Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the sentiment analysis pipeline
+sentiment_classifier = pipeline("text-classification", model="isom5240grp21/finetuned_model3")
+
+# Load the keyword extraction pipeline
+keyword_extractor = pipeline("text2text-generation", model="ilsilfverskiold/bart_keywords")
+
+def main():
+    st.title("Hotel Review Keywords Extractor")
+
+    # User input for the review
+    review = st.text_area("Enter your review:")
+
+    if st.button("Analyze"):
+        if review:
+            # Perform sentiment analysis
+            sentiment = sentiment_classifier(review)
+            sentiment_label = sentiment[0]['label']
+
+            # Perform keyword extraction
+            keywords = keyword_extractor(review)
+            generated_text = keywords[0]['generated_text']
+
+            # Display the result based on sentiment
+            if sentiment_label == 'LABEL_0':  # Assuming LABEL_0 indicates negative sentiment
+                st.write(f"The hotel did bad in: {generated_text}")
+            else:  # Assuming LABEL_1 indicates positive sentiment
+                st.write(f"The hotel did good in: {generated_text}")
+
+if __name__ == "__main__":
+    main()