HOLYBOY commited on
Commit
a08209a
1 Parent(s): 546041c

Update app.py

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Files changed (1) hide show
  1. app.py +1 -46
app.py CHANGED
@@ -10,49 +10,4 @@ from transformers import AutoConfig
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  from transformers import AutoModelForSequenceClassification
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  from transformers import TFAutoModelForSequenceClassification
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  from transformers import pipeline
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- from scipy.special import softmax
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-
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- # Requirements
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- model_path ="HOLYBOY/Sentiment_Analysis"
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- tokenizer = AutoTokenizer.from_pretrained(model_path)
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- config = AutoConfig.from_pretrained(model_path)
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- model = AutoModelForSequenceClassification.from_pretrained(model_path)
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-
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- # Preprocess text (username and link placeholders)
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- def preprocess(text):
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- new_text = []
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- for t in text.split(" "):
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- t = "@user" if t.startswith("@") and len(t) > 1 else t
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- t = "http" if t.startswith("http") else t
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- new_text.append(t)
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- return " ".join(new_text)
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-
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- # ---- Function to process the input and return prediction
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- def sentiment_analysis(text):
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- text = preprocess(text)
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-
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- encoded_input = tokenizer(text, return_tensors = "pt") # for PyTorch-based models
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- output = model(**encoded_input)
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- scores_ = output[0][0].detach().numpy()
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- scores_ = softmax(scores_)
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-
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- # Format output dict of scores
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- labels = ["Negative", "Neutral", "Positive"]
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- scores = {l:float(s) for (l,s) in zip(labels, scores_) }
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-
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- return scores
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-
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- # ---- Gradio app interface
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- app = gr.Interface(fn = sentiment_analysis,
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- inputs = gr.Textbox("Input your tweet to classify or use the example provided below..."),
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- outputs = "label",
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- title = "Public Perception of COVID-19 Vaccines",
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- description = "This app analyzes Perception of text based on tweets about COVID-19 Vaccines using a fine-tuned distilBERT model",
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- interpretation = "default",
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- examples = [["The idea of introducing the vaccine is good"],
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- ["I am definately not taking the jab"],
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- ["The vaccine is bad and can cause serious health implications"],
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- ["I dont have any opinion "]]
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- )
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-
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- app.launch(share =True)
 
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  from transformers import AutoModelForSequenceClassification
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  from transformers import TFAutoModelForSequenceClassification
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  from transformers import pipeline
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+ from scipy.special import softmax