new ammendments

app.py

@@ -1,48 +1,39 @@
+# Sami Alghamdi 21 May 2023
 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
+import io
 
 # Initialize objects
-emotion_pipeline = pipeline('sentiment-analysis', 
-model='nlptown/bert-base-multilingual-uncased-sentiment')
+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)
+    df = pd.read_csv(io.StringIO(file.decode('utf-8')))
     emotions = []
     sentiments = []
     entities = []
     keywords = []
-    for row in reader:
-        text = row['Content']  # Replace 'Content' with the correct column 
-name
+    for _, row in df.iterrows():
+        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
+        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)
+    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]
@@ -58,7 +49,5 @@ 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 = gr.Interface(fn=process_csv, inputs=gr.inputs.Textbox(lines=13, label="Paste CSV Here"), outputs="text")
 iface.launch()
-