Create app.py

app.py

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+import streamlit as st
+import altair as alt
+import torch
+from transformers import AlbertTokenizer, AlbertForSequenceClassification
+
+# Load pre-trained model and tokenizer
+model_name = "albert-base-v2"
+tokenizer = AlbertTokenizer.from_pretrained(model_name)
+model = AlbertForSequenceClassification.from_pretrained(model_name)
+
+# Define function to classify input text
+def classify_text(text):
+    inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits.detach().numpy()[0]
+    probabilities = torch.softmax(torch.tensor(logits), dim=0).tolist()
+    return probabilities
+
+# Set up Streamlit app
+st.title("ALBERT Text Classification App")
+
+# Create input box for user to enter text
+text_input = st.text_area("Enter text to classify", height=200)
+
+# Classify input text and display results
+if st.button("Classify"):
+    if text_input:
+        probabilities = classify_text(text_input)
+        df = pd.DataFrame({
+            'Label': ['Negative', 'Positive'],
+            'Probability': probabilities
+        })
+        chart = alt.Chart(df).mark_bar().encode(
+            x='Probability',
+            y=alt.Y('Label', sort=['Negative', 'Positive'])
+        )
+        st.write(chart)
+    else:
+        st.write("Please enter some text to classify.")