Update app.py

app.py

@@ -8,16 +8,6 @@ from scipy.special import softmax
 from transformers import pipeline
 
 
-
-
-#import the model
-pipe=pipeline(model="Junr-syl/sentiments_analysis_DISTILBERT")
-# tokenizer = AutoTokenizer.from_pretrained('Junr-syl/sentiments_analysis_DISTILBERT')
-
-# model_path = f"Junr-syl/sentiments_analysis_DISTILBERT"
-# config = AutoConfig.from_pretrained(model_path)
-# model = AutoModelForSequenceClassification.from_pretrained(model_path)
-
 #Set the page configs
 st.set_page_config(page_title='Sentiments Analysis',page_icon='😎',layout='centered')
 
@@ -29,52 +19,42 @@ st.markdown("<h1 style='text-align: center'> Tweet Sentiments </h1>",unsafe_allo
 with st.form(key='tweet',clear_on_submit=True):
     text=st.text_area('Copy and paste a tweet or type one',placeholder='I find it quite amusing how people ignore the effects of not taking the vaccine')
     submit=st.form_submit_button('submit')
+if submit:
+    st.success('Text received',icon='✅')
+#Select a model
+exp=st.expander(label='Choose a model and Click Continue or go ahead and Click continue to use default')
+models={'Roberta': 'Junr-syl/sentiments_analysis_Roberta',
+        'Bert':    'Junr-syl/sentiments_analysis_upgrade',
+        'Distilbert':'Junr-syl/sentiments_analysis_DISTILBERT'}
+
+with exp:
+    model=st.selectbox('Which model would you want to Use?',('Bert','Roberta','Distilbert'))
+
+selected_model=models[model]
+#Click continue if no model is selected
+Cont=st.button('Continue','Continue processing input')
 
 #create columns to show outputs
 col1,col2,col3=st.columns(3)
-col1.title('Sentiment Emoji')
-col2.title('How this user feels about the vaccine')
-col3.title('Confidence of this prediction')
+col1.write('<h2 style="font-size: 24px;"> Sentiment Emoji </h2>',unsafe_allow_html=True)
+col2.write('<h2 style="font-size: 24px;"> How this user feels about the vaccine </h2>',unsafe_allow_html=True)
+col3.write('<h2 style="font-size: 24px;"> Confidence of this prediction </h2>',unsafe_allow_html=True)
 
-# if submit:
-#     print('submitted')
-#     #pass text to preprocessor
-#     def preprocess(text):
-#     #initiate an empty list 
-#         new_text = []
-#         #split text by space
-#         for t in text.split(" "):
-#             #set username to @user
-#             t = '@user' if t.startswith('@') and len(t) > 1 else t  
-#             #set tweet source to http
-#             t = 'http' if t.startswith('http') else t 
-#             #store text in the list
-#             new_text.append(t)
-#             #change text from list back to string
-#         return " ".join(new_text) 
-    
+if Cont:
+    #import the model
+    pipe=pipeline(model=selected_model)
+    tokenizer = AutoTokenizer.from_pretrained('selected_model')
+
+    model_path = f"Junr-syl/sentiments_analysis_DISTILBERT"
+    config = AutoConfig.from_pretrained(model_path)
+    model = AutoModelForSequenceClassification.from_pretrained(model_path)
 
-#     #pass text to model
+    
+#pass text to model
 output=pipe(text)
 output_dict=output[0]
 lable=output_dict['label']
 score=output_dict['score']
-#     #change label id 
-#     #config.id2label = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
-
-#     text = preprocess(text)
-
-#     # PyTorch-based models
-#     encoded_input = tokenizer(text, return_tensors='pt')
-#     output = model(**encoded_input)
-#     scores = output[0][0].detach().numpy()
-#     scores = softmax(scores)
-
-#     #Process scores
-#     ranking = np.argsort(scores)
-#     ranking = ranking[::-1]  
-#     l = config.id2label[ranking[0]]
-#     s = scores[ranking[0]]
 
     #output
 if lable=='NEGATIVE':