Upload 2 files

goodies/data.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:025591039882326919545ffe4e47a9285d3f567c617c7b061a4f335f8a3d3a2b
-size 11089589
+oid sha256:8bed21061c54fb40693e1879336dfd2be7f1583089ed286fe395e44e3fda1762
+size 11088805

goodies/sentiment.py

@@ -6,13 +6,14 @@ from bs4 import BeautifulSoup
 import pandas as pd
 import altair as alt
 from collections import OrderedDict
-import nltk
 from nltk.tokenize import sent_tokenize
 
+# Load the punkt tokenizer from nltk
+import nltk
 nltk.download('punkt')
 
 # Load model and tokenizer
-model_name = 'C:/projects/sentiment/albert_sentiment_model/checkpoint-3000'
+model_name = 'dejanseo/sentiment'
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
@@ -85,6 +86,21 @@ def classify_sentences(text):
 st.title("Sentiment Classification from URL")
 
 url = st.text_input("Enter URL:")
+
+# Additional information
+st.markdown("""
+Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/).
+
+The model is designed to be deployed in an automated pipeline capable of classifying text sentiment for thousands (or even millions) of text chunks or as a part of a scraping pipeline.
+
+This is a demo model which may occassionally misclasify some texts. In a typical commercial project, a larger model is deployed for the task, and in special cases, a domain-specific model is developed for the client.
+
+# Engage Our Team
+Interested in using this in an automated pipeline for bulk query processing?
+
+Please [book an appointment](https://dejanmarketing.com/conference/) to discuss your needs.
+""")
+
 if url:
     text = get_text_from_url(url)
     if text:
@@ -140,3 +156,15 @@ if url:
 
     else:
         st.write("Could not extract text from the provided URL.")
+
+# Additional information at the end
+st.markdown("""
+Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/).
+
+The model is designed to be deployed in an automated pipeline capable of classifying text sentiment for thousands (or even millions) of text chunks or as a part of a scraping pipeline. This is a demo model which may occassionally misclasify some texts. In a typical commercial project, a larger model is deployed for the task, and in special cases, a domain-specific model is developed for the client.
+
+### Engage Our Team
+Interested in using this in an automated pipeline for bulk query processing?
+
+Please [book an appointment](https://dejanmarketing.com/conference/) to discuss your needs.
+""")