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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", | |
| ), | |
| ], | |
| ) | |
| gradio_ui.launch(enable_queue=True) | |