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Update app.py
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app.py
CHANGED
@@ -3,11 +3,12 @@ import streamlit as st
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from transformers import pipeline
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# function part
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#
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def
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# text2story
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def text2story(text):
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@@ -15,30 +16,28 @@ def text2story(text):
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story_text = pipe(text)[0]['generated_text']
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return story_text
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def text2audio(story_text):
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pipe = pipeline("text-to-audio", model="Matthijs/mms-tts-eng")
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audio_data = pipe(story_text)
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return audio_data
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def main():
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st.
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if uploaded_file is not None:
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print(uploaded_file)
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file.write(bytes_data)
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st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
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#Stage 1: Image to Text
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st.text('Processing img2text...')
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st.write(
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if __name__ == "__main__":
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main()
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from transformers import pipeline
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# function part
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# function part
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def generate_image_caption(image_path):
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"""Generates a caption for the given image using a pre-trained model."""
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img2caption = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
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result = img2caption(image_path)
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return result[0]['generated_text']
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# text2story
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def text2story(text):
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story_text = pipe(text)[0]['generated_text']
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return story_text
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def main():
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# App title
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st.title("Streamlit Demo on Hugging Face")
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# Write some text
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st.write("Welcome to a demo app showcasing basic Streamlit components!")
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uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
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if uploaded_file is not None:
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print(uploaded_file)
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st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
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#Stage 1: Image to Text
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st.text('Processing img2text...')
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image_caption = generate_image_caption(uploaded_image.name)
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st.write(image_caption)
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if __name__ == "__main__":
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main()
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