Update src/main.py

src/main.py

@@ -4,14 +4,7 @@ import sqlite3
 import hashlib
 from datetime import datetime
 from localization_eval import evaluate_submission
-import logging
-
-logging.basicConfig(level=logging.INFO) # Configure the root logger
-
-logging.basicConfig(
-    format='%(asctime)s - %(levelname)s - %(message)s',
-    level=logging.INFO
-)
+from PIL import Image
 
 # --- CONFIGURATION & SETUP ---
 st.set_page_config(
@@ -24,6 +17,15 @@ st.set_page_config(
 DB_NAME = "./benchmark.db"
 
 
+def see_entire_table():
+    conn = sqlite3.connect(DB_NAME)
+    c = conn.cursor()
+    c.execute('SELECT * FROM submissions')
+    conn.commit()
+    conn.close()
+
+
+
 def init_db():
     conn = sqlite3.connect(DB_NAME)
     c = conn.cursor()
@@ -33,7 +35,7 @@ def init_db():
     # Submissions Table
     c.execute('''CREATE TABLE IF NOT EXISTS submissions 
                  (id INTEGER PRIMARY KEY AUTOINCREMENT, 
-                  username TEXT, score REAL, timestamp DATETIME)''')
+                  username TEXT, bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 , timestamp DATETIME)''')
     conn.commit()
     conn.close()
 
@@ -67,24 +69,44 @@ def login_user(username, password):
     conn.close()
     return data
 
-def save_submission(username, score):
+def save_submission(username, bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 ):
     conn = sqlite3.connect(DB_NAME)
     c = conn.cursor()
-    c.execute('INSERT INTO submissions(username, score, timestamp) VALUES (?,?,?)', 
-              (username, score, datetime.now()))
+    c.execute('INSERT INTO submissions(username, bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 , timestamp) VALUES (?,?, ?, ? ,? ,?)', 
+              (username, bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 , datetime.now()))
     conn.commit()
     conn.close()
 
+
+    
+
 def get_leaderboard_data():
     conn = sqlite3.connect(DB_NAME)
     # Get the BEST score for each user
     query = """
-    SELECT username, MAX(score) as best_accuracy, MAX(timestamp) as last_submission
-    FROM submissions 
-    GROUP BY username 
-    ORDER BY best_accuracy DESC
+    WITH BestScores AS (
+        SELECT 
+            username, 
+            MAX(bbox_mAP) as max_bbox_mAP 
+        FROM submissions 
+        GROUP BY username
+    )
+    SELECT 
+        s.username, 
+        s.bbox_mAP, 
+        s.bbox_AP50, 
+        s.segm_mAP, 
+        s.segm_AP50, 
+        MAX(s.timestamp) as last_submission -- MAX(timestamp) to get the most recent best submission
+    FROM submissions s
+    INNER JOIN BestScores b ON s.username = b.username AND s.bbox_mAP = b.max_bbox_mAP
+    GROUP BY s.username
+    ORDER BY s.bbox_mAP DESC, s.timestamp ASC
     """
     df = pd.read_sql_query(query, conn)
+    # Rename the column for display clarity
+    df = df.rename(columns={'bbox_mAP': 'Best_bbox_mAP'})
+    
     conn.close()
     return df
 
@@ -131,16 +153,46 @@ def main_app():
         st.session_state['username'] = None
         st.rerun()
 
-    # --- PAGE: SUBMIT MODEL ---
+    # --- NEW: Overview Expander ---
+    with st.expander("ℹ️ Overview of the AI Benchmark Arena"):
+        # Placeholder for an informative image
+        st.markdown(
+            """
+            A natural application of computer vision is to assist blind people, whether that may be to overcome their daily visual challenges or break down their social accessibility barriers. BIV-Priv is proposed to preserve a blind person's visual privacy to ensure they can access visual-related tools safely.
+
+VizWiz Challenge 2025 is the 1th edition of the Few-Shot Private Object Localization Challenge on the BIV-Priv dataset. To participate in the challenge, you can find instructions on the Challenge website.
+
+Note: All the timings on the EvalAI platform are local to your timezone.
+            """
+        )
+        overview_image = Image.open("./overview_image.png").resize((600, 600))
+        st.image(overview_image, caption="Example of an object localization task", )
+
+    # --- NEW: Evaluation Expander ---
+    with st.expander("📐 How is the Score Calculated?"):
+        st.markdown(
+            """
+            Terms and Conditions
+The images and annotations in this dataset belong to the VizWiz team and are licensed under a Commons Attribution 4.0 International License.
+Copyright © 2015, VizWiz team. All rights reserved. Redistribution and use software in source and binary form, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the VizWiz team nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE AND ANNOTATIONS ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+            """
+        )
+
+    st.markdown("---") # Visual separator
+
+    # --- PAGE: SUBMIT MODEL (Existing Code) ---
     if choice == "Submit Model":
         st.header("🚀 Submit your Predictions")
         
+        # ... (rest of the Submit Model code)
+        
         col1, col2 = st.columns([2, 1])
         
         with col1:
             # st.info("Upload your CSV file. It must contain `id` and `prediction` columns.")
-            uploaded_file = st.file_uploader("Choose a CSV file", type="json")
+            uploaded_file = st.file_uploader("Choose a JSON file", type="json") # Corrected type to 'json' based on localization_eval
             
+            # ... (rest of the submission logic)
             
             if uploaded_file is not None:
                 save_path = f"./{uploaded_file.name}"  
@@ -149,28 +201,23 @@ def main_app():
                 with open(save_path, "wb") as f:
                     f.write(uploaded_file.getbuffer())
 
-
                 if st.button("Evaluate"):
                     with st.spinner('Calculating score against Ground Truth...'):
-                        logging.info("Calculating Score....")
-                        logging.info(f"File location : {save_path}")
-                        score = evaluate_submission("src/biv_query.json", save_path)
-                        logging.info(f"Score Calculation Complete : {score}")
-                        st.success(f"Score : {score}")
-                        if score is not None:
-                            save_submission(st.session_state['username'], score)
+                        # Ensure the ground truth path is correct
+                        bbox_mAP, bbox_AP50, segm_mAP, segm_AP50  = evaluate_submission("/src/biv_query.json", save_path)
+                        st.success(f"bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 : {bbox_mAP, bbox_AP50, segm_mAP, segm_AP50}")
+                        if bbox_mAP is not None and bbox_AP50 is not None and segm_mAP is not None and segm_AP50 is not None:
+                            save_submission(st.session_state['username'], bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 )
                             st.balloons()
                             st.success("Submission Successful!")
                             
                             # Display Metrics
-                            st.metric(label="Your Model Accuracy Score", value=f"{score}%")
+                            st.metric(label="Result : ", value=f"{bbox_mAP, bbox_AP50, segm_mAP, segm_AP50}%")
         
-        with col2:
-            st.markdown("### Sample Format")
-            st.dataframe(pd.DataFrame({'id': [1, 2], 'prediction': [100, 205]}), hide_index=True)
 
-    # --- PAGE: LEADERBOARD ---
+    # --- PAGE: LEADERBOARD (Existing Code) ---
     elif choice == "Leaderboard":
+        # ... (rest of the Leaderboard code)
         
         st.header("🏆 Leaderboard")
         st.write("Rankings based on the highest accuracy score achieved.")
@@ -178,19 +225,28 @@ def main_app():
         df_leaderboard = get_leaderboard_data()
         
         if not df_leaderboard.empty:
-            # styling the leaderboard
+            # Add a Rank column
+            df_leaderboard.insert(0, 'Rank', range(1, len(df_leaderboard) + 1))
+            
+            # Apply formatting and configuration
             st.dataframe(
                 df_leaderboard,
                 column_config={
+                    "Rank": st.column_config.Column("Rank", width="small"),
                     "username": "Participant",
-                    "best_accuracy": st.column_config.ProgressColumn(
-                        "Accuracy Score",
-                        format="%.2f%%",
-                        min_value=0,
-                        max_value=100,
+                    # The main ranking metric (bbox_mAP), formatted to 4 decimal places
+                    "Best_bbox_mAP": st.column_config.NumberColumn(
+                        "bbox_mAP (Primary)",
+                        format="%.4f",
+                        help="Best Bounding Box Mean Average Precision achieved."
                     ),
+                    # Other metrics, formatted as numbers without a progress bar
+                    "bbox_AP50": st.column_config.NumberColumn("bbox_AP50", format="%.4f"),
+                    "segm_mAP": st.column_config.NumberColumn("segm_mAP", format="%.4f"),
+                    "segm_AP50": st.column_config.NumberColumn("segm_AP50", format="%.4f"),
+                    # Datetime column configuration remains the same
                     "last_submission": st.column_config.DatetimeColumn(
-                        "Last Active",
+                        "Last Active Submission",
                         format="D MMM YYYY, h:mm a",
                     ),
                 },
@@ -198,11 +254,14 @@ def main_app():
                 hide_index=True,
             )
         else:
-            st.info("No submissions yet. Be the first!")
+            st.info("No submissions yet. Be the first to submit your model!")
 
+            
 if __name__ == '__main__':
     init_db()
-    
+    print("see entire table")
+    see_entire_table()
+    print("---------------------------")
     # Session State Initialization
     if 'logged_in' not in st.session_state:
         st.session_state['logged_in'] = False