Add application file

app.py

@@ -0,0 +1,64 @@
+import pandas as pd
+import spacy
+import gradio as gr
+import csv
+from nrclex import NRCLex
+from transformers import pipeline
+from rake_nltk import Rake
+
+# 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):
+    reader = csv.DictReader(file)
+    emotions = []
+    sentiments = []
+    entities = []
+    keywords = []
+    for row in reader:
+        text = row['Content']  # Replace 'Content' with the correct column 
+name
+        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))  # Extract keywords for 
+each text
+
+    fieldnames = reader.fieldnames + list(emotions[0].keys()) + 
+['sentiment', 'entities', 'keywords']
+    output = []
+    for row, emotion_scores, sentiment, entity, keyword in zip(reader, 
+emotions, sentiments, entities, keywords):
+        row.update(emotion_scores)  # Update the row dictionary with 
+emotion scores
+        row.update({'sentiment': sentiment, 'entities': entity, 
+'keywords': keyword})  # Update the row dictionary with sentiment, 
+entities and keywords
+        output.append({field: row.get(field, '') for field in fieldnames})  
+# Write row with matching fields or empty values
+    return pd.DataFrame(output).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()  # Extract keywords from text
+
+iface = gr.Interface(fn=process_csv, inputs=gr.inputs.File(type='csv'), 
+outputs=gr.outputs.File())
+iface.launch()
+