Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+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)