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Update app.py
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app.py
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import streamlit as st
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from transformers import pipeline
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def main():
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sentiment_pipeline = pipeline(model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
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@@ -17,4 +79,5 @@ def main():
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st.write(f"Confidence: {confidence:.2f}")
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if __name__ == "__main__":
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main()
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import streamlit as st
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from transformers import pipeline
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from PIL import Image
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import io
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from gtts import gTTS
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# Page config
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st.title("🖼️ → 📖 Image-to-Story Demo")
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st.write("Upload an image and watch as it’s captioned, turned into a short story, and even read aloud!")
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# Load and cache pipelines
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@st.cache_resource
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def load_captioner():
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return pipeline("image-to-text", model="unography/blip-large-long-cap")
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@st.cache_resource
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def load_story_gen():
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return pipeline(
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"text-generation",
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model="gpt2",
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tokenizer="gpt2"
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)
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captioner = load_captioner()
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story_gen = load_story_gen()
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# 1) Image upload
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uploaded = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
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if uploaded:
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img = Image.open(uploaded)
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st.image(img, use_column_width=True)
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# 2) Generate caption
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with st.spinner("Generating caption…"):
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caps = captioner(img)
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# `caps` is a list of dicts like [{"generated_text": "..."}]
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caption = caps[0]["generated_text"]
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st.write("**Caption:**", caption)
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# 3) Generate story from caption
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with st.spinner("Spinning up a story…"):
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story_out = story_gen(
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caption,
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max_length=200,
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num_return_sequences=1,
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do_sample=True,
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top_p=0.9
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)
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story = story_out[0]["generated_text"]
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st.write("**Story:**", story)
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# 4) Play story as audio
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if st.button("🔊 Play Story Audio"):
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with st.spinner("Generating audio…"):
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tts = gTTS(text=story, lang="en")
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buf = io.BytesIO()
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tts.write_to_fp(buf)
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buf.seek(0)
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st.audio(buf.read(), format="audio/mp3")
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"""
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import streamlit as st
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from transformers import pipeline
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def main():
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sentiment_pipeline = pipeline(model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
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st.write(f"Confidence: {confidence:.2f}")
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if __name__ == "__main__":
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main()
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"""
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