update UI

app.py

@@ -1,14 +1,8 @@
 import gradio as gr
-from datetime import date
-import json
-import csv
-import datetime
-import smtplib
-from email.mime.text import MIMEText
-import requests
 from transformers import AutoTokenizer, AutoModelWithLMHead 
 import gc
 import os
+import pandas as pd
 
 cwd = os.getcwd()
 model_path = os.path.join(cwd)
@@ -27,10 +21,10 @@ def get_emotion(text):
     return label
 
 def generate_emotion(article):
-    print("hello")
     sen_list = article
-    sen_list = sen_list.split('\r\n')
+    sen_list = sen_list.split('\n')
     sen_list_temp = sen_list[0:]
+    print(sen_list_temp)
     results_dict = []
     results = []
 
@@ -46,16 +40,18 @@ def generate_emotion(article):
                 }
             )
 
-    result = {
-        'result': results_dict,
-    }
+    # result = {
+    #     'result': results_dict,
+    # }
+    result = {'Input':sen_list_temp, 'Detected Emotion':results}
     gc.collect()
-    print("LENGTH of results ====> ", results)
+    # print("LENGTH of results ====> ", results)
     
-    return result
+    return pd.DataFrame(result)
 
 inputs=gr.Textbox(lines=10, label="Sentences",elem_id="inp_div")
-outputs=gr.Textbox(lines=10, label="Here is the Result",elem_id="inp_div")
+outputs = [gr.Dataframe(row_count = (1, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Input","Detected Emotion"])]
+
 
 demo = gr.Interface(
     generate_emotion,
@@ -63,6 +59,6 @@ demo = gr.Interface(
     outputs,
     title="Emotion Detection",
     description="Feel free to give your feedback", 
-    css=".gradio-container {background-color: lightgray} #inp_div {background-color: [#7](https://www1.example.com/issues/7)FB3D5;"
+    css=".gradio-container {background-color: lightgray} #inp_div {background-color: #FB3D5;}"
 )
 demo.launch()