everything

app.py

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+import streamlit as st
+import time
+from transformers import pipeline
+import os
+
+os.environ['KMP_DUPLICATE_LIB_OK'] = "True"
+
+
+
+st.title("Sentiment Analysis App")
+
+form = st.form(key='Sentiment Analysis')
+box = form.selectbox('Select Pre-trained Model:', ['bertweet-base-sentiment-analysis',
+                                                   'distilbert-base-uncased-finetuned-sst-2-english',
+                                                   'twitter-roberta-base-sentiment'
+                                                   ], key=1)
+tweet = form.text_input(label='Enter text to analyze:', value="\"We've seen in the last few months, unprecedented amounts of Voter Fraud.\" @SenTedCruz True!")
+submit = form.form_submit_button(label='Submit')
+
+if submit and tweet:
+    with st.spinner('Analyzing...'):
+        time.sleep(1)
+    # st.header(tweet)
+
+    if tweet is not None:
+        col1, col2, col3 = st.columns(3)
+        if box == 'bertweet-base-sentiment-analysis':
+            pipeline = pipeline(task="sentiment-analysis", model="finiteautomata/bertweet-base-sentiment-analysis")
+        elif box == 'twitter-xlm-roberta-base-sentiment':
+            pipeline = pipeline(task="sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
+        else:
+            pipeline = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+        predictions = pipeline(tweet)
+        print(predictions)
+        col1.header("Tweet")
+        col1.subheader(tweet)
+        col2.header("Judgement")
+        col3.header("Probability")
+        for p in predictions:
+            if box == 'bertweet-base-sentiment-analysis':
+                if p['label'] == "POS":
+                    col2.success(f"{ p['label'] }")
+                    col3.success(f"{ round(p['score'] * 100, 1)}%")
+                elif p['label'] == "NEU":
+                    col2.warning(f"{ p['label'] }")
+                    col3.warning(f"{round(p['score'] * 100, 1)}%")
+                else:
+                    col2.error(f"{p['label']}")
+                    col3.error(f"{round(p['score'] * 100, 1)}%")
+            elif box == 'distilbert-base-uncased-finetuned-sst-2-english':
+                if p['label'] == "POSITIVE":
+                    col2.success(f"{p['label']}")
+                    col3.success(f"{round(p['score'] * 100, 1)}%")
+                else:
+                    col2.error(f"{p['label']}")
+                    col3.error(f"{round(p['score'] * 100, 1)}%")
+            else:
+                if p['label'] == "POSITIVE":
+                    col2.success(f"{p['label']}")
+                    col3.success(f"{round(p['score'] * 100, 1)}%")
+                else:
+                    col2.error(f"{p['label']}")
+                    col3.error(f"{round(p['score'] * 100, 1)}%")

requirements.txt

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+streamlit
+transformers