app.py

import streamlit as st 
from transformers import pipeline, AutoTokenizer
import torch
import re
import numpy as np
import soundfile as sf
from PIL import Image
from datasets import load_dataset
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# ==================== Model loading with caching ====================
@st.cache_resource(show_spinner=False)
def load_models():
    """Pre-load and cache all models"""
    logger.info("Loading image captioning model...")
    caption_model = pipeline(
        task="image-to-text", 
        model="Salesforce/blip-image-captioning-base",
        device=0 if torch.cuda.is_available() else -1
    )

    logger.info("Loading story generation model...")
    story_model = pipeline(
        task="text-generation",
        model="Tincando/fiction_story_generator",
        device=0 if torch.cuda.is_available() else -1,
        torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
    )

    logger.info("Loading text-to-speech model...")
    tts_model = pipeline(
        task="text-to-audio", 
        model="Chan-Y/speecht5_finetuned_tr_commonvoice",
        device=0 if torch.cuda.is_available() else -1
    )
    tts_tokenizer = AutoTokenizer.from_pretrained(
        "Chan-Y/speecht5_finetuned_tr_commonvoice"
    )
    
    return caption_model, story_model, tts_model, tts_tokenizer

# ==================== Streamlit page configuration ====================
st.set_page_config(
    page_title="🧸 AI Story Generator Pro",
    page_icon="📖",
    layout="wide",
    initial_sidebar_state="expanded"
)

# ==================== Sidebar settings ====================
with st.sidebar:
    st.title("⚙️ Generation Settings")
    temperature = st.slider("Creativity", 0.5, 1.5, 0.85, step=0.05)
    max_length = st.slider("Story Length", 100, 500, 200)
    story_style = st.selectbox("Story Style", ["Fairy Tale", "Sci-Fi", "Adventure"])
    voice_speed = st.slider("Voice Speed", 0.5, 2.0, 1.0)

# ==================== Main interface ====================
st.title("🖼️ AI Story Generator")
st.write("Upload an image to get a customized story with audio narration.")

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

if uploaded_file:
    # ==================== Image display ====================
    col1, col2 = st.columns([1, 2])
    with col1:
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded Image", use_column_width=True)

    # ==================== Generation process ====================
    if st.button("Generate Story", type="primary"):
        try:
            progress_bar = st.progress(0)
            status_text = st.empty()

            # Load models
            with st.spinner("🔄 Loading models..."):
                caption_model, story_model, tts_model, tts_tokenizer = load_models()
                speaker_emb = torch.tensor(
                    load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")[7306]["xvector"]
                ).unsqueeze(0)
            progress_bar.progress(20)

            # Generate image caption
            with st.spinner("📷 Analyzing image content..."):
                caption_result = caption_model(image)
                caption = caption_result[0]['generated_text']
            progress_bar.progress(40)

            # Generate story
            with st.spinner("✍️ Writing the story..."):
                prompt = f"Write a children's story in {story_style} style about: {caption}"
                story = story_model(
                    prompt,
                    temperature=temperature,
                    max_length=max_length,
                    do_sample=True
                )[0]['generated_text']
                # Ensure story ends with punctuation
                story = re.sub(r'[^.!?]+$', '', story)
            progress_bar.progress(70)

            # Text-to-speech synthesis
            with st.spinner("🔊 Generating audio..."):
                chunks = re.split(r'(?<=[.!?]) +', story)
                audio_arrays = []
                for chunk in chunks:
                    inputs = tts_tokenizer(chunk, return_tensors="pt")
                    speech = tts_model.generate(
                        inputs["input_ids"],
                        forward_params={
                            "speaker_embeddings": speaker_emb,
                            "speed": voice_speed
                        }
                    )
                    audio_arrays.append(speech.numpy())
                combined = np.concatenate(audio_arrays)
                sf.write("output.wav", combined, samplerate=16000)
            progress_bar.progress(100)

            # ==================== Display results ====================
            with col2:
                st.subheader("📖 Generated Story")
                st.success(story)
                
                st.subheader("🔊 Audio Narration")
                st.audio("output.wav", format="audio/wav")

                # Download buttons
                st.download_button(
                    label="Download Story Text",
                    data=story,
                    file_name="generated_story.txt",
                    mime="text/plain"
                )
                st.download_button(
                    label="Download Audio File",
                    data=open("output.wav", "rb"),
                    file_name="story_audio.wav",
                    mime="audio/wav"
                )

        except Exception as e:
            st.error(f"Generation failed: {str(e)}")
            st.button("Retry", on_click=st.cache_resource.clear)