Sentiment analysis T5

app.py

@@ -1,24 +1,13 @@
-from transformers import AutoTokenizer, AutoModelWithLMHead
+from transformers import T5ForConditionalGeneration, T5Tokenizer
 import gradio as grad
-
-text2text_tkn = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small")
-mdl = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/wikihow-t5-small")
-
-
-def text2text_summary(para):
-    initial_txt = para.strip().replace("\n", "")
-    tkn_text = text2text_tkn.encode(initial_txt, return_tensors="pt")
-    tkn_ids = mdl.generate(
-        tkn_text,
-        max_length=250,
-        num_beams=5,
-        repetition_penalty=2.5,
-        early_stopping=True
-    )
-    response = text2text_tkn.decode(tkn_ids[0], skip_special_tokens=True)
+text2text_tkn= T5Tokenizer.from_pretrained("t5-small")
+mdl = T5ForConditionalGeneration.from_pretrained("t5-small")
+def text2text_sentiment(text):
+    inp = "sst2 sentence: "+text
+    enc = text2text_tkn(inp, return_tensors="pt")
+    tokens = mdl.generate(**enc)
+    response=text2text_tkn.batch_decode(tokens)
     return response
-
-
-para = grad.Textbox(lines=10, label="Paragraph", placeholder="Copy paragraph")
-out = grad.Textbox(lines=1, label="Summary")
-grad.Interface(text2text_summary, inputs=para, outputs=out).launch()
+para=grad.Textbox(lines=1, label="English Text", placeholder="Text in English")
+out=grad.Textbox(lines=1, label="Sentiment")
+grad.Interface(text2text_sentiment, inputs=para, outputs=out).launch()