import re
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
import string

# Initialize pipelines with caching
@st.cache_resource
def load_pipelines():
    captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
    storyer = pipeline("text-generation", model="aspis/gpt2-genre-story-generation")
    tts = pipeline("text-to-speech", model="facebook/mms-tts-eng")
    return captioner, storyer, tts

captioner, storyer, tts = load_pipelines()

def clean_generated_story(raw_story: str) -> str:
    """
    Cleans the generated story by:
    1. Removing digits.
    2. Removing words that are likely random letter combinations based on having no vowels.
    3. Removing single-letter words unless they are allowed (such as 'a' or 'I').
    """
    # Remove all digits using regex
    story_without_numbers = re.sub(r'\d+', '', raw_story)
    
    vowels = set('aeiouAEIOU')
    
    def is_valid_word(word: str) -> bool:
        # Allow "a" and "I" for single-letter words
        if len(word) == 1 and word.lower() not in ['a', 'i']:
            return False
        # For words longer than one letter, filter out those that do not contain any vowels
        if len(word) > 1 and not any(char in vowels for char in word):
            return False
        return True

    # Split the story into words, apply filtering, and recombine into a clean story
    words = story_without_numbers.split()
    filtered_words = [word for word in words if is_valid_word(word)]
    
    # Optionally, you can trim the clean story to a certain word count
    clean_story = " ".join(filtered_words[:100])
    return clean_story

def generate_content(image):
    pil_image = Image.open(image)
    
    # Generate caption from the image
    caption = captioner(pil_image)[0]["generated_text"]
    st.write("**🌟 What's in the picture: 🌟**")
    st.write(caption)

    # Create prompt for the story
    # Notice there’s no need to include the extra cleaning instructions in this prompt,
    # because our code handles them later.
    prompt = (
        f"Write a funny, interesting story for young children precisely centered on this scene {caption}\nStory:"
        f" mention the exact place, location or venue within {caption}"
    )
    
    # Generate raw story from the model
    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()
    
    # Clean the raw story using our custom function
    story = clean_generated_story(raw)
    
    st.write("**📖 Your funny story: 📖**")
    st.write(story)

    # Generate audio for the story
    chunks = textwrap.wrap(story, width=200)
    audio = np.concatenate([tts(chunk)["audio"].squeeze() for chunk in chunks])

    # Save audio to a temporary 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 section
st.title("✨ Magic Story Maker ✨")
st.markdown("Upload a picture to make a funny story and hear it too! 📸")

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

if uploaded_image is None:
    st.image("https://example.com/placeholder_image.jpg", caption="Upload your picture here! 📷", use_container_width=True)
else:
    st.image(uploaded_image, caption="Your Picture 🌟", use_container_width=True)

if st.button("✨ Make My Story! ✨"):
    if uploaded_image is not None:
        with st.spinner("🔮 Creating your magical story..."):
            caption, story, audio_path = generate_content(uploaded_image)
            st.success("🎉 Your story is ready! 🎉")
            st.audio(audio_path, format="audio/wav")
            os.remove(audio_path)
    else:
        st.warning("Please upload a picture first! 📸")