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import gradio as gr | |
import pandas as pd | |
import numpy as np | |
import re | |
from transformers import pipeline | |
sentiment = pipeline( | |
"sentiment-analysis", | |
model="distilbert-base-uncased-finetuned-sst-2-english", | |
tokenizer="distilbert-base-uncased-finetuned-sst-2-english", | |
) | |
def clean_text(text): | |
text = text.encode("ascii", errors="ignore").decode( | |
"ascii" | |
) # remove non-ascii, Chinese characters | |
text = text.lower() | |
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(r"[^\w\s]", "", text) # remove punctuation and special characters | |
text = re.sub( | |
" +", " ", text | |
).strip() # get rid of multiple spaces and replace with a single | |
return text | |
# note that the sentiment-analysis pipeline returns 2 values - a label and a score | |
def sentiment_analysis(text): | |
input_text = ( | |
pd.DataFrame(text.split(".")) | |
.stack() | |
.reset_index() | |
.rename(columns={0: "Paras"}) | |
.drop("level_0", axis=1) | |
.drop("level_1", axis=1) | |
.dropna() | |
) | |
input_text["Clean_Text"] = input_text["Paras"].map(lambda text: clean_text(text)) | |
corpus = list(input_text["Clean_Text"].values) | |
input_text["Sentiment"] = sentiment(corpus) | |
input_text["Sentiment_Label"] = [x.get("label") for x in input_text["Sentiment"]] | |
input_text["Sentiment_Score"] = [x.get("score") for x in input_text["Sentiment"]] | |
cols = ["Paras", "Sentiment_Label", "Sentiment_Score"] | |
df = input_text[cols].copy() | |
df = df[df["Paras"].str.strip().astype(bool)] | |
df["Sentiment_Score"] = np.where( | |
df["Sentiment_Label"] == "NEGATIVE", | |
-(df["Sentiment_Score"]), | |
df["Sentiment_Score"], | |
) | |
df["Sentiment_Score"] = df["Sentiment_Score"].round(6) | |
overall_sentiment_score = df["Sentiment_Score"].sum().round(3) | |
sentiment_count = df["Sentiment_Label"].value_counts().to_string() | |
return overall_sentiment_score, sentiment_count, df | |
gradio_ui = gr.Interface( | |
fn=sentiment_analysis, | |
title="Analyse The Sentiment Structure Of A Speech", | |
description="Upload a speech or parts of it for a detailed sentiment analysis", | |
inputs=gr.inputs.Textbox(lines=30, label="Paste Text Here"), | |
outputs=[ | |
gr.outputs.Textbox(type="number", label="Overall Sentiment Score"), | |
gr.outputs.Textbox( | |
type="auto", label="How Many Positive & Negative Sentences?" | |
), | |
gr.outputs.Dataframe( | |
headers=["Paras", "Sentiment_Label", "Sentiment_Score"], | |
max_rows=None, | |
max_cols=3, | |
overflow_row_behaviour="paginate", | |
type="auto", | |
label="Detailed Assessment By Sentence", | |
), | |
], | |
enable_queue=True, | |
) | |
gradio_ui.launch() | |