Update app.py

app.py

@@ -1,7 +1,13 @@
 # app.py  (Fast-build Hugging Face Gradio)
 # School Mark Analysis: RegNo, Name, Tamil, English, Maths, Science, Social
-# Features: Total, Average, Rank, Remark, subject averages, fail-count (1..5), top-3 overall, top-3 per subject,
-# search by RegNo, download result CSV
+# Features:
+# - Total, Average, Rank, Remark
+# - Subject-wise average + bar chart
+# - Fail-count distribution (failed in 1..5 subjects)
+# - Top-3 overall, Top-3 in each subject
+# - Search by RegNo
+# - Download final result CSV
+# - Logo display (reliable on Hugging Face) using gr.Image + Pillow
 
 import os
 import tempfile
@@ -9,43 +15,16 @@ import numpy as np
 import pandas as pd
 import gradio as gr
 import matplotlib.pyplot as plt
-import os
-import gradio as gr
-
-def logo_html():
-    # Put the logo in the SAME folder as app.py
-    candidates = [
-        "logo.png", "logo.jpg", "logo.jpeg",
-        "Logo.png", "Logo.jpg", "Logo.jpeg"
-    ]
-    for fn in candidates:
-        if os.path.exists(fn):
-            return f"""
-            <div style="text-align:center; margin:6px 0 10px 0;">
-              <img src="file={fn}" style="width:130px; display:block; margin:0 auto;" />
-              <div style="font-size:30px; font-weight:800; color:#1E5AA8;">Amrita Manthana</div>
-              <div style="font-size:18px; font-weight:800; color:#1E5AA8;">Prof.B.Santhi,SRC,SASTRA</div>
-            </div>
-            """
-    # fallback (no image found)
-    return """
-    <div style="text-align:center; margin:6px 0 10px 0;">
-      <div style="font-size:30px; font-weight:800; color:#1E5AA8;">Amrita Manthana</div>
-      <div style="font-size:18px; font-weight:800; color:#1E5AA8;">Prof.B.Santhi,SRC,SASTRA</div>
-      <div style="font-size:12px; color:#777;">(logo file not found in repo root)</div>
-    </div>
-    """
-
-# Use this inside your Blocks:
-# with gr.Blocks(...) as demo:
-#     gr.HTML(logo_html())
-
+from PIL import Image
 
 SUBJECTS_DEFAULT = ["Tamil", "English", "Maths", "Science", "Social"]
 ID_COL_DEFAULT = "RegNo"
 NAME_COL_DEFAULT = "Name"
 
 
+# -----------------------------
+# Utilities
+# -----------------------------
 def _clean_columns(df: pd.DataFrame) -> pd.DataFrame:
     df = df.copy()
     df.columns = [c.strip() for c in df.columns]
@@ -73,7 +52,6 @@ def _validate_and_prepare(df: pd.DataFrame, id_col: str, name_col: str, subjects
 def _remark(avg: float, failed_subjects: int) -> str:
     if failed_subjects > 0:
         return "Fail"
-    # Only pass students reach here
     if avg >= 80:
         return "Distinction"
     if 60 <= avg <= 79:
@@ -83,8 +61,16 @@ def _remark(avg: float, failed_subjects: int) -> str:
     return "Pass"
 
 
-def compute_marks(df: pd.DataFrame, pass_mark: int = 35, id_col: str = ID_COL_DEFAULT,
-                  name_col: str = NAME_COL_DEFAULT, subjects: list[str] = SUBJECTS_DEFAULT):
+# -----------------------------
+# Core computations
+# -----------------------------
+def compute_marks(
+    df: pd.DataFrame,
+    pass_mark: int = 35,
+    id_col: str = ID_COL_DEFAULT,
+    name_col: str = NAME_COL_DEFAULT,
+    subjects: list[str] = SUBJECTS_DEFAULT,
+):
     df = _validate_and_prepare(df, id_col, name_col, subjects)
 
     out = df.copy()
@@ -140,6 +126,9 @@ def compute_marks(df: pd.DataFrame, pass_mark: int = 35, id_col: str = ID_COL_DE
     return out, subj_avg, fail_dist, top3_overall, top3_each_subject, summary
 
 
+# -----------------------------
+# Plots (close figs to avoid memory growth)
+# -----------------------------
 def plot_subject_avg(subj_avg: pd.DataFrame):
     fig, ax = plt.subplots(figsize=(7, 4))
     ax.bar(subj_avg["Subject"], subj_avg["Class_Average"])
@@ -149,6 +138,7 @@ def plot_subject_avg(subj_avg: pd.DataFrame):
     ax.set_ylim(0, 100)
     plt.xticks(rotation=25, ha="right")
     plt.tight_layout()
+    plt.close(fig)
     return fig
 
 
@@ -165,9 +155,13 @@ def plot_remark_distribution(result_df: pd.DataFrame):
     ax.set_ylabel("Number of Students")
     plt.xticks(rotation=20, ha="right")
     plt.tight_layout()
+    plt.close(fig)
     return fig
 
 
+# -----------------------------
+# Gradio handlers
+# -----------------------------
 def load_csv(file_obj):
     if file_obj is None:
         return None, "Please upload a CSV.", None
@@ -207,9 +201,7 @@ def search_regno(result_df, regno_value):
     if not regno_value:
         return "Enter RegNo to search.", pd.DataFrame()
 
-    # Exact match (string or numeric)
     col = result_df[ID_COL_DEFAULT]
-    res = None
     if pd.api.types.is_numeric_dtype(col):
         try:
             q = float(regno_value)
@@ -219,41 +211,48 @@ def search_regno(result_df, regno_value):
     else:
         res = result_df[col.astype(str) == str(regno_value)]
 
-    if res is None or res.empty:
+    if res.empty:
         return "No matching record found.", pd.DataFrame()
     return f"Found {len(res)} record(s).", res
 
 
+# -----------------------------
+# Logo loader (reliable in HF)
+# -----------------------------
+def load_logo():
+    for fn in ["logo.jpg", "logo.png", "logo.jpeg", "Logo.jpg", "Logo.png", "Logo.jpeg"]:
+        if os.path.exists(fn):
+            try:
+                return Image.open(fn)
+            except Exception:
+                return None
+    return None
+
+
+# -----------------------------
+# UI
+# -----------------------------
 CSS = """
 #titleblock {text-align:center; margin-top: 6px; margin-bottom: 8px;}
 #t1 {font-size:30px; font-weight:800; color:#1E5AA8;}
 #t2 {font-size:18px; font-weight:800; color:#1E5AA8;}
 """
 
-def header_html():
-    if os.path.exists("logo.jpg"):
-        return """
-        <div id="titleblock">
-          <img src="file=logo.jpg" style="width:110px; display:block; margin:0 auto;" />
-          <div id="t1">Amrita Manthana</div>
-          <div id="t2">Prof.B.Santhi,SRC,SASTRA</div>
-        </div>
-        """
-    return """
-    <div id="titleblock">
+with gr.Blocks(css=CSS, title="School Mark Analysis") as demo:
+    # Logo + title
+    logo = load_logo()
+    if logo is not None:
+        gr.Image(value=logo, show_label=False, interactive=False, height=160)
+
+    gr.HTML("""
+    <div id="titleblock" style="margin-top:-10px;">
       <div id="t1">Amrita Manthana</div>
       <div id="t2">Prof.B.Santhi,SRC,SASTRA</div>
     </div>
-    """
-
-    
-
-
-with gr.Blocks(css=CSS, title="School Mark Analysis") as demo:
-    gr.HTML(header_html())
+    """)
 
     df_state = gr.State(None)
-    result_state = gr.State(None)  # stores result_df for search
+    result_state = gr.State(None)
 
     with gr.Row():
         with gr.Column(scale=1, min_width=340):
@@ -273,12 +272,20 @@ with gr.Blocks(css=CSS, title="School Mark Analysis") as demo:
         with gr.Column(scale=2):
             with gr.Tabs():
                 with gr.Tab("Result Table"):
-                    result_table = gr.Dataframe(label="Result (Total, Average, Rank, Remark, Failed_Subjects)", interactive=False, wrap=True)
+                    result_table = gr.Dataframe(
+                        label="Result (Total, Average, Rank, Remark, Failed_Subjects)",
+                        interactive=False,
+                        wrap=True
+                    )
                 with gr.Tab("Subject Averages"):
                     subj_avg_table = gr.Dataframe(label="Subject-wise Averages", interactive=False, wrap=True)
                     avg_plot = gr.Plot(label="Bar Chart: Subject-wise Average")
                 with gr.Tab("Fail Counts"):
-                    fail_dist_table = gr.Dataframe(label="Students failed in 1/2/3/4/5 subjects", interactive=False, wrap=True)
+                    fail_dist_table = gr.Dataframe(
+                        label="Students failed in 1/2/3/4/5 subjects",
+                        interactive=False,
+                        wrap=True
+                    )
                 with gr.Tab("Toppers"):
                     top3_overall_table = gr.Dataframe(label="Overall Top 3", interactive=False, wrap=True)
                     top3_each_subject_table = gr.Dataframe(label="Top 3 in each subject", interactive=False, wrap=True)
@@ -304,8 +311,10 @@ with gr.Blocks(css=CSS, title="School Mark Analysis") as demo:
     run_btn.click(
         run_and_store,
         inputs=[df_state, pass_mark],
-        outputs=[summary, result_table, subj_avg_table, fail_dist_table, top3_overall_table, top3_each_subject_table,
-                 avg_plot, remark_plot, download_file, result_state]
+        outputs=[
+            summary, result_table, subj_avg_table, fail_dist_table, top3_overall_table, top3_each_subject_table,
+            avg_plot, remark_plot, download_file, result_state
+        ]
     )
 
     search_btn.click(