added the pipeline package

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import pandas as pd
 import altair as alt
-#from transformers import pipeline
+from transformers import pipeline
 from PIL import Image
 #!pip install -U sentence-transformers
 from sentence_transformers import SentenceTransformer, util
@@ -27,30 +27,26 @@ st.title (" Nashville Analytics Summit Conference Helper")# (" Your top 3 Import
 
 st.markdown("**Problem**🤔:")
 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")
-
+st.markdown("---")
 st.markdown("**Solution**💡:" )
 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.")
-
+st.markdown("---")
 st.markdown("** Approach**🔑:") 
 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.")
 st.markdown("* The dataset used is Nashville Analytics Summit descriptions of the presentations, which include the Unique ID, Name of presenter, Description of presentation, Activity Code, Start Time, End Time, Location Name")
 
-st.markdown("* Given a {claim, evidence} pair, determine whether the climate claim is supported or refuted (or neither) by the evidence")
-
 st.markdown("---")
 
-st.markdown("## The Details")
+#st.markdown("### The Details")
 
 # section 1: the context, problem; how to address
 st.markdown("### Problem")
 st.markdown("With the growth of the Nashville Analytics Summit every year, there have been an increase in the number participants, the number of talks/topics have exponentially increased over time covering a myriad of current issues and developments. This has made it challenging for the participants to easily navigate the event and attend the sessions of interest before reading through the sessions descriptions. Struggling finding the Session that Matches your personal interests? Transformers to the rescue!!!.")
 
 dificult_reading = Image.open('dificult_reading.png')
-st.image(dificult_reading, caption='Reading sessions descriptions. (1)')
-
-
-
+st.image(dificult_reading, caption='Reading sessions descriptions.')
 
+st.markdown("---")
 # section 2: how can transformers help?
 st.markdown("### How can Transformers Help?💡")
 
@@ -63,7 +59,7 @@ st.markdown("**Sentence Similarity**")
 #st.markdown("* This is simply an extenstion of the textual entailment task")
 #st.markdown("* Given two sentences, sent1 and sent2, determine the relationship: entail, contradict, neutral")
 #st.markdown("* With fact verification, we can think of the sentences as claim and evidence and labels as support, refute, or not enough information to refute or support.")
-
+st.markdown("---")
 # section 4: The process
 # this is the pipeline in my notes (u are here highlight)
 st.markdown("### The Process 🔑")