PBusienei commited on
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5fdacf8
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1 Parent(s): 8def105

Formated the titles

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  1. app.py +5 -3
app.py CHANGED
@@ -25,9 +25,11 @@ from urllib.error import URLError
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  # set up title and sidebar
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  st.title (" Nashville Analytics Summit Conference Helper")# (" Your top 3 Important Sessions")
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- st.markdown("**Problem**🤔: Since its inception in 2013, Nashville Analytics Summit has seen a growth of over 488%. The Summit prides its itself as the fastest growing locally grown tech events in the south region. With an increasing number of participants and dozens of talks covering a myriad of topics, there is a need to tailor participants needs to their interests")
 
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- st.markdown("**Solution**💡: Develop an application in which users can input the description of areas of interest and app returns the top three Sessions matching the description requested.")
 
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  st.markdown("** Approach**🔑:")
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  st.markdown("* For the approach, I used a transformer model, multi-qa-MiniLM-L6-cos-v1, that uses sentence similarity to match the description of the event and the input description.")
@@ -64,7 +66,7 @@ st.markdown("**Sentence Similarity**")
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  # section 4: The process
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  # this is the pipeline in my notes (u are here highlight)
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- #st.markdown("### The Process 🔑")
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  #st.markdown("Imagine: A person is curious about whether a claim they heard about climate change is true. How can transformers help validate or refute the claim?")
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  # set up title and sidebar
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  st.title (" Nashville Analytics Summit Conference Helper")# (" Your top 3 Important Sessions")
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+ st.markdown("**Problem**🤔:")
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+ st.markdown("Since its inception in 2013, Nashville Analytics Summit has seen a growth of over 488%. The Summit prides its itself as the fastest growing locally grown tech events in the south region. With an increasing number of participants and dozens of talks covering a myriad of topics, there is a need to tailor participants needs to their interests")
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+ st.markdown("**Solution**💡:" )
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+ st.markdown("Develop an application in which users can input the description of areas of interest and app returns the top three Sessions matching the description requested.")
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  st.markdown("** Approach**🔑:")
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  st.markdown("* For the approach, I used a transformer model, multi-qa-MiniLM-L6-cos-v1, that uses sentence similarity to match the description of the event and the input description.")
 
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  # section 4: The process
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  # this is the pipeline in my notes (u are here highlight)
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+ st.markdown("### The Process 🔑")
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  #st.markdown("Imagine: A person is curious about whether a claim they heard about climate change is true. How can transformers help validate or refute the claim?")
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