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| #importing the spacy and bert model | |
| from transformers import BertTokenizer, BertForSequenceClassification | |
| from transformers import pipeline | |
| import gradio as gr | |
| from collections import Counter | |
| import re | |
| import spacy | |
| import pandas as pd | |
| #Intializing the spacy model for NER and the finbert model for sentiment analysis | |
| nlp = spacy.load('en_core_web_sm') | |
| finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3) | |
| tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone') | |
| sentiment = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer) | |
| #defining a function to give us the sentiment of the article | |
| def return_sentiment(text): | |
| text = re.sub(r'Photo by.+', '', text) | |
| text = re.sub(r"\n", " ", text) | |
| text = re.sub(r"\n\n", " ", text) | |
| text = re.sub(r"\t", " ", text) | |
| text = text.strip(" ") | |
| text = re.sub( | |
| " +", " ", text | |
| ).strip() # get rid of multiple spaces and replace with a single | |
| results = sentiment(text[:512]) | |
| return (f"{results[0]['label']} ---> {results[0]['score']}") | |
| #defining a function to return the names of the organization present in the article | |
| def show_org(text): | |
| text = re.sub(r'Photo by.+', '', text) | |
| text = re.sub(r"\n", " ", text) | |
| text = re.sub(r"\n\n", " ", text) | |
| text = re.sub(r"\t", " ", text) | |
| text = text.strip(" ") | |
| text = re.sub( | |
| " +", " ", text | |
| ).strip() # get rid of multiple spaces and replace with a single | |
| org = [] | |
| doc = nlp(text) | |
| if doc.ents: | |
| for ent in doc.ents: | |
| if ent.label_ == 'ORG': | |
| org.append(ent.text) | |
| None | |
| final = (Counter(org).most_common(1)[0][0]) | |
| return (f'Organization: {final}') | |
| def final_output(text): | |
| return return_sentiment(text), show_org(text) | |
| sentiment_analysis = gr.Interface( | |
| final_output, | |
| inputs = gr.inputs.Textbox(label="Input your news article here", optional=False), | |
| outputs=[gr.outputs.Textbox(label="Sentiment Analysis"), | |
| gr.outputs.Textbox(label="Named Organization")] | |
| ) | |
| if __name__ == "__main__": | |
| sentiment_analysis.launch(debug=True) |