Create app.py

app.py

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+import streamlit as st
+import transformers
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import numpy as np
+
+# Load the pre-trained text classification model from Hugging Face
+model_name = "bert-base-uncased"
+num_labels = 2
+
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=num_labels)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def classify_text(text):
+    # Preprocess the text input
+    encoded_text = tokenizer(text, truncation=True, padding=True, return_tensors="pt")
+
+    # Make predictions using the pre-trained model
+    with torch.no_grad():
+        outputs = model(**encoded_text)
+        logits = outputs.logits
+        predictions = np.argmax(logits, axis=1)
+
+    # Convert predictions to class labels
+    class_labels = ["positive", "negative"]
+    predicted_labels = [class_labels[i] for i in predictions]
+
+    # Return the predicted labels
+    return predicted_labels
+
+# Initialize the Streamlit app
+st.title("Text Classification Demo")
+
+# Create the text input field
+input_text = st.text_input("Enter text to classify:", "")
+
+# Make predictions and display the results
+if input_text:
+    predicted_labels = classify_text(input_text)
+    st.write("Predicted labels:", predicted_labels)