converted to tf

app.py

@@ -1,13 +1,13 @@
 import numpy as np
 import gradio as gr
-import torch
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import tensorflow as tf
+from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
 
 checkpoint="manuu01/DeBERTa-SeagullStory"
 
 
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model=AutoModelForSequenceClassification.from_pretrained(checkpoint)
+model=TFAutoModelForSequenceClassification.from_pretrained(checkpoint,from_pt=True,dtype=tf.float16)
 answers=['Yes',"Doesn't matter","No"]
 f=open("Questions.txt","a")
 
@@ -67,14 +67,9 @@ def generate_tone(index,question):
     """.replace("\n","")
 
     ]
-    
-    input = tokenizer(passages[index], question, truncation=True, return_tensors="pt")
-    output = model(input["input_ids"].to("cpu"))  # device = "cuda:0" or "cpu"
-    prediction = torch.softmax(output["logits"][0], -1).tolist()
-    response=answers[np.argmax(prediction)]
-    
-    f.write(f'Passage = {index}\nQuestion: {question}\nAnswer: {response}\n\n')
-    return response
+    inputs=tokenizer(passages[index],question, return_tensors="tf")
+    response=(model(**inputs).logits)
+    return model.config.id2label[np.argmax(response)]
 
 passages=["General","Pier","Boat","Island"]