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import streamlit as st | |
from transformers import pipeline | |
from sentence_transformers import SentenceTransformer | |
# Load the Sentence Transformers model | |
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') | |
# Load the website classification model (replace 'Suphawan/website_classification' with your actual model) | |
classifier = pipeline("sentiment-analysis", model="Suphawan/website_classification") | |
def main(): | |
st.title("Website Classification") | |
with st.form("text_field"): | |
text = st.text_area('Enter some text:') | |
# clicked will be True only when the button is clicked | |
clicked = st.form_submit_button("Submit text") | |
if clicked: | |
# Perform website classification | |
classification_results = classifier([text]) | |
category = classification_results[0]['label'] | |
confidence = classification_results[0]['score'] | |
# Compute sentence embeddings | |
sentence_embeddings = model.encode([text]) | |
st.write("Category:", category) | |
st.write("Confidence:", confidence) | |
st.write("Sentence Embeddings:", sentence_embeddings) | |
if __name__ == "__main__": | |
main() | |