Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,68 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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)
+DATA_PATH = Path(__file__).resolve().parent.parent / "reviews.csv"
+df = pd.read_csv(DATA_PATH)
+
+# 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
+
+col1, col2, col3 = st.columns(3)
+col1.metric("Total Reviews", total_reviews)
+col2.metric("Positive %", f"{positive_rate:.1f}%")
+col3.metric("Negative %", f"{negative_rate:.1f}%")
+
+# Sentiment chart
+st.subheader("Sentiment Breakdown")
+st.bar_chart(df["sentiment"].value_counts())
+
+# Sidebar filters
+st.sidebar.header("Filters")
+selected_sentiment = st.sidebar.multiselect(
+    "Select sentiment",
+    options=df["sentiment"].unique(),
+    default=df["sentiment"].unique()
+)
+
+filtered_df = df[df["sentiment"].isin(selected_sentiment)]
+
+# Show reviews
+st.subheader("Filtered Reviews")
+st.dataframe(filtered_df[["review_text", "sentiment"]])
+
+# Insights
+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]
+    st.write("Example negative review:")
+    st.warning(top_issue)
+    st.info("Recommendation: Investigate common complaints and improve service quality.")
+else:
+    st.write("No major negative issues found.")
+
+# Assistant
+st.subheader("Ask the Assistant")
+
+question = st.text_input("Ask a question about the reviews")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if question:
+    if "positive" in question.lower():
+        st.write("Positive reviews indicate customer satisfaction.")
+    elif "negative" in question.lower():
+        st.write("Negative reviews indicate dissatisfaction and areas for improvement.")
+    else:
+        st.write("This dataset contains restaurant reviews labeled as positive or negative.")