add twitter feature

app.py

@@ -1,6 +1,7 @@
 from transformers import pipeline
 import matplotlib.pyplot as plt
 import twitter_scraper as ts
+import pandas as pd
 
 import gradio as gr
 
@@ -17,7 +18,8 @@ sentiment_pipeline = pipeline(
 ner_pipeline = pipeline(
     "ner",
     model=pretrained_ner,
-    tokenizer=pretrained_ner
+    tokenizer=pretrained_ner,
+    grouped_entities=True
 )
 
 examples = [
@@ -50,6 +52,23 @@ def sentiment_df(df):
     df['Score'] = scores
     return df
 
+def ner_df(df):
+    text_list = list(df["Text"].astype(str).values)
+    label_list = list(df["Label"].astype(str).values)
+    result = [ner(text) for text in text_list]
+    terms = []
+    sentiments = []
+    ent = ['PER', 'NOR']
+    for i, preds in enumerate(result):
+        for pred in preds['entities']:
+            if pred['entity_group'] in ent:
+                terms.append(pred['word'])
+                sentiments.append(label_list[i])
+    df_ner = pd.DataFrame(columns=['Entity', 'Sentiment'])
+    df_ner['Entity'] = terms
+    df_ner['Sentiment'] = sentiments
+    return df_ner
+
 
 def twitter_analyzer(keyword, max_tweets):
     df = ts.scrape_tweets(keyword, max_tweets=max_tweets)
@@ -59,6 +78,11 @@ def twitter_analyzer(keyword, max_tweets):
     df.groupby(["Label"])["Text"].count().plot.pie(autopct="%.1f%%", figsize=(6,6))
     return fig, df[["URL", "Text", "Label", "Score"]]
 
+def ner_analyzer(keyword, df_ner):
+    df_ner = df_ner[df_ner.line_race != keyword]
+    fig = plt.figure()
+    return fig
+
 if __name__ == "__main__":
 
     with gr.Blocks() as demo: