Create 23_NLP_Transformer_Prompt3.py

pages/23_NLP_Transformer_Prompt3.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load pre-trained model and tokenizer
+model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+def analyze_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    return probs.detach().numpy()[0]
+
+st.title("Sentiment Analysis with Transformer")
+
+user_input = st.text_area("Enter text for sentiment analysis:", "I love this product!")
+
+if st.button("Analyze Sentiment"):
+    sentiment_scores = analyze_sentiment(user_input)
+    
+    st.write("Sentiment Scores:")
+    st.write(f"Negative: {sentiment_scores[0]:.4f}")
+    st.write(f"Positive: {sentiment_scores[1]:.4f}")
+    
+    # Create and display multiple graphs
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
+    
+    # Bar plot
+    ax1.bar(['Negative', 'Positive'], sentiment_scores)
+    ax1.set_ylabel('Score')
+    ax1.set_title('Sentiment Analysis Results (Bar Plot)')
+    
+    # Pie chart
+    ax2.pie(sentiment_scores, labels=['Negative', 'Positive'], autopct='%1.1f%%')
+    ax2.set_title('Sentiment Analysis Results (Pie Chart)')
+    
+    st.pyplot(fig)
+    
+    # Heatmap
+    fig, ax = plt.subplots(figsize=(8, 2))
+    sns.heatmap([sentiment_scores], annot=True, cmap="coolwarm", cbar=False, ax=ax)
+    ax.set_xticklabels(['Negative', 'Positive'])
+    ax.set_yticklabels(['Sentiment'])
+    ax.set_title('Sentiment Analysis Results (Heatmap)')
+    st.pyplot(fig)
+
+st.write("Note: This example uses a pre-trained model for English sentiment analysis.")