Update app.py

app.py

@@ -1,13 +1,61 @@
 import gradio as gr
+import os
+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
 
+cwd = os.getcwd()
+model_path = os.path.join(cwd)
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion")
+model_base = AutoModelWithLMHead.from_pretrained(model_path)
+def get_emotion(text):
 
+    # input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
+    input_ids = tokenizer.encode(text, return_tensors='pt')
+    output = model_base.generate(input_ids=input_ids,
+                                 max_length=2)
 
-def generate_emotion(sentences):
-    result = "happy"
+    dec = [tokenizer.decode(ids) for ids in output]
+    label = dec[0]
+    gc.collect()
+    return label
+
+def generate_emotion(article):
+    sen_list = article
+    sen_list = sen_list.split('\r\n')
+    sen_list_temp = sen_list[0:]
+    results_dict = []
+    results = []
+
+    for sen in sen_list_temp:
+        if(sen.strip()):
+            log_sen_list.append(sen)
+            cur_result = get_emotion(sen)
+
+            results.append(cur_result)
+            results_dict.append(
+                {
+                    'sentence': sen,
+                    'emotion': cur_result
+                }
+            )
+
+    result = {
+        'result': results_dict,
+    }
+    gc.collect()
+    print("LENGTH of results ====> ", results)
+    
     return result
 
-inputs=gr.Textbox(lines=5, label="Sentences",elem_id="inp_div")
-outputs=gr.Textbox(lines=5, label="Here is the Result",elem_id="inp_div")
+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")
 
 demo = gr.Interface(
     generate_emotion,