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Streamlit_app.py

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+import streamlit as st 
+import numpy as np
+import transformers
+import torch
+import pandas as pd
+import plotly.express as px
+import matplotlib.pyplot as plt
+import select
+from PIL import Image
+
+
+
+# Load the dataset
+df = pd.read_csv('Eval_subset.csv')
+st.set_page_config(
+    page_title="Twitter Sentiment Analyzer", page_icon="📊", layout="wide"
+)
+
+#Image Description
+image = Image.open('image1.jpg')
+image_y = Image.open('image2.png')
+st.image([image,image_y])
+
+
+@st.cache_resource
+def get_model():
+    model = transformers.AutoModelForSequenceClassification.from_pretrained("Kwasiasomani/Finetuned-Distilbert-base-model")
+    tokenizer = transformers.AutoTokenizer.from_pretrained("Kwasiasomani/Finetuned-Distilbert-base-model")
+    return tokenizer,model
+
+tokenizer, model = get_model()
+
+st.header("Covid19 Twitter Sentimental Analysis")
+st.text("This app uses Distilbert-base-uncased for the analysis.")
+add_text_sidebar = st.sidebar.title("Menu")
+with st.sidebar:
+    st.title("Twitter Sentiment Analyzer")
+
+    st.markdown(
+        """
+        <div style="text-align: justify;">
+            This app performs sentiment analysis on the latest tweets based on 
+            the entered search term. Since the app can only predict positive or 
+            negative, and neutral sentiment, it is more suitable towards analyzing the 
+            sentiment of brand, product, service, company, or person. 
+            Only English tweets are supported.
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+#Graphical representation
+st.sidebar.markdown("### Number of tweets by sentiment")
+sentiment_count = df['safe_text'].value_counts()
+sentiment_count = pd.DataFrame({'Sentiment':sentiment_count.index, 'Tweets':sentiment_count.values})
+if not st.sidebar.checkbox("Hide", True):
+    st.markdown("### Number of tweets by sentiment")
+    if select == 'Bar plot':
+        fig = px.bar(sentiment_count, x='Sentiment', y='Tweets', color='Tweets', height=50)
+        st.plotly_chart(fig)
+    else:
+        fig = px.pie(sentiment_count, values='Tweets', names='Sentiment')
+        st.plotly_chart(fig)
+
+
+
+
+
+# Side view description 
+st.sidebar.subheader("Show random tweet")
+random_tweet = st.sidebar.radio('Sentiment', ('positive', 'neutral', 'negative'))
+st.sidebar.markdown(df['safe_text'])
+
+
+st.markdown("[Github link](https://github.com/kwasiasomani)")
+st.markdown("[Medium link](https://medium.com/@kwasiasomani85)")
+st.markdown('Created by Foster,Kwasi,Linda,Stella,Joshua and Bright')
+user_input = st.text_area('Enter text to predict')
+button = st.button('predict')
+
+
+# Define Helper Function
+label = {0: 'Negative', 1:'Neutral', 2:'Positive'}
+
+
+      
+# Prediction
+if user_input and button:
+    test_input = tokenizer([user_input],return_tensors='pt')
+    st.slider("Number of tweets", min_value=100, max_value=2000, key="user_input")
+
+    # Test output
+    output = model(**test_input)
+    st.write('Logits:',output.logits)
+    predicted_class = np.argmax(output.logits.detach().numpy())
+    st.write('prediction:',label[predicted_class])