Update src/streamlit_app.py

src/streamlit_app.py

@@ -4,18 +4,38 @@ from pathlib import Path
 
 st.set_page_config(page_title="Customer Experience Analyzer", layout="wide")
 
-st.title("Customer Experience Analyzer")
-st.write("Analyze customer sentiment from restaurant reviews.")
-
-# Load the CSV from the same folder as this file
+# Load data
 DATA_PATH = Path(__file__).parent / "reviews.csv"
 df = pd.read_csv(DATA_PATH)
 
+# Title
+st.title("Customer Experience Analyzer")
+st.write("Analyze customer sentiment from restaurant reviews and identify where customer experience can improve.")
+
+# Sidebar
+st.sidebar.header("Filters")
+
+selected_sentiment = st.sidebar.multiselect(
+    "Select sentiment",
+    options=df["sentiment"].unique(),
+    default=df["sentiment"].unique()
+)
+
+search_term = st.sidebar.text_input("Search reviews by keyword")
+
+filtered_df = df[df["sentiment"].isin(selected_sentiment)]
+
+if search_term:
+    filtered_df = filtered_df[
+        filtered_df["review_text"].str.contains(search_term, case=False, na=False)
+    ]
+
 # KPIs
-total_reviews = len(df)
-positive_rate = (df["sentiment"] == "positive").mean() * 100 if total_reviews > 0 else 0
-negative_rate = (df["sentiment"] == "negative").mean() * 100 if total_reviews > 0 else 0
+total_reviews = len(filtered_df)
+positive_rate = (filtered_df["sentiment"] == "positive").mean() * 100 if total_reviews > 0 else 0
+negative_rate = (filtered_df["sentiment"] == "negative").mean() * 100 if total_reviews > 0 else 0
 
+st.subheader("Overview")
 col1, col2, col3 = st.columns(3)
 col1.metric("Total Reviews", total_reviews)
 col2.metric("Positive %", f"{positive_rate:.1f}%")
@@ -23,43 +43,90 @@ col3.metric("Negative %", f"{negative_rate:.1f}%")
 
 # Chart
 st.subheader("Sentiment Breakdown")
-st.bar_chart(df["sentiment"].value_counts())
+if total_reviews > 0:
+    st.bar_chart(filtered_df["sentiment"].value_counts())
+else:
+    st.warning("No reviews match the selected filters.")
 
-# Sidebar filter
-st.sidebar.header("Filters")
-selected_sentiment = st.sidebar.multiselect(
-    "Select sentiment",
-    options=df["sentiment"].unique(),
-    default=df["sentiment"].unique()
-)
+# Example reviews
+st.subheader("Example Customer Reviews")
 
-filtered_df = df[df["sentiment"].isin(selected_sentiment)]
+col4, col5 = st.columns(2)
 
-# Reviews
-st.subheader("Filtered Reviews")
-st.dataframe(filtered_df[["review_text", "sentiment"]])
+positive_examples = filtered_df[filtered_df["sentiment"] == "positive"]
+negative_examples = filtered_df[filtered_df["sentiment"] == "negative"]
 
-# Insight
+with col4:
+    st.markdown("### Positive Review Example")
+    if len(positive_examples) > 0:
+        st.success(positive_examples.iloc[0]["review_text"])
+    else:
+        st.info("No positive review found for this selection.")
+
+with col5:
+    st.markdown("### Negative Review Example")
+    if len(negative_examples) > 0:
+        st.error(negative_examples.iloc[0]["review_text"])
+    else:
+        st.info("No negative review found for this selection.")
+
+# Key insights
 st.subheader("Key Insights")
-negative_df = filtered_df[filtered_df["sentiment"] == "negative"]
 
-if len(negative_df) > 0:
-    example_negative = negative_df["review_text"].iloc[0]
-    st.write("Example negative review:")
-    st.warning(example_negative)
-    st.info("Recommendation: investigate common complaints and improve service quality.")
+if total_reviews > 0:
+    if positive_rate > negative_rate:
+        st.write("Customer sentiment is mostly positive in the selected reviews.")
+    elif negative_rate > positive_rate:
+        st.write("Customer sentiment is mostly negative in the selected reviews.")
+    else:
+        st.write("Customer sentiment is evenly split between positive and negative.")
+
+    st.write(
+        f"""
+        - Out of **{total_reviews}** filtered reviews, **{positive_rate:.1f}%** are positive and **{negative_rate:.1f}%** are negative.
+        - This helps management quickly assess overall customer satisfaction.
+        - Searching by keyword can help identify specific issues such as service, food, or staff.
+        """
+    )
+else:
+    st.write("No insights available because no reviews match the selected filters.")
+
+# Recommendations
+st.subheader("Manager Recommendations")
+
+if total_reviews > 0:
+    if negative_rate > 60:
+        st.warning("Customer dissatisfaction is high. Management should urgently review repeated complaints and investigate operational issues.")
+    elif negative_rate > 40:
+        st.info("Customer sentiment is mixed. Management should identify the most common negative themes and improve consistency.")
+    else:
+        st.success("Customer sentiment is mostly positive. Management should preserve strengths and monitor new complaints.")
 else:
-    st.write("No major negative issues found in the current selection.")
+    st.write("No recommendation available.")
 
-# Simple assistant
+# Reviews table
+st.subheader("Filtered Reviews Table")
+if total_reviews > 0:
+    st.dataframe(
+        filtered_df[["review_text", "sentiment"]].reset_index(drop=True),
+        use_container_width=True
+    )
+else:
+    st.write("No reviews to display.")
+
+# Assistant
 st.subheader("Ask the Assistant")
+
 question = st.text_input("Ask a question about the reviews")
 
 if question:
     q = question.lower()
+
     if "positive" in q:
-        st.write("Positive reviews indicate customer satisfaction.")
+        st.write("Positive reviews suggest that customers were satisfied with their restaurant experience.")
     elif "negative" in q:
-        st.write("Negative reviews indicate dissatisfaction and areas for improvement.")
+        st.write("Negative reviews suggest that customers experienced problems that may affect satisfaction and loyalty.")
+    elif "improve" in q or "improvement" in q:
+        st.write("Management should focus on recurring negative feedback and investigate the causes behind poor customer experiences.")
     else:
-        st.write("This dataset contains restaurant reviews labeled as positive or negative.")
+        st.write("This dashboard helps management understand customer sentiment, review examples, and potential improvement areas.")