True)

    with st.spinner("Generating image caption..."):
        inputs = processor(images=image, return_tensors="pt")
        out = blip_model.generate(**inputs)
        caption = processor.decode(out[0], skip_special_tokens=True)
        st.success("Caption generated!")
        st.write(f"**Caption:** {caption}")

    with st.spinner("Writing a children's story..."):
        prompt = f"Write a story for children aged 3-10 about this: {caption}"
        story_output = gpt2(prompt, max_length=120, do_sample=True)[0]["generated_text"]
        story = story_output.strip().replace('\n', ' ')
        st.success("Story created!")
        st.write(f"**Story:**\n\n{story}")

    with st.spinner("Converting story to audio..."):
        tts = gTTS(text=story, lang='en')
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
            tts.save(fp.name)
            st.audio(fp.name, format="audio/mp3")