app.py

# import part
!pip install streamlit pyngrok
import streamlit as st
from transformers import pipeline
from PIL import Image
import io

  
# function part
def generate_image_caption(image):
    """Generates a caption for the given image using a pre-trained model."""
    img2caption = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
    result = img2caption(image)
    return result[0]['generated_text']

def text2story(text):
    """Generates a children's story from text input with genre adaptation"""
    story_prompt = f"Create a funny 100-word story for 8-year-olds about: {text}. Include: "
    story_prompt += "1) A silly character 2) Magical object 3) Sound effects 4) Happy ending"
    
    pipe = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
    story_text = pipe(
        story_prompt,
        max_new_tokens=200,
        temperature=0.9,
        top_k=50
    )[0]['generated_text']
    return story_text.split("Happy ending")[-1].strip()  # Clean output

def main():
    st.title("📖 Image Story Generator")
    st.write("Upload an image and get a magical children's story!")

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

    if uploaded_image:
        image = Image.open(uploaded_image).convert("RGB")
        st.image(image, use_column_width=True)
        
        with st.spinner("✨ Analyzing image..."):
            caption = generate_image_caption(image)
        
        st.subheader("Image Understanding")
        st.write(caption)
        
        with st.spinner("📖 Writing story..."):
            story = text2story(caption)
        
        st.subheader("Magical Story")
        st.write(story)

if __name__ == "__main__":
    main()