Create app.py

app.py

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+import streamlit as st
+import transformers
+import torch
+
+st.title("Sentiment Analysis App")
+
+# Set default text
+default_text = "Cody Jiang is a fantastic student in CS-UY-4613!"
+
+# Select pretrained model
+model_names = ['distilbert-base-uncased-finetuned-sst-2-english', 'bert-base-uncased', 'roberta-base', ]
+model_name = st.selectbox("Select a pretrained model", model_names)
+
+# Load selected model
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Define sentiment labels
+sentiment_labels = ['Negative', 'Positive']
+
+# Analyze sentiment
+if st.button("Submit"):
+    with st.spinner("Analyzing..."):
+        text = st.text_input("Enter text to analyze", default_text)
+        inputs = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
+        inputs = inputs.to(device)
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predictions = torch.argmax(logits, dim=1).cpu().numpy()
+        sentiment = sentiment_labels[predictions[0]]
+
+    st.success(f"Sentiment: {sentiment}")