Create NLP_Transformer_Prompt_2.py

pages/NLP_Transformer_Prompt_2.py

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+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+from transformers import pipeline
+import matplotlib.pyplot as plt
+import streamlit as st
+
+# Load pre-trained model and tokenizer
+model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
+model = BertForSequenceClassification.from_pretrained(model_name)
+tokenizer = BertTokenizer.from_pretrained(model_name)
+
+# Function to classify text
+def classify_text(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**inputs)
+    scores = torch.nn.functional.softmax(outputs.logits, dim=1)
+    return scores
+
+# Streamlit interface
+st.title("NLP Transformer with PyTorch and Hugging Face")
+st.header("Sentiment Analysis")
+
+text = st.text_area("Enter text for sentiment analysis:")
+
+if st.button("Classify"):
+    scores = classify_text(text).detach().numpy()[0]
+    labels = ["1 star", "2 stars", "3 stars", "4 stars", "5 stars"]
+    
+    st.write("Classification Scores:")
+    for label, score in zip(labels, scores):
+        st.write(f"{label}: {score:.4f}")
+
+    fig, ax = plt.subplots()
+    ax.bar(labels, scores, color='blue')
+    ax.set_xlabel('Sentiment')
+    ax.set_ylabel('Score')
+    ax.set_title('Sentiment Analysis Scores')
+    st.pyplot(fig)