Upload 2 files

app.py

@@ -0,0 +1,88 @@
+import streamlit as st
+import streamlit.components.v1 as com
+#import libraries
+from transformers import AutoModelForSequenceClassification,AutoTokenizer, AutoConfig
+import numpy as np
+#convert logits to probabilities
+from scipy.special import softmax
+
+
+#import the model
+tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
+
+model_path = f"penscola/tweet_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='wide')
+
+#welcome Animation
+com.iframe("https://embed.lottiefiles.com/animation/149093")
+st.markdown('<h1> Tweet Sentiments </h1>',unsafe_allow_html=True)
+
+#Create a form to take user inputs
+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')
+
+#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')
+
+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) 
+    
+
+    #pass text to model
+
+    #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 l=='NEGATIVE':
+        with col1:
+            com.iframe("https://embed.lottiefiles.com/animation/125694")
+        col2.write('Negative')
+        col3.write(f'{s}%')
+    elif l=='POSITIVE':
+        with col1:
+            com.iframe("https://embed.lottiefiles.com/animation/148485")
+        col2.write('Positive')
+        col3.write(f'{s}%')
+    else:
+        with col1:
+            com.iframe("https://embed.lottiefiles.com/animation/136052")
+        col2.write('Neutral')
+        col3.write(f'{s}%')
+
+

requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+transformers
+transformers[torch]
+Scipy