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| import streamlit as st | |
| import transformers | |
| import numpy as np | |
| # Load the pre-trained model | |
| model1 = transformers.pipeline("text2text-generation", model="bigscience/T0pp") | |
| model2 = transformers.pipeline("text2text-generation", model="google/flan-t5-xxl") | |
| model3 = transformers.pipeline("text2text-generation", model="google/flan-t5-xl") | |
| model4 = transformers.pipeline("text2text-generation", model="tuner007/pegasus_paraphrase") | |
| model5 = transformers.pipeline("text2text-generation", model="tuner007/pegasus_paraphrase") | |
| # Define the Streamlit app | |
| def main(): | |
| st.title("Topic Modeling with Hugging Face") | |
| text = st.text_area("Enter some text to generate topics", height=200) | |
| if st.button("Generate Topics"): | |
| # Generate topics | |
| topics1 = model1(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7) | |
| topics2 = model2(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7) | |
| topics3 = model3(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7) | |
| topics4 = model4(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7) | |
| topics5 = model5(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7) | |
| # Print topics | |
| st.write("Top 5 topics:") | |
| for i in range(5): | |
| st.write(f"{i+1}. {topics1[i]['generated_text']}") | |
| st.write(f"{i+1}. {topics2[i]['generated_text']}") | |
| st.write(f"{i+1}. {topics3[i]['generated_text']}") | |
| st.write(f"{i+1}. {topics4[i]['generated_text']}") | |
| st.write(f"{i+1}. {topics5[i]['generated_text']}") | |
| if __name__ == "__main__": | |
| main() | |