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import pandas as pd
import spacy
import gradio as gr
from nrclex import NRCLex
from transformers import pipeline
from rake_nltk import Rake
import io
import spacy.cli
# Download the spaCy model
spacy.cli.download("en_core_web_sm")
# Initialize objects
emotion_pipeline = pipeline('sentiment-analysis', model='nlptown/bert-base-multilingual-uncased-sentiment')
nlp = spacy.load('en_core_web_sm')
rake = Rake()
def process_csv(file):
df = pd.read_csv(io.StringIO(file.decode('utf-8')))
emotions = []
sentiments = []
entities = []
keywords = []
for _, row in df.iterrows():
text = row['Content']
nrc_obj = NRCLex(text)
emotion_scores = nrc_obj.affect_frequencies
emotions.append(emotion_scores)
sentiment = analyze_emotion(text)
sentiments.append(sentiment)
entities.append(analyze_entities(text))
keywords.append(extract_keywords(text))
df['emotions'] = emotions
df['sentiment'] = sentiments
df['entities'] = entities
df['keywords'] = keywords
return df.to_csv(index=False)
def analyze_emotion(text):
result = emotion_pipeline(text)[0]
sentiment = result['label']
return sentiment
def analyze_entities(text):
doc = nlp(text)
entities = [(ent.text, ent.label_) for ent in doc.ents]
return entities
def extract_keywords(text):
rake.extract_keywords_from_text(text)
return rake.get_ranked_phrases()
iface = gr.Interface(fn=process_csv, inputs=gr.inputs.Textbox(lines=13, label="Paste CSV Here"), outputs="text")
iface.launch()
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