commit

Sentiment Analysis using HuggingFace

sentimentappstreamlit.py

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+# importing libraries
+import streamlit as st
+import transformers
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import os, pickle
+import datetime
+import time
+import base64
+import pandas as pd
+import altair as alt
+from textblob import TextBlob
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+from PIL import Image
+
+
+
+# Load the tokenizer and the fine-tuned model
+tokenizer = AutoTokenizer.from_pretrained('Adoley/covid-tweets-sentiment-analysis')
+model = AutoModelForSequenceClassification.from_pretrained('Adoley/covid-tweets-sentiment-analysis')
+
+# Create a sentiment analysis pipeline with your fine-tuned model
+nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+
+
+def predict_sentiment(input_text):
+    # Use the nlp pipeline to predict the sentiment of the input text
+    result = nlp(input_text)[0]
+    # Extract the predicted sentiment label and score
+    label = result['label']
+    score = result['score']
+    # Map the sentiment label to an emoji
+    sentiment_emojis = {
+        'LABEL_0': '😔',  # Negative
+        'LABEL_1': '😊',  # Positive
+        'LABEL_2': '😐'   # Neutral
+    }
+    emoji = sentiment_emojis[label]
+    # Return the predicted sentiment label, score, and emoji
+    return label, score, emoji
+
+
+
+st.set_page_config(page_title="Sentiment Analysis App")
+st.title('Sentiment Analysis App')
+
+# Import the image
+image = Image.open('sentiments.png')
+
+# Show the image
+st.image(image, caption='Sentiment Analysis', use_column_width=True)
+
+# Set up sidebar
+st.sidebar.header('Navigation')
+menu = ['Home', 'About']
+choice = st.sidebar.selectbox("Select an option", menu)
+
+# Home section
+if choice == 'Home':
+    st.markdown("<h1 style='text-align: center;'>Welcome</h1>", unsafe_allow_html=True)
+    st.markdown("<p style='text-align: center;'>This App predicts the sentiments expressed in an input.</p>", unsafe_allow_html=True)
+
+    # Set Page Title
+    st.title('Sentiment Prediction App')
+    st.markdown('Type your text and click on Submit')
+    text_input = st.text_area(label="Enter some text here", height=100, max_chars=1000)
+    st.write('You entered:', text_input)
+
+    # Create a dataframe to hold the sentiment scores
+    data = {'sentiment': ['Negative', 'Neutral', 'Positive'], 'score': [0, 0, 0]}
+    df = pd.DataFrame(data)
+
+    if st.button('Predict'):
+        if text_input.strip() == '':
+         st.warning('Please enter some text.')
+        else:
+            label, score, emoji = predict_sentiment(text_input)
+            sentiment_map = {
+            'LABEL_0': 'Negative',
+            'LABEL_1': 'Positive',
+            'LABEL_2': 'Neutral'
+        }
+            sentiment = sentiment_map[label]
+            st.success(f'The sentiment is {sentiment} {emoji} with a score of {score:.2f}')
+        
+         # Create a string that explains the sentiment label and score
+            if sentiment == 'Neutral':
+                explanation = f"The sentiment of the input text is {sentiment}."
+            else:
+                explanation = f"The sentiment of the input text is {sentiment} with a confidence score of {score:.2f}. A score closer to 1 indicates a strong {sentiment.lower()} sentiment, while a score closer to 0.5 indicates a weaker sentiment."
+        
+        # Update the sentiment scores in the dataframe
+            if sentiment == 'Negative':
+                df.loc[df['sentiment'] == 'Negative', 'score'] += score
+            elif sentiment == 'Neutral':
+                df.loc[df['sentiment'] == 'Neutral', 'score'] += score
+            elif sentiment == 'Positive':
+                df.loc[df['sentiment'] == 'Positive', 'score'] += score
+
+
+        # Sentiment Analysis Visualization
+            df = pd.DataFrame({'sentiment': [sentiment], 'score': [score]})
+            chart = alt.Chart(df).mark_bar(width=30).encode(
+            x='sentiment',
+            y='score'
+            ).properties(
+            width=500
+            )
+            st.altair_chart(chart, use_container_width= True)
+
+# About section
+elif choice == 'About':
+    st.title('About')
+    # Add information about the model
+    st.write('The model used in this app is a pre-trained BERT model fine-tuned on the Stanford Sentiment Treebank (SST-2) dataset. The SST-2 dataset consists of movie reviews labeled as either positive or negative. The model has achieved state-of-the-art performance on the SST-2 dataset and is known for its speed and efficiency. However, like all machine learning models, it has its limitations and may not perform well on certain types of text or in certain contexts. It is important to keep this in mind when interpreting the results of this app.')
+    st.write('This WebApp was designed using Streamlit.')