test 3rd option

app.py

@@ -12,19 +12,40 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 classifier = pipeline(task="sentiment-analysis", model=model, tokenizer=tokenizer)
-# classifier = pipeline(task="sentiment-analysis")
 
 textIn = st.text_input("Input Text Here:", "I really like the color of your car!")
 
-option = st.selectbox('Which pre-trained model would you like for your sentiment analysis?',('Pipeline', 'TextBlob'))
+option = st.selectbox('Which pre-trained model would you like for your sentiment analysis?',('Pipeline', 'TextBlob', 'FINE-TUNED'))
 
 st.write('You selected:', option)
 
 
-tokens = tokenizer.tokenize(textIn)
-token_ids = tokenizer.convert_tokens_to_ids(tokens)
-input_ids = tokenizer(textIn)
+#------------------------------------------------------------------------
 
+# tokens = tokenizer.tokenize(textIn)
+# token_ids = tokenizer.convert_tokens_to_ids(tokens)
+# input_ids = tokenizer(textIn)
+
+
+# X_train = [textIn]
+
+# batch = tokenizer(X_train, padding=True, truncation=True, max_length=512, return_tensors="pt")
+# # batch = torch.tensor(batchbatch["input_ids"])
+
+# with torch.no_grad():
+#     outputs = model(**batch, labels=torch.tensor([1, 0]))
+#     predictions = F.softmax(outputs.logits, dim=1)
+#     labels = torch.argmax(predictions, dim=1)
+#     labels = [model.config.id2label[label_id] for label_id in labels.tolist()]
+
+# # save_directory = "saved"
+# tokenizer.save_pretrained(save_directory)
+# model.save_pretrained(save_directory)
+
+# tokenizer = AutoTokenizer.from_pretrained(save_directory)
+# model = AutoModelForSequenceClassification.from_pretrained(save_directory)
+
+#------------------------------------------------------------------------
 
 if option == 'Pipeline':
     # pipeline
@@ -45,3 +66,7 @@ if option == 'TextBlob':
         sentiment = 'Positive'
 
     st.write('According to TextBlob, input text is ', sentiment, ' and a subjectivity score (from 0 being objective to 1 being subjective) of ', subjectivity)
+
+
+if option == 'FINE-TUNED':
+    ...