Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from keybert import KeyBERT
+
+# Initialize sentiment analysis pipeline
+@st.cache_resource
+def load_sentiment_pipeline():
+    model_name = "mayf/amazon_reviews_bert_ft"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    return pipeline(
+        "sentiment-analysis",
+        model=model,
+        tokenizer=tokenizer,
+        return_all_scores=True
+    )
+
+# Initialize KeyBERT model
+@st.cache_resource
+def load_keybert_model():
+    return KeyBERT(model="all-MiniLM-L6-v2")
+
+sentiment_pipeline = load_sentiment_pipeline()
+kw_model = load_keybert_model()
+
+# Streamlit App
+st.set_page_config(page_title="Review Analyzer", layout="wide")
+st.title("📊 Review Sentiment & Keyword Analyzer")
+
+review = st.text_area("Enter your review:")
+
+if st.button("Analyze Review"):
+    if not review:
+        st.warning("Please enter a review to analyze.")
+    else:
+        # Sentiment Analysis
+        scores = sentiment_pipeline(review)[0]
+        # Convert scores to displayable format
+        sentiment_results = {item['label']: round(item['score'], 4) for item in scores}
+        st.subheader("Sentiment Scores")
+        st.json(sentiment_results)
+
+        # Keyword Extraction
+        keywords = kw_model.extract_keywords(
+            review,
+            keyphrase_ngram_range=(1, 2),
+            stop_words="english",
+            top_n=5
+        )
+        st.subheader("Top Keywords")
+        for kw, score in keywords:
+            st.write(f"- **{kw}** (Score: {score:.4f})")
+
+        # Final Score: example combining sentiment and keywords
+        avg_sentiment = sum(sentiment_results.values()) / len(sentiment_results)
+        st.subheader("Composite Score with Keywords")
+        st.write(f"Average Sentiment Score: {avg_sentiment:.4f}")
+        st.write("Keywords used for this score:")
+        st.write(', '.join([kw for kw, _ in keywords]))