Create nlpquiz.py

nlpquiz.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the pre-trained model and tokenizer
+model_name = "bert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Define the prediction function
+def classify_text(text):
+    # Tokenize the input text
+    encoded_text = tokenizer(text, truncation=True, padding=True, return_tensors="pt")
+
+    # Make predictions with the model
+    predictions = model(**encoded_text)
+    pred_labels = predictions.logits.argmax(-1).cpu().numpy()
+
+    # Get the predicted labels and their corresponding probabilities
+    labels = tokenizer.convert_ids_to_labels(pred_labels)
+    probs = predictions.logits.softmax(-1).cpu().numpy()[:, 1]
+
+    return labels, probs
+
+# Create the Streamlit app
+st.title("Text Classification App")
+
+# Input field for user text
+user_text = st.text_input("Enter text to classify:")
+
+# Predict the classification labels and probabilities
+if user_text:
+    labels, probs = classify_text(user_text)
+
+    # Display the classification results
+    st.header("Classification Results:")
+
+    for label, prob in zip(labels, probs):
+        st.write(f"Label: {label} (Probability: {prob:.3f})")