Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,132 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+from pathlib import Path
+
+# Try to import from your uploaded files.
+# If they are missing, we will show a helpful error in the app.
+try:
+    from demo import run_checks, format_console_output
+    from llm_utils import generate_summary
+except ImportError as e:
+    st.error(f"❌ Could not import modules: {e}")
+    st.info("Make sure you have uploaded 'demo.py' and 'llm_utils.py' to your Space!")
+    st.stop()
+
+# ---------------------------------------------------------------------------
+# Configuration & Setup
+# ---------------------------------------------------------------------------
+st.set_page_config(
+    page_title="Analytics Validation Demo",
+    page_icon="📊",
+    layout="wide"
+)
+
+# ---------------------------------------------------------------------------
+# Data Loading Helper
+# ---------------------------------------------------------------------------
+@st.cache_data
+def load_data_cached(filepath):
+    # We copy the load_data logic here or import it if compatible
+    # Importing is better to keep logic in one place
+    from demo import load_data
+    return load_data(filepath)
+
+# ---------------------------------------------------------------------------
+# UI Helpers
+# ---------------------------------------------------------------------------
+def display_issues(issues):
+    """Render list of issues as Streamlit alerts."""
+    if not issues:
+        st.success("✅ No issues detected. Data appears clean.")
+        return
+
+    st.warning(f"⚠️ Found {len(issues)} issues")
+    
+    for issue in issues:
+        severity_icon = "🔴" if issue["severity"] == "ERROR" else "⚠️"
+        with st.expander(f"{severity_icon} [{issue['severity']}] {issue['type']} in {issue.get('column', 'General')}"):
+            st.write(f"**Detail:** {issue['detail']}")
+            if issue.get("dates"):
+                st.write(f"**Affected Dates:** {', '.join(issue['dates'])}")
+
+def display_metrics(stats):
+    """Render Key Metrics in columns."""
+    st.subheader("Key Metrics")
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        rev = stats["revenue"]
+        st.metric("Total Revenue", f"${rev['total']:,.2f}")
+        st.caption(f"Mean: ${rev['mean']:,.2f} | Missing: {rev['missing_count']}")
+
+    with col2:
+        ord_ = stats["orders"]
+        st.metric("Total Orders", f"{int(ord_['total']):,}")
+        st.caption(f"Mean: {ord_['mean']:,.0f} | Missing: {ord_['missing_count']}")
+    
+    st.divider()
+
+# ---------------------------------------------------------------------------
+# Main App Layout
+# ---------------------------------------------------------------------------
+def main():
+    st.title("📊 Analytics Validation Engine")
+    st.markdown("""
+    This demo validates daily business metrics for anomalies, missing data, and consistency errors.
+    It uses a deterministic rule engine and an optional local LLM for narration.
+    """)
+
+    # Sidebar parameters
+    st.sidebar.header("Configuration")
+    uploaded_file = st.sidebar.file_uploader("Upload CSV", type=["csv"])
+    
+    # Check for default data if no upload
+    data_path = "data.csv"
+    if uploaded_file is not None:
+        data_path = uploaded_file
+    elif not Path(data_path).exists():
+        st.error("❌ `data.csv` not found and no file was uploaded.")
+        st.info("Please upload a CSV file in the sidebar or add `data.csv` to your Space files.")
+        st.stop()
+
+    # Load Data
+    try:
+        if uploaded_file:
+            df = pd.read_csv(uploaded_file, parse_dates=["date"])
+            if not pd.api.types.is_datetime64_any_dtype(df["date"]):
+                 df["date"] = pd.to_datetime(df["date"])
+            df = df.sort_values("date").reset_index(drop=True)
+        else:
+            df = load_data_cached(data_path)
+            
+        st.sidebar.success(f"Loaded {len(df)} rows")
+        
+    except Exception as e:
+        st.error(f"Error loading data: {e}")
+        st.stop()
+
+    # Run Analysis
+    with st.spinner("Running validation rules..."):
+        results = run_checks(df)
+    
+    # Display Findings
+    
+    # 1. Executive Summary
+    st.header("Executive Summary")
+    with st.spinner("Generating summary..."):
+        summary = generate_summary(results)
+    st.info(summary)
+    
+    # 2. Detailed Issues
+    st.header("Detected Issues")
+    display_issues(results["issues"])
+    
+    # 3. Data Overview
+    st.header("Data Overview")
+    display_metrics(results["stats"])
+    
+    with st.expander("View Raw Data"):
+        st.dataframe(df)
 
-"""
-# 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 __name__ == "__main__":
+    main()