Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py (FULL REPLACEMENT - session_state upload fix + grade>=C pass logic)
+# app.py (UPDATED FULL FILE - HF/Streamlit stable uploader + no xls NameError + Grade>=C pass)
 import streamlit as st
 import pandas as pd
 import numpy as np
@@ -14,21 +14,24 @@ st.caption(
 )
 
 # -----------------------------
-# Grade logic (FINAL as per user)
+# Grade logic (as specified)
 # -----------------------------
 def grade_pass_fail(g):
     if pd.isna(g):
         return "Unknown"
     g = str(g).strip().upper()
 
+    # Explicit FAIL
     if g.startswith(("D", "E", "F")):
         return "Fail"
 
+    # C- is FAIL, all other C variants are PASS
     if g.startswith("C"):
         if g == "C-" or g.startswith("C-"):
             return "Fail"
         return "Pass"
 
+    # A/B (with any +/-) are PASS
     if g.startswith(("A", "B")):
         return "Pass"
 
@@ -54,6 +57,7 @@ def coerce_numeric(df: pd.DataFrame, cols):
 
 
 def detect_student_rows(df: pd.DataFrame, grade_col: str) -> pd.DataFrame:
+    # keep rows that look like grade (A, B+, C-, etc.) OR have any numeric mark
     tmp_grade = df[grade_col].astype(str).str.strip()
     grade_like = tmp_grade.str.match(r"^[A-Fa-f][\+\-]?$", na=False)
 
@@ -87,6 +91,7 @@ def infer_component_cols(df: pd.DataFrame, grade_col: str, sno_col: str) -> list
     component_cols = [c for c in df.columns if c in common and c not in [grade_col, sno_col]]
 
     if not component_cols:
+        # fallback: numeric columns except grade/sno
         numeric_cols = []
         for c in df.columns:
             if c in [grade_col, sno_col]:
@@ -106,14 +111,20 @@ def infer_component_cols(df: pd.DataFrame, grade_col: str, sno_col: str) -> list
 
 def add_consistency(df: pd.DataFrame, component_cols: list[str]) -> pd.DataFrame:
     df = df.copy()
-    cols_for_sd = [c for c in component_cols if c.lower() != "total" and c in df.columns and pd.api.types.is_numeric_dtype(df[c])]
+    cols_for_sd = [
+        c for c in component_cols
+        if c.lower() != "total" and c in df.columns and pd.api.types.is_numeric_dtype(df[c])
+    ]
     df["Consistency_SD"] = df[cols_for_sd].std(axis=1, skipna=True) if len(cols_for_sd) >= 2 else np.nan
     return df
 
 
 def make_fail_reason_hints(df: pd.DataFrame, component_cols: list[str]) -> pd.DataFrame:
     df = df.copy()
-    comps = [c for c in component_cols if c.lower() != "total" and c in df.columns and pd.api.types.is_numeric_dtype(df[c])]
+    comps = [
+        c for c in component_cols
+        if c.lower() != "total" and c in df.columns and pd.api.types.is_numeric_dtype(df[c])
+    ]
 
     if not comps:
         df["FailReasonHint"] = np.where(df["PassFail"] == "Fail", "Grade below C.", "")
@@ -146,7 +157,7 @@ def make_fail_reason_hints(df: pd.DataFrame, component_cols: list[str]) -> pd.Da
 
 
 # -----------------------------
-# Upload (HF safe using session_state)
+# Upload (HF stable using session_state)
 # -----------------------------
 uploaded = st.file_uploader("Upload Excel (.xlsx)", type=["xlsx"], key="uploader")
 
@@ -154,32 +165,39 @@ if "file_bytes" not in st.session_state:
     st.session_state["file_bytes"] = None
 if "file_name" not in st.session_state:
     st.session_state["file_name"] = None
+if "sheet_names" not in st.session_state:
+    st.session_state["sheet_names"] = None
 
-# If new upload exists, store it in session_state (survives reruns)
+# Persist uploaded bytes across reruns
 if uploaded is not None:
     st.session_state["file_bytes"] = uploaded.getvalue()
     st.session_state["file_name"] = uploaded.name
+    st.session_state["sheet_names"] = None  # reset sheets on new upload
 
-# If still no bytes, stop safely
 if st.session_state["file_bytes"] is None:
     st.info("Upload an Excel file to begin.")
     st.stop()
 
-file_bytes = st.session_state["file_bytes"]
-bio = io.BytesIO(file_bytes)
-
-try:
-    xls = pd.ExcelFile(bio)
-except Exception as e:
-    st.error(f"Could not read Excel file. Error: {e}")
-    st.stop()
+# Load sheet names into session_state (prevents xls NameError on reruns)
+if st.session_state["sheet_names"] is None:
+    try:
+        bio = io.BytesIO(st.session_state["file_bytes"])
+        xls_local = pd.ExcelFile(bio)
+        st.session_state["sheet_names"] = xls_local.sheet_names
+    except Exception as e:
+        st.error(f"Could not read Excel file: {e}")
+        st.stop()
 
-sheet = st.selectbox("Select sheet", xls.sheet_names, index=0)
+sheet = st.selectbox("Select sheet", st.session_state["sheet_names"], index=0)
 
-bio.seek(0)
+# Read selected sheet
+bio = io.BytesIO(st.session_state["file_bytes"])
 raw = pd.read_excel(bio, sheet_name=sheet)
 raw = normalize_headers(raw)
 
+# -----------------------------
+# Build dataframe
+# -----------------------------
 grade_col_name = pick_grade_column(raw)
 
 df = detect_student_rows(raw, grade_col_name)
@@ -343,13 +361,23 @@ def student_drilldown_view(d: pd.DataFrame):
     with c3:
         st.metric("At Risk", "Yes" if row.get("Fail") else "No")
 
+    hint = row.get("FailReasonHint", "")
+    if hint:
+        st.write("**Reason hint:**", hint)
+
 
 def export_view(d: pd.DataFrame):
     st.subheader("Export for Power BI")
     clean_csv = d.to_csv(index=False).encode("utf-8")
-    st.download_button("⬇️ Download Cleaned Data (CSV)", clean_csv, file_name="cleaned_marks_with_passfail.csv", mime="text/csv")
+    st.download_button(
+        "⬇️ Download Cleaned Data (CSV)",
+        clean_csv,
+        file_name="cleaned_marks_with_passfail.csv",
+        mime="text/csv"
+    )
 
 
+# Render view
 if view == "Executive (Management)":
     executive_view(filtered)
 elif view == "Risk & Intervention":