Update app.py

app.py

@@ -1,34 +1,38 @@
-import gradio as gr
-from transformers import AutoTokenizer
-import timm
+# import gradio as gr
+# from transformers import AutoTokenizer
+# import timm
+
+# title = "Finetuning [BERT] on A Financial News Sentiment Dataset"
+# description = """
+# The LLM was finetuned on a Financial News Tweet Sentiment Dataset. The documents have 3 different labels: 
+#  "LABEL_0": "Bearish", 
+#   "LABEL_1": "Bullish", 
+#   "LABEL_2": "Neutral"
+# <img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px>
+# """
 
-title = "Finetuning [BERT] on A Financial News Sentiment Dataset"
-description = """
-The LLM was finetuned on a Financial News Tweet Sentiment Dataset. The documents have 3 different labels: 
- "LABEL_0": "Bearish", 
-  "LABEL_1": "Bullish", 
-  "LABEL_2": "Neutral"
-<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 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)."
 
+# def sentiment_analyzer(tweet):
+#   model_reloaded = timm.create_model('hf_hub:at2507/zeroshot_finetuned_sentiment', pretrained=True)
+#   # model = model.load("models/at2507/zeroshot_finetuned_sentiment")
+#   # gr.Interface.load("models/at2507/zeroshot_finetuned_sentiment").launch()
+#   tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+#   zeroshotsent_model = pipeline("text-classification", model = model.to('cpu:0'), tokenizer=tokenizer)
+#   return zeroshotsent_model(tweet)
 
-def sentiment_analyzer(tweet):
-  model_reloaded = timm.create_model('hf_hub:at2507/zeroshot_finetuned_sentiment', pretrained=True)
-  # model = model.load("models/at2507/zeroshot_finetuned_sentiment")
-  # gr.Interface.load("models/at2507/zeroshot_finetuned_sentiment").launch()
-  tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-  zeroshotsent_model = pipeline("text-classification", model = model.to('cpu:0'), tokenizer=tokenizer)
-  return zeroshotsent_model(tweet)
+# gr.Interface(
+#     fn=sentiment_analyzer,
+#     inputs="textbox",
+#     outputs="text",
+#     title=title,
+#     description=description,
+#     article=article,
+#     examples=[["CLNE, TRXC, TGE and ADMS among midday movers"], 
+#      ["CRISPR Therapeutics among healthcare gainers; Plus Therapeutics leads the losers"]],
+# ).launch()
+
+import gradio as gr
 
-gr.Interface(
-    fn=sentiment_analyzer,
-    inputs="textbox",
-    outputs="text",
-    title=title,
-    description=description,
-    article=article,
-    examples=[["CLNE, TRXC, TGE and ADMS among midday movers"], 
-     ["CRISPR Therapeutics among healthcare gainers; Plus Therapeutics leads the losers"]],
-).launch()
+gr.Interface.load("models/at2507/zeroshot_finetuned_sentiment").launch()