updated with larger model

app.py

@@ -6,7 +6,7 @@ from transformers import pipeline, AutoModelForSequenceClassification, AutoToken
 def get_model():
     tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
     pulled_model = AutoModelForSequenceClassification.from_pretrained(
-        "NazmusAshrafi/stock_twitter_topic_Bert")
+        "NazmusAshrafi/large_dataset_stock_twitter_topic_Bert")
     return tokenizer, pulled_model
 
 
@@ -21,21 +21,34 @@ classifier = pipeline("sentiment-analysis",
 
 st.title("Find the topic of a stock related tweets")
 st.subheader(
-    'This model can predict 3 topics - :blue[Investment decision], :green[User Volume], :orange[Market crisis] - Entering a topic related tweet will yeild the best results')
-
+    'This model can predict 5 topics - :blue[Tesla Investment News], :green[Technology stock watchlist], :orange[Esports News], :blue[Apple Market Updates], :green[Amazon Updates] - Entering a topic related tweet will yeild the best results')
+
+# elif topic == 0:
+#     return 'Tesla Investment News'
+# elif topic == 1:
+#     return 'Technology stock watchlist'
+# elif topic == 2:
+#     return 'Esports News'
+# elif topic == 3:
+#     return 'Apple Market Updates'
+# elif topic == 4:
+#     return 'Amazon Updates'
 
+st.subheader(
+    'Examples:')
+st.markdown(
+    ':blue[Tesla Investment News: Electric cars stock going to go up in future, buy now"]')
 st.markdown(
-    ':blue[Investment decision example: "AAP Dow Theory = Will go down in future, Do not buy"]')
+    ':green[Technology stock watchlist: "Keep a look out for that stock, its really good!"]')
 st.markdown(
-    ':green[User Volume example: "Loosing a lot of volume, user are leaving this platform"]')
+    ':orange[Esports News: "Ninja is going to play in the next tournament, the best is back playing Fortnite."]')
 st.markdown(
-    ':orange[Market crisis example: "Market is in danger because of corona virus"]')
+    ':blue[Apple Market Updates: "$AAPL released a new phone but the looks never change much. Buy anyway!"]')
+st.markdown(
+    ':green[Amazon Updates: "Black Fridays at Amazon, hail Jeff Bezos"]')
 
 
 st.subheader("", divider='rainbow')
-# 0 = Investment decision
-# 1 = User volume
-# 2 = Market crisis
 
 
 user_input = st.text_area("Enter a tweet about a stock")
@@ -50,5 +63,5 @@ if user_input and button:
     st.write("Confidence Score: ", classifier(user_input)[0]['score'])
 
 
-st.markdown(
-    'Note: Model may be predicting "Investment decision" way too often, this is because of the data imbalance. The model was trained on 3 topics, but the data was not balanced. The model was trained on a dataset where a large portion of the data was on "Investment decision" topic. This is why the model is biased towards "Investment decision" topic. This is a flaw of this model, and it can be fixed by training the model on a balanced dataset.')
+# st.markdown(
+#     'Note: Model may be predicting "Investment decision" way too often, this is because of the data imbalance. The model was trained on 3 topics, but the data was not balanced. The model was trained on a dataset where a large portion of the data was on "Investment decision" topic. This is why the model is biased towards "Investment decision" topic. This is a flaw of this model, and it can be fixed by training the model on a balanced dataset.')

instructions.txt

@@ -0,0 +1,7 @@
+Run using:
+streamlit run app.py
+
+Push using:
+git add .
+git comit -m "New feature added"
+git push