Add streamlit app

app.py

@@ -0,0 +1,44 @@
+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load the pretrained model and tokenizer
+model_name = "distilbert-base-uncased-finetuned-sst-2-english"  # Example model
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Set up the device (GPU or CPU)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Function to perform sentiment analysis
+def perform_sentiment_analysis(text):
+    inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
+    inputs = inputs.to(device)
+    outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.softmax(logits, dim=1).detach().cpu().numpy()[0]
+    sentiment_label = "Positive" if probabilities[1] > probabilities[0] else "Negative"
+    return sentiment_label, probabilities
+
+# Streamlit app
+def main():
+    st.title("Sentiment Analysis App")
+    st.write("Enter a text and select a pretrained model to perform sentiment analysis.")
+
+    text = st.text_area("Enter text", value="")
+
+    model_options = {
+        "distilbert-base-uncased-finetuned-sst-2-english": "DistilBERT (SST-2)",
+        # Add more models here if desired
+    }
+
+    selected_model = st.selectbox("Select a pretrained model", list(model_options.keys()))
+
+    if st.button("Analyze"):
+        sentiment_label, probabilities = perform_sentiment_analysis(text)
+        st.write(f"Sentiment: {sentiment_label}")
+        st.write(f"Positive probability: {probabilities[1]}")
+        st.write(f"Negative probability: {probabilities[0]}")
+
+if __name__ == "__main__":
+    main()