Update src/streamlit_app.py

src/streamlit_app.py

@@ -4,38 +4,18 @@ from pathlib import Path
 
 st.set_page_config(page_title="Customer Experience Analyzer", layout="wide")
 
-# 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")
+st.write("Analyze customer sentiment from restaurant reviews.")
 
-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)
-    ]
+# Load the CSV from the same folder as this file
+DATA_PATH = Path(__file__).parent / "reviews.csv"
+df = pd.read_csv(DATA_PATH)
 
 # KPIs
-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
+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
 
-st.subheader("Overview")
 col1, col2, col3 = st.columns(3)
 col1.metric("Total Reviews", total_reviews)
 col2.metric("Positive %", f"{positive_rate:.1f}%")
@@ -43,90 +23,43 @@ col3.metric("Negative %", f"{negative_rate:.1f}%")
 
 # Chart
 st.subheader("Sentiment Breakdown")
-if total_reviews > 0:
-    st.bar_chart(filtered_df["sentiment"].value_counts())
-else:
-    st.warning("No reviews match the selected filters.")
-
-# Example reviews
-st.subheader("Example Customer Reviews")
-
-col4, col5 = st.columns(2)
+st.bar_chart(df["sentiment"].value_counts())
 
-positive_examples = filtered_df[filtered_df["sentiment"] == "positive"]
-negative_examples = filtered_df[filtered_df["sentiment"] == "negative"]
+# Sidebar filter
+st.sidebar.header("Filters")
+selected_sentiment = st.sidebar.multiselect(
+    "Select sentiment",
+    options=df["sentiment"].unique(),
+    default=df["sentiment"].unique()
+)
 
-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.")
+filtered_df = df[df["sentiment"].isin(selected_sentiment)]
 
-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.")
+# Reviews
+st.subheader("Filtered Reviews")
+st.dataframe(filtered_df[["review_text", "sentiment"]])
 
-# Key insights
+# Insight
 st.subheader("Key Insights")
+negative_df = filtered_df[filtered_df["sentiment"] == "negative"]
 
-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.")
+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.")
 else:
-    st.write("No recommendation available.")
+    st.write("No major negative issues found in the current selection.")
 
-# 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
+# Simple 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 suggest that customers were satisfied with their restaurant experience.")
+        st.write("Positive reviews indicate customer satisfaction.")
     elif "negative" in q:
-        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.")
+        st.write("Negative reviews indicate dissatisfaction and areas for improvement.")
     else:
-        st.write("This dashboard helps management understand customer sentiment, review examples, and potential improvement areas.")
+        st.write("This dataset contains restaurant reviews labeled as positive or negative.")