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| import gradio as gr | |
| import pandas as pd | |
| from transformers import pipeline | |
| from transformers import DistilBertTokenizer, DistilBertForSequenceClassification | |
| df = pd.read_csv("WhatsApp.csv") | |
| tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased-finetuned-sst-2-english') | |
| model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased-finetuned-sst-2-english') | |
| nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) | |
| def sentiment_analysis_model(review): | |
| res = nlp(review) | |
| label = res[0]['label'] | |
| score = res[0]['score'] | |
| output = f"{label}, Score: {score}" | |
| return output | |
| demo = gr.Interface(fn=sentiment_analysis_model, inputs="text", outputs="text", title="SENTIMENT ANALYSIS using DistilBert model", | |
| description="Sentiment Analysis using Pretrained DistilBert model. The DistilBert model is trained on a sample data of google playstore reviews for WhatsApp application. Provide a review and the model will classify it to be POSITIVE or NEGATIVE") | |
| demo.launch() | |