Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,24 +2,29 @@ import streamlit as st
 import pandas as pd
 import os
 import hashlib
+import sqlite3 # Kept for potential local debugging, though we use CSV for persistence
 from datetime import datetime
 from pathlib import Path
 from huggingface_hub import CommitScheduler
-from eval_answer_therapy import evaluate_submission
+from localization_eval import evaluate_submission
 from PIL import Image
 
-# --- PERSISTENCE CONFIGURATION ---
-DATA_FILENAME = "submissions.csv"
-USER_FILENAME = "users.csv"
+# --- CONFIGURATION & SETUP ---
+st.set_page_config(
+    page_title="AI Benchmark Arena",
+    page_icon="🏆",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# --- HUGGING FACE PERSISTENCE SETUP ---
 DATA_DIR = Path("data")
 DATA_DIR.mkdir(exist_ok=True)
+SUBMISSIONS_CSV = DATA_DIR / "submissions.csv"
+USERS_CSV = DATA_DIR / "users.csv"
 
-DATA_PATH = DATA_DIR / DATA_FILENAME
-USER_PATH = DATA_DIR / USER_FILENAME
-
-# Initialize CommitScheduler
-# This will automatically sync everything in the /data folder to your HF Dataset
-repo_id = "your-username/your-private-dataset-name" # TODO: Change this
+# Change 'your-username/your-dataset-name' to your actual repo ID
+repo_id = "your-username/your-private-dataset" 
 
 scheduler = CommitScheduler(
     repo_id=repo_id,
@@ -30,81 +35,78 @@ scheduler = CommitScheduler(
     token=os.getenv("HF_TOKEN")
 )
 
-# --- DB HELPERS (Replaced SQLite with Pandas/CSV) ---
+
 
 def init_db():
-    # Create files if they don't exist
-    if not USER_PATH.exists():
-        pd.DataFrame(columns=["username", "password"]).to_csv(USER_PATH, index=False)
-    if not DATA_PATH.exists():
-        pd.DataFrame(columns=["username", "bbox_mAP", "bbox_AP50", "segm_mAP", "segm_AP50", "timestamp"]).to_csv(DATA_PATH, index=False)
+    """Initializes the CSV files if they do not exist in the data directory."""
+    if not USERS_CSV.exists():
+        pd.DataFrame(columns=["username", "password"]).to_csv(USERS_CSV, index=False)
+    
+    if not SUBMISSIONS_CSV.exists():
+        pd.DataFrame(columns=["username", "bbox_mAP", "bbox_AP50", "segm_mAP", "segm_AP50", "timestamp"]).to_csv(SUBMISSIONS_CSV, index=False)
 
 def make_hashes(password):
     return hashlib.sha256(str.encode(password)).hexdigest()
 
 def add_user(username, password):
     with scheduler.lock:
-        df = pd.read_csv(USER_PATH)
+        df = pd.read_csv(USERS_CSV)
         if username in df['username'].values:
             return False
         new_user = pd.DataFrame([{"username": username, "password": make_hashes(password)}])
         df = pd.concat([df, new_user], ignore_index=True)
-        df.to_csv(USER_PATH, index=False)
+        df.to_csv(USERS_CSV, index=False)
         return True
 
 def login_user(username, password):
-    df = pd.read_csv(USER_PATH)
-    user_row = df[(df['username'] == username) & (df['password'] == make_hashes(password))]
-    return not user_row.empty
+    if not USERS_CSV.exists():
+        return []
+    df = pd.read_csv(USERS_CSV)
+    user_match = df[(df['username'] == username) & (df['password'] == make_hashes(password))]
+    return user_match.values.tolist()
 
 def save_submission(username, bbox_mAP, bbox_AP50, segm_mAP, segm_AP50):
     with scheduler.lock:
-        df = pd.read_csv(DATA_PATH)
-        new_sub = pd.DataFrame([{
+        df = pd.read_csv(SUBMISSIONS_CSV)
+        new_row = {
             "username": username,
             "bbox_mAP": bbox_mAP,
             "bbox_AP50": bbox_AP50,
             "segm_mAP": segm_mAP,
             "segm_AP50": segm_AP50,
             "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        }])
-        df = pd.concat([df, new_sub], ignore_index=True)
-        df.to_csv(DATA_PATH, index=False)
+        }
+        df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
+        df.to_csv(SUBMISSIONS_CSV, index=False)
 
 def get_leaderboard_data():
-    if not DATA_PATH.exists():
+    if not SUBMISSIONS_CSV.exists():
         return pd.DataFrame()
     
-    df = pd.read_csv(DATA_PATH)
+    df = pd.read_csv(SUBMISSIONS_CSV)
     if df.empty:
         return df
 
-    # Logic to get the BEST score per user
-    # Sorting by segm_mAP (desc) and timestamp (asc) to get best/earliest
+    # Logic: Get the highest segm_mAP per user, then the earliest timestamp if tied
+    df['timestamp'] = pd.to_datetime(df['timestamp'])
     df = df.sort_values(by=['segm_mAP', 'timestamp'], ascending=[False, True])
     df_best = df.drop_duplicates(subset='username', keep='first')
     
-    df_best = df_best.rename(columns={'segm_mAP': 'Best_segm_mAP'})
+    df_best = df_best.rename(columns={'segm_mAP': 'Best_segm_mAP', 'timestamp': 'last_submission'})
     return df_best
 
-# --- CONFIGURATION & SETUP ---
-st.set_page_config(page_title="AI Benchmark Arena", page_icon="🏆", layout="wide")
-
-# (The rest of your ui_login_signup and main_app functions remain largely the same)
-# Just ensure you call the new CSV-based functions.
+# --- User Interface ---
 
-# ... [KEEP YOUR ui_login_signup() and UI code here] ...
 def ui_login_signup():
-    
     st.title("Welcome to Benchmark Arena 🏆")
-
+    
     tab1, tab2 = st.tabs(["Login", "Sign Up"])
 
     with tab1:
         st.subheader("Sign In")
         username = st.text_input("Username", key="login_user")
         password = st.text_input("Password", type='password', key="login_pass")
-
+        
         if st.button("Login"):
             if login_user(username, password):
                 st.session_state['logged_in'] = True
@@ -117,27 +119,101 @@ def ui_login_signup():
         st.subheader("Create New Account")
         new_user = st.text_input("Username", key="new_user")
         new_pass = st.text_input("Password", type='password', key="new_pass")
-
+        
         if st.button("Sign Up"):
             if add_user(new_user, new_pass):
                 st.success("Account created! Please navigate to Login.")
             else:
                 st.warning("Username already exists.")
 
-
-
 def main_app():
-    # (Sidebar and Expanders code here - same as your original)
+    # Sidebar Navigation
+    st.sidebar.title(f"Hi, {st.session_state['username']}!")
+    menu = ["Submit Model", "Leaderboard"]
+    choice = st.sidebar.radio("Navigation", menu)
     
-    # In your "Evaluate" button logic:
-    # Ensure variables are mapped correctly to the new save_submission()
-    if st.button("Evaluate"):
-        with st.spinner('Calculating...'):
-            bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 = evaluate_submission("src/biv_query.json", save_path)
-            if bbox_mAP is not None:
-                save_submission(st.session_state['username'], bbox_mAP, bbox_AP50, segm_mAP, segm_AP50)
-                st.balloons()
-                st.success("Submission Successful!")
+    st.sidebar.markdown("---")
+    if st.sidebar.button("Logout"):
+        st.session_state['logged_in'] = False
+        st.session_state['username'] = None
+        st.rerun()
+
+    with st.expander("ℹ️ Overview of the AI Benchmark Arena"):
+        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.
+            """
+        )
+        try:
+            overview_image = Image.open("src/overview_image.png").resize((600, 600))
+            st.image(overview_image, caption="Example of an object localization task")
+        except FileNotFoundError:
+            st.warning("Overview image not found in src/ folder.")
+
+    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.
+            """
+        )
+
+    st.markdown("---") 
+
+    if choice == "Submit Model":
+        st.header("🚀 Submit your Predictions")
+        
+        col1, col2 = st.columns([2, 1])
+        
+        with col1:
+            uploaded_file = st.file_uploader("Choose a JSON file", type="json")
+            
+            if uploaded_file is not None:
+                save_path = f"./{uploaded_file.name}"  
+
+                with open(save_path, "wb") as f:
+                    f.write(uploaded_file.getbuffer())
+
+                if st.button("Evaluate"):
+                    with st.spinner('Calculating score against Ground Truth...'):
+                        # Using your custom evaluation function
+                        bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 = evaluate_submission("src/biv_query.json", save_path)
+                        
+                        if all(v is not None for v in [bbox_mAP, bbox_AP50, segm_mAP, segm_AP50]):
+                            st.success(f"Results: bbox_mAP: {bbox_mAP:.2f}, bbox_AP50: {bbox_AP50:.2f}, segm_mAP: {segm_mAP:.2f}, segm_AP50: {segm_AP50:.2f}")
+                            save_submission(st.session_state['username'], bbox_mAP, bbox_AP50, segm_mAP, segm_AP50)
+                            st.balloons()
+                            st.success("Submission Successful!")
+                        else:
+                            st.error("Evaluation failed. Please check your JSON format.")
+
+    elif choice == "Leaderboard":
+        st.header("🏆 Leaderboard")
+        st.write("Rankings based on the highest segmentation mAP score achieved.")
+        
+        df_leaderboard = get_leaderboard_data()
+        
+        if not df_leaderboard.empty:
+            df_leaderboard.insert(0, 'Rank', range(1, len(df_leaderboard) + 1))
+            
+            st.dataframe(
+                df_leaderboard,
+                column_config={
+                    "Rank": st.column_config.Column("Rank", width="small"),
+                    "username": "Participant",
+                    "Best_segm_mAP": st.column_config.NumberColumn("segm_mAP (Primary)", format="%.4f"),
+                    "bbox_mAP": st.column_config.NumberColumn("bbox_mAP", format="%.4f"),
+                    "bbox_AP50": st.column_config.NumberColumn("bbox_AP50", format="%.4f"),
+                    "segm_AP50": st.column_config.NumberColumn("segm_AP50", format="%.4f"),
+                    "last_submission": st.column_config.DatetimeColumn("Last Active Submission", format="D MMM YYYY, h:mm a"),
+                },
+                use_container_width=True,
+                hide_index=True,
+            )
+        else:
+            st.info("No submissions yet. Be the first to submit your model!")
 
 if __name__ == '__main__':
     init_db()