Create app.py

app.py

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+import streamlit as st
+import transformers
+import torch
+
+# Define sentiment analysis models
+models = {
+    "DistilBERT": transformers.pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english"),
+    "BERT": transformers.pipeline("sentiment-analysis", model="bert-base-uncased-finetuned-sst-2-english"),
+    "RoBERTa": transformers.pipeline("sentiment-analysis", model="roberta-base-openai-detector"),
+}
+
+# Define function to analyze sentiment using selected model
+def analyze_sentiment(text, model_name):
+    model = models[model_name]
+    result = model(text)[0]
+    return result['label'], result['score']
+
+# Define Streamlit app
+def app():
+    st.title("Sentiment Analysis App")
+
+    # User input
+    text = st.text_area("Enter text to analyze", max_chars=1024)
+
+    # Sentiment analysis
+    if st.button("Analyze"):
+        st.write("Analyzing sentiment...")
+        with st.spinner("Wait for it..."):
+            results = []
+            for model_name in models:
+                label, score = analyze_sentiment(text, model_name)
+                results.append((model_name, label, score))
+        st.success("Sentiment analysis complete!")
+        st.write("Results:")
+        for model_name, label, score in results:
+            st.write(f"- {model_name}: {label} ({score:.2f})")
+
+    # Advanced features
+    if st.beta_expander("Advanced Options", expanded=False):
+        model_name = st.selectbox("Select model", list(models.keys()))
+        if st.button("Test model"):
+            label, score = analyze_sentiment("This is a test.", model_name)
+            st.write(f"Test result: {label} ({score:.2f})")
+
+# Run Streamlit app
+if __name__ == "__main__":
+    app()