add examples, additional description.

app.py

@@ -13,14 +13,13 @@ The LLM was finetuned on a Financial News Tweet Sentiment Dataset. The documents
 <img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px>
 """
 
-article = "Check out the dataset that [BERT cased]((https://huggingface.co/bert-base-cased?text=Paris+is+the+%5BMASK%5D+of+France.)) was [finetuned on](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment/viewer/zeroshot--twitter-financial-news-sentiment/train?row=9505)."
+article = "Check out the model card [here](https://huggingface.co/at2507/finetuned_model)!"
 
-
-def sentiment_analyzer(tweet):
+def sentiment_analyzer(financial_news_headline):
   tokenizer = AutoTokenizer.from_pretrained("at2507/finetuned_model")
   model = AutoModelForSequenceClassification.from_pretrained("at2507/finetuned_model")
   zeroshotsent_model = pipeline("text-classification", model = model.to('cpu:0'), tokenizer=tokenizer)
-  return zeroshotsent_model(tweet)
+  return zeroshotsent_model(financial_news_headline)
 
 gr.Interface(
     fn=sentiment_analyzer,
@@ -30,7 +29,9 @@ gr.Interface(
     description=description,
     article=article,
     examples=[["CLNE, TRXC, TGE and ADMS among midday movers"],
-     ["CRISPR Therapeutics among healthcare gainers; Plus Therapeutics leads the losers"]],
+     ["CRISPR Therapeutics among healthcare gainers; Plus Therapeutics leads the losers"], 
+    ["Firsthand Technology Value Fund and Itau CorpBanca among Financial gainers; Mmtec and Jupai among losers"], 
+    ["Canopy Growth up 6% as BofA buys the dip"]],
 ).launch()