app.py

import streamlit as st
from PIL import Image
from transformers import (
    BlipProcessor, 
    BlipForConditionalGeneration,
    AutoTokenizer,
    AutoModelForCausalLM
)
from gtts import gTTS
import io
import torch

# ======================
# Stage 1: Image Captioning
# ======================
@st.cache_resource
def load_image_model():
    """Load image captioning model"""
    return (
        BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base"),
        BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
    )

def stage1_process(uploaded_file):
    """Generate image caption"""
    processor, model = load_image_model()
    img = Image.open(uploaded_file).convert("RGB")
    inputs = processor(images=img, return_tensors="pt")
    outputs = model.generate(**inputs)
    return processor.decode(outputs[0], skip_special_tokens=True)

# ======================
# Stage 2: Story Generation (Optimized)
# ======================
@st.cache_resource
def load_story_model():
    """Load optimized story model"""
    return (
        AutoTokenizer.from_pretrained("gpt2-medium"),
        AutoModelForCausalLM.from_pretrained("gpt2-medium")
    )

def stage2_process(keyword):
    """Generate structured story"""
    tokenizer, model = load_story_model()
    
    # Enhanced prompt template
    prompt = f"""Write a children's story in 100-150 words with these elements:
    - Theme: {keyword}
    - Characters: Friendly animals
    - Moral: Sharing is caring

    Story begins: One sunny morning, a little rabbit named Cotton discovered"""
    
    inputs = tokenizer(prompt, return_tensors="pt", max_length=150, truncation=True)
    outputs = model.generate(
        inputs.input_ids,
        max_new_tokens=300,
        temperature=0.9,
        top_k=50,
        no_repeat_ngram_size=3,
        repetition_penalty=1.2,
        do_sample=True
    )
    full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return full_text.split("Story begins:")[-1].strip()

# ======================
# Stage 3: Text-to-Speech
# ======================
def stage3_process(text):
    """Convert text to audio"""
    try:
        clean_text = text.strip().replace('\n', ' ')[:300]
        if len(clean_text) < 20:
            return None
        tts = gTTS(text=clean_text, lang='en')
        audio = io.BytesIO()
        tts.write_to_fp(audio)
        audio.seek(0)
        return audio
    except:
        return None

# ======================
# Main Application
# ======================
def main():
    st.title("📖 Children's Story Generator")
    
    # Initialize session state
    if 'processing' not in st.session_state:
        st.session_state.update({
            'caption': None,
            'story': None,
            'audio': None
        })
    
    # File upload
    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "png"])
    
    if uploaded_file:
        # Permanent display
        st.image(uploaded_file, width=300)
        
        # Stage 1
        if not st.session_state.caption:
            with st.spinner("Analyzing image..."):
                st.session_state.caption = stage1_process(uploaded_file)
        st.success(f"Detected Theme: {st.session_state.caption}")
        
        # Stage 2
        if not st.session_state.story:
            with st.spinner("Writing magical story..."):
                st.session_state.story = stage2_process(st.session_state.caption)
        
        # Display story
        if st.session_state.story:
            st.subheader("Generated Story")
            st.write(st.session_state.story)
            
            # Stage 3
            if not st.session_state.audio:
                with st.spinner("Generating audio..."):
                    st.session_state.audio = stage3_process(st.session_state.story)
            if st.session_state.audio:
                st.audio(st.session_state.audio, format="audio/mp3")
                st.download_button("Download Audio", 
                                 st.session_state.audio.getvalue(),
                                 "story.mp3",
                                 mime="audio/mp3")

if __name__ == "__main__":
    main()