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import streamlit as st
from transformers import pipeline
from PIL import Image
import io
from gtts import gTTS
import tempfile
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!")
@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()
uploaded = st.file_uploader("Upload an image", type=["png","jpg","jpeg"], key="image")
if uploaded:
img = Image.open(uploaded)
st.image(img, use_column_width=True)
# Caption
if "caption" not in st.session_state:
with st.spinner("Generating caption…"):
caps = captioner(img)
st.session_state.caption = caps[0] if isinstance(caps, list) else caps
st.write("**Caption:**", st.session_state.caption)
# Story
if "story" not in st.session_state:
with st.spinner("Spinning up a story…"):
out = story_gen(
st.session_state.caption,
max_length=200,
num_return_sequences=1,
do_sample=True,
top_p=0.9
)
st.session_state.story = out[0]["generated_text"]
st.write("**Story:**", st.session_state.story)
# Prepare audio bytes once
if "audio_bytes" not in st.session_state:
with st.spinner("Generating audio…"):
tts = gTTS(text=st.session_state.story, lang="en")
buf = io.BytesIO()
tts.write_to_fp(buf)
st.session_state.audio_bytes = buf.getvalue()
# Play button
if st.button("🔊 Play Story Audio"):
# Write to a temp file
tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
tmp.write(st.session_state.audio_bytes)
tmp.flush()
tmp_path = tmp.name
tmp.close()
# Stream it
st.audio(tmp_path, format="audio/mp3")
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