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Upload app.py
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app.py
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@@ -1,15 +1,11 @@
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import streamlit as st
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from transformers import pipeline
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from sentence_transformers import SentenceTransformer
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# Load the Sentence Transformers model
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model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
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# Load the sentiment analysis model
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classifier = pipeline("sentiment-analysis", model="Suphawan/website_classification")
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def main():
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st.title("Website
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with st.form("text_field"):
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text = st.text_area('Enter some text:')
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@@ -17,17 +13,13 @@ def main():
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clicked = st.form_submit_button("Submit text")
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if clicked:
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# Perform
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confidence =
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# Compute sentence embeddings
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sentence_embeddings = model.encode([text])
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st.write("
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st.write("Confidence:", confidence)
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st.write("Sentence Embeddings:", sentence_embeddings)
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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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# Load the sentiment analysis model
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classifier = pipeline("sentiment-analysis", model="Suphawan/website_classification")
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def main():
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st.title("Website Classification")
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with st.form("text_field"):
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text = st.text_area('Enter some text:')
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clicked = st.form_submit_button("Submit text")
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if clicked:
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# Perform website classification
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classification_results = classifier([text])
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category = classification_results[0]['label']
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confidence = classification_results[0]['score']
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st.write("Category:", category)
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st.write("Confidence:", confidence)
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if __name__ == "__main__":
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main()
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