Update app.py

app.py

@@ -1,42 +1,52 @@
 import streamlit as st
-import pandas as pd
-import numpy as np
+from textblob import TextBlob
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+from flair.models import TextClassifier
+from flair.data import Sentence
 import matplotlib.pyplot as plt
-import seaborn as sns
-import transformers
-from transformers import pipeline
-
-# Set up sentiment analysis pipeline
-model_names = ['nlptown/bert-base-multilingual-uncased-sentiment', 
-               'cardiffnlp/twitter-roberta-base-sentiment', 
-               'distilbert-base-uncased-finetuned-sst-2-english',
-               'nlptown/roberta-base-sentiment-english']
-models = {}
-for model_name in model_names:
-    models[model_name] = pipeline('sentiment-analysis', model=model_name)
-
-# Set up Streamlit app
-st.title("Sentiment Analysis App")
-
-# User input
-text_input = st.text_input("Enter text to analyze:")
-
-if text_input:
-    # Analyze text with each model and store results in a DataFrame
-    results = []
-    for model_name, model in models.items():
-        result = model(text_input)[0]
-        results.append({'Model': model_name, 'Score': result['score'], 'Label': result['label']})
-    results_df = pd.DataFrame(results)
-
-    # Display results in a table
-    st.write("Results:")
-    st.table(results_df)
-
-    # Plot results in a bar chart
-    fig, ax = plt.subplots(figsize=(8, 6))
-    sns.barplot(x='Model', y='Score', hue='Label', data=results_df, ax=ax)
-    ax.set_title('Sentiment Analysis Results')
-    ax.set_ylabel('Score')
-    ax.set_xlabel('Model')
-    st.pyplot(fig)
+
+# Function to perform sentiment analysis using TextBlob model
+def textblob_sentiment(text):
+    blob = TextBlob(text)
+    return blob.sentiment.polarity
+
+# Function to perform sentiment analysis using VADER model
+def vader_sentiment(text):
+    analyzer = SentimentIntensityAnalyzer()
+    scores = analyzer.polarity_scores(text)
+    return scores['compound']
+
+# Function to perform sentiment analysis using Flair model
+def flair_sentiment(text):
+    classifier = TextClassifier.load('en-sentiment')
+    sentence = Sentence(text)
+    classifier.predict(sentence)
+    if sentence.labels[0].value == 'POSITIVE':
+        return 1.0
+    elif sentence.labels[0].value == 'NEGATIVE':
+        return -1.0
+    else:
+        return 0.0
+
+# Set up the Streamlit app
+st.title('Sentiment Analysis App')
+
+# Get user input
+text = st.text_input('Enter text to analyze')
+
+# Perform sentiment analysis using each model
+textblob_score = textblob_sentiment(text)
+vader_score = vader_sentiment(text)
+flair_score = flair_sentiment(text)
+
+# Display the sentiment scores
+st.write('TextBlob score:', textblob_score)
+st.write('VADER score:', vader_score)
+st.write('Flair score:', flair_score)
+
+# Create a graph of the sentiment scores
+fig, ax = plt.subplots()
+ax.bar(['TextBlob', 'VADER', 'Flair'], [textblob_score, vader_score, flair_score])
+ax.axhline(y=0, color='gray', linestyle='--')
+ax.set_title('Sentiment Scores')
+st.pyplot(fig)