Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,15 +2,14 @@ import streamlit as st
 import pandas as pd
 from pathlib import Path
 
-# Page config
 st.set_page_config(page_title="Customer Experience Analyzer", layout="wide")
 
-# Title
 st.title("Customer Experience Analyzer")
 st.write("Analyze customer sentiment from restaurant reviews.")
 
-# Load data (IMPORTANT: correct path for Hugging Face)
-df = pd.read_csv("reviews.csv")
+# 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(df)
@@ -22,11 +21,11 @@ col1.metric("Total Reviews", total_reviews)
 col2.metric("Positive %", f"{positive_rate:.1f}%")
 col3.metric("Negative %", f"{negative_rate:.1f}%")
 
-# Sentiment chart
+# Chart
 st.subheader("Sentiment Breakdown")
 st.bar_chart(df["sentiment"].value_counts())
 
-# Sidebar filters
+# Sidebar filter
 st.sidebar.header("Filters")
 selected_sentiment = st.sidebar.multiselect(
     "Select sentiment",
@@ -36,32 +35,31 @@ selected_sentiment = st.sidebar.multiselect(
 
 filtered_df = df[df["sentiment"].isin(selected_sentiment)]
 
-# Show reviews
+# Reviews
 st.subheader("Filtered Reviews")
 st.dataframe(filtered_df[["review_text", "sentiment"]])
 
-# Insights
+# Insight
 st.subheader("Key Insights")
-
 negative_df = filtered_df[filtered_df["sentiment"] == "negative"]
 
 if len(negative_df) > 0:
-    top_issue = negative_df["review_text"].iloc[0]
+    example_negative = negative_df["review_text"].iloc[0]
     st.write("Example negative review:")
-    st.warning(top_issue)
-    st.info("Recommendation: Investigate common complaints and improve service quality.")
+    st.warning(example_negative)
+    st.info("Recommendation: investigate common complaints and improve service quality.")
 else:
-    st.write("No major negative issues found.")
+    st.write("No major negative issues found in the current selection.")
 
-# Assistant
+# Simple assistant
 st.subheader("Ask the Assistant")
-
 question = st.text_input("Ask a question about the reviews")
 
 if question:
-    if "positive" in question.lower():
+    q = question.lower()
+    if "positive" in q:
         st.write("Positive reviews indicate customer satisfaction.")
-    elif "negative" in question.lower():
+    elif "negative" in q:
         st.write("Negative reviews indicate dissatisfaction and areas for improvement.")
     else:
         st.write("This dataset contains restaurant reviews labeled as positive or negative.")