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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()
""" |