import streamlit as st
from transformers import pipeline
from PIL import Image
import io
from gtts import gTTS

# Page config
st.title("🖼️ → 📖 Image-to-Story Demo")
st.write("Upload an image and watch as it’s captioned, turned into a short story, and even read aloud!")

# Load and cache pipelines
@st.cache_resource
def load_captioner():
    return pipeline("image-to-text", model="unography/blip-large-long-cap")

@st.cache_resource
def load_story_gen():
    return pipeline(
        "text-generation",
        model="gpt2",
        tokenizer="gpt2"
    )

captioner = load_captioner()
story_gen = load_story_gen()

# 1) Image upload
uploaded = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
if uploaded:
    img = Image.open(uploaded)
    st.image(img, use_column_width=True)

    # 2) Generate caption
    with st.spinner("Generating caption…"):
        caps = captioner(img)
        # `caps` is a list of dicts like [{"generated_text": "..."}]
        caption = caps[0]["generated_text"]
    st.write("**Caption:**", caption)

    # 3) Generate story from caption
    with st.spinner("Spinning up a story…"):
        story_out = story_gen(
            caption,
            max_length=200,
            num_return_sequences=1,
            do_sample=True,
            top_p=0.9
        )
        story = story_out[0]["generated_text"]
    st.write("**Story:**", story)

    # 4) Play story as audio
    if st.button("🔊 Play Story Audio"):
        with st.spinner("Generating audio…"):
            tts = gTTS(text=story, lang="en")
            buf = io.BytesIO()
            tts.write_to_fp(buf)
            buf.seek(0)
        st.audio(buf.read(), format="audio/mp3")


"""
import streamlit as st
from transformers import pipeline

def main():
    sentiment_pipeline = pipeline(model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")

    st.title("Sentiment Analysis with HuggingFace Spaces")
    st.write("Enter a sentence to analyze its sentiment:")

    user_input = st.text_input("")
    if user_input:
        result = sentiment_pipeline(user_input)
        sentiment = result[0]["label"]
        confidence = result[0]["score"]

        st.write(f"Sentiment: {sentiment}")
        st.write(f"Confidence: {confidence:.2f}")

if __name__ == "__main__":
    main()
"""