Update src/main.py

src/main.py

@@ -1,404 +1,404 @@
-import streamlit as st
-st.write("hello")
+# import streamlit as st
+# st.write("hello")
 
 
 
-# import os
-# import json
-# import uuid
-# import time
-# import tempfile
-# from typing import Any, Dict, List, Tuple
+import os
+import json
+import uuid
+import time
+import tempfile
+from typing import Any, Dict, List, Tuple
 
-# import streamlit as st
-# import pandas as pd
-# from PIL import Image
-# from huggingface_hub import HfApi, hf_hub_download
-# from huggingface_hub.utils import HfHubHTTPError
-
-# print("Imported required functions")
-# # =========================
-# # CONFIG
-# # =========================
-
-# st.set_page_config(
-#     page_title="AI Benchmark Arena",
-#     page_icon="🏆",
-#     layout="wide",
-#     initial_sidebar_state="expanded",
-# )
-# print("Config setup")
-
-# #Set this to the private dataset repo that acts as the "database"
-# DB_REPO_ID = os.getenv("DB_REPO_ID", "NidhiS09/VizWiz-submissions-db")
-# DB_REPO_TYPE = "dataset"
-# print(DB_REPO_ID)
-
-
-# # This must exist as a Space Secret in the PUBLIC UI Space
-# SUBMISSIONS_TOKEN = os.getenv("SUBMISSIONS_TOKEN", "")
-# print("Token Acquired")
-
-# # Phase config (copied from EvalAI config intent)
-# PHASES = [
-#     {"label": "Dev (qeury-dev2024)", "codename": "test-dev2024"},
-#     {"label": "Standard (query-standard2024)", "codename": "test-standard2024"},
-#     {"label": "Challenge (query-challenge2024)", "codename": "test-challenge2024"},
-# ]
-
-# CHALLENGE_TYPES = ["Object Detection", "Instance Segmentation"]
-
-# # Leaderboard columns from your EvalAI yaml
-# LEADERBOARD_METRICS = ["bbox_mAP", "bbox_AP50", "segm_mAP", "segm_AP50"]
-# DEFAULT_SORT_METRIC = "segm_AP50"
-# print("Phases, challenge type and metrics setup")
-# # =========================
-# # HELPERS
-# # =========================
-
-# def _require_token() -> None:
-#     if not SUBMISSIONS_TOKEN:
-#         st.error(
-#             "Missing SUBMISSIONS_TOKEN. Add it in Space Settings → Secrets "
-#             "(token must have read/write access ONLY to the private DB dataset repo)."
-#         )
-#         st.stop()
-
-
-# def _validate_submission_json(obj: Any) -> Tuple[bool, str]:
-#     """
-#     Validates the submission format from your instructions:
-#     - Top-level must be a list
-#     - Each item must be a dict containing:
-#       image_id (int), score (number), category_id (int), area (number),
-#       bbox ([x,y,w,h]), segmentation (list)
-#     """
-#     if not isinstance(obj, list):
-#         return False, "Submission must be a JSON list of annotations."
-
-#     required_keys = {"image_id", "score", "category_id", "area", "bbox", "segmentation"}
-
-#     for i, ann in enumerate(obj):
-#         if not isinstance(ann, dict):
-#             return False, f"Annotation at index {i} must be an object/dict."
-
-#         missing = required_keys - set(ann.keys())
-#         if missing:
-#             return False, f"Annotation at index {i} missing keys: {sorted(list(missing))}"
-
-#         # Basic type checks
-#         if not isinstance(ann["image_id"], int):
-#             return False, f"image_id at index {i} must be an integer."
-#         if not isinstance(ann["category_id"], int):
-#             return False, f"category_id at index {i} must be an integer."
-
-#         if not isinstance(ann["score"], (int, float)):
-#             return False, f"score at index {i} must be a number."
-
-#         if not isinstance(ann["area"], (int, float)):
-#             return False, f"area at index {i} must be a number."
-
-#         bbox = ann["bbox"]
-#         if not (isinstance(bbox, list) and len(bbox) == 4 and all(isinstance(x, (int, float)) for x in bbox)):
-#             return False, f"bbox at index {i} must be a list of 4 numbers: [x, y, w, h]."
-
-#         segm = ann["segmentation"]
-#         if not isinstance(segm, list):
-#             return False, f"segmentation at index {i} must be a list."
-
-#     return True, "OK"
-
-
-# def _upload_json(api: HfApi, data: Dict[str, Any] | List[Any], path_in_repo: str) -> None:
-#     with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as tmp:
-#         json.dump(data, tmp, ensure_ascii=False)
-#         tmp_path = tmp.name
-
-#     try:
-#         api.upload_file(
-#             path_or_fileobj=tmp_path,
-#             path_in_repo=path_in_repo,
-#             repo_id=DB_REPO_ID,
-#             repo_type=DB_REPO_TYPE,
-#             token=SUBMISSIONS_TOKEN,
-#             commit_message=f"Add {path_in_repo}",
-#         )
-#     finally:
-#         try:
-#             os.remove(tmp_path)
-#         except OSError:
-#             pass
-
-
-# def _create_submission_record(
-#     *,
-#     pred: List[Dict[str, Any]],
-#     team: str,
-#     model_name: str,
-#     phase_codename: str,
-#     challenge_type: str,
-#     original_filename: str,
-# ) -> str:
-#     """
-#     Writes pred/meta/status to the private DB dataset repo.
-#     Returns submission_id.
-#     """
-#     _require_token()
-#     api = HfApi()
-
-#     submission_id = str(uuid.uuid4())
-#     ts = int(time.time())
-
-#     meta = {
-#         "submission_id": submission_id,
-#         "team": team.strip(),
-#         "model": model_name.strip(),
-#         "phase_codename": phase_codename,
-#         "challenge_type": challenge_type,
-#         "timestamp": ts,
-#         "original_filename": original_filename,
-#     }
-
-#     status = {"state": "queued", "timestamp": ts}
-
-#     base = f"submissions/{submission_id}"
-#     _upload_json(api, pred, f"{base}/pred.json")
-#     _upload_json(api, meta, f"{base}/meta.json")
-#     _upload_json(api, status, f"{base}/status.json")
-
-#     return submission_id
-
-
-# def _download_leaderboard_jsonl() -> str | None:
-#     """
-#     Downloads leaderboard.jsonl from the DB repo.
-#     Returns local path or None if missing.
-#     """
-#     _require_token()
-#     try:
-#         return hf_hub_download(
-#             repo_id=DB_REPO_ID,
-#             repo_type=DB_REPO_TYPE,
-#             filename="leaderboard.jsonl",
-#             token=SUBMISSIONS_TOKEN,
-#         )
-#     except HfHubHTTPError as e:
-#         # Most common: 404 when file doesn't exist yet
-#         if "404" in str(e):
-#             return None
-#         raise
-
-
-# def _load_leaderboard_df() -> pd.DataFrame:
-#     path = _download_leaderboard_jsonl()
-#     if path is None:
-#         return pd.DataFrame(columns=["team", "model", "phase_codename", *LEADERBOARD_METRICS, "timestamp"])
-
-#     rows = []
-#     with open(path, "r", encoding="utf-8") as f:
-#         for line in f:
-#             line = line.strip()
-#             if not line:
-#                 continue
-#             try:
-#                 rows.append(json.loads(line))
-#             except json.JSONDecodeError:
-#                 # Skip malformed lines rather than crashing the UI
-#                 continue
-
-#     if not rows:
-#         return pd.DataFrame(columns=["team", "model", "phase_codename", *LEADERBOARD_METRICS, "timestamp"])
-
-#     df = pd.DataFrame(rows)
-
-#     # Ensure columns exist
-#     for col in ["team", "model", "phase_codename", "timestamp", *LEADERBOARD_METRICS]:
-#         if col not in df.columns:
-#             df[col] = None
-
-#     # Sort descending by default metric
-#     if DEFAULT_SORT_METRIC in df.columns:
-#         df = df.sort_values(by=DEFAULT_SORT_METRIC, ascending=False, kind="mergesort")
-
-#     return df
-
-
-# # =========================
-# # UI
-# # =========================
-# #2 FUNCTIONS WERE HERE, RENDER_OVERVIEW AND RENDER EVAL
-
-# def page_submit():
-#     st.header("🚀 Submit your Predictions")
-
-#     col1, col2 = st.columns([2, 1])
-
-#     with col2:
-#         st.subheader("Submission Info")
-#         team = st.text_input("Team / Display Name", value=st.session_state.get("team", ""))
-#         model_name = st.text_input("Model Name", value=st.session_state.get("model_name", ""))
-
-#         phase_label = st.selectbox("Phase", [p["label"] for p in PHASES])
-#         phase_codename = next(p["codename"] for p in PHASES if p["label"] == phase_label)
-
-#         challenge_type = st.radio("Challenge type", CHALLENGE_TYPES, horizontal=False)
-
-#         st.session_state["team"] = team
-#         st.session_state["model_name"] = model_name
-
-#         st.caption("Your submission will be queued for evaluation. Scores appear on the leaderboard after processing.")
-
-#     with col1:
-#         st.subheader("Upload Submission File")
-#         uploaded_file = st.file_uploader("Choose a JSON file", type=["json"])
-
-#         if uploaded_file is None:
-#             st.info("Upload a JSON file that contains a list of annotations.")
-#             return
-
-#         # Parse JSON
-#         try:
-#             raw = uploaded_file.getvalue().decode("utf-8")
-#             pred_obj = json.loads(raw)
-#         except Exception:
-#             st.error("Could not parse JSON. Please upload a valid JSON file.")
-#             return
-
-#         ok, msg = _validate_submission_json(pred_obj)
-#         if not ok:
-#             st.error(f"Invalid submission format: {msg}")
-#             return
-
-#         st.success("Submission file looks valid ✅")
-
-#         submit_clicked = st.button("Submit (Queue for Evaluation)", type="primary")
-
-#         if submit_clicked:
-#             if not team.strip():
-#                 st.error("Please enter Team / Display Name.")
-#                 return
-#             if not model_name.strip():
-#                 st.error("Please enter Model Name.")
-#                 return
-
-#             with st.spinner("Uploading submission to the private database repo..."):
-#                 try:
-#                     submission_id = _create_submission_record(
-#                         pred=pred_obj,
-#                         team=team,
-#                         model_name=model_name,
-#                         phase_codename=phase_codename,
-#                         challenge_type=challenge_type,
-#                         original_filename=uploaded_file.name,
-#                     )
-#                 except Exception as e:
-#                     st.error(f"Upload failed: {e}")
-#                     return
-
-#             st.balloons()
-#             st.success("Submission queued successfully!")
-#             st.code(f"Submission ID: {submission_id}")
-
-#             st.info("Next: the private evaluator will score your submission and update the leaderboard.")
-
-
-# def page_leaderboard():
-#     st.header("🏆 Leaderboard")
-#     st.write(f"Ranked by **{DEFAULT_SORT_METRIC}** (descending).")
-
-#     with st.spinner("Loading leaderboard from private database repo..."):
-#         try:
-#             df = _load_leaderboard_df()
-#         except Exception as e:
-#             st.error(f"Could not load leaderboard: {e}")
-#             return
-
-#     if df.empty:
-#         st.info("No scored submissions yet. Submit a model to get started!")
-#         return
-
-#     # Add Rank column
-#     df_display = df.copy()
-#     df_display.insert(0, "Rank", range(1, len(df_display) + 1))
-
-#     # Optional: pretty timestamp
-#     if "timestamp" in df_display.columns:
-#         df_display["timestamp"] = pd.to_datetime(df_display["timestamp"], unit="s", errors="coerce")
-
-#     st.dataframe(
-#         df_display,
-#         column_config={
-#             "Rank": st.column_config.Column("Rank", width="small"),
-#             "team": "Team",
-#             "model": "Model",
-#             "phase_codename": "Phase",
-#             "bbox_mAP": st.column_config.NumberColumn("bbox_mAP", format="%.4f"),
-#             "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"),
-#             "timestamp": st.column_config.DatetimeColumn("Scored at", format="D MMM YYYY, h:mm a"),
-#         },
-#         use_container_width=True,
-#         hide_index=True,
-#     )
-
-
-# def main():
-
-#     st.write("✅ App booted and rendering UI")
+import streamlit as st
+import pandas as pd
+from PIL import Image
+from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.utils import HfHubHTTPError
+
+print("Imported required functions")
+# =========================
+# CONFIG
+# =========================
+
+st.set_page_config(
+    page_title="AI Benchmark Arena",
+    page_icon="🏆",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+print("Config setup")
+
+#Set this to the private dataset repo that acts as the "database"
+DB_REPO_ID = os.getenv("DB_REPO_ID", "NidhiS09/VizWiz-submissions-db")
+DB_REPO_TYPE = "dataset"
+print(DB_REPO_ID)
+
+
+# This must exist as a Space Secret in the PUBLIC UI Space
+SUBMISSIONS_TOKEN = os.getenv("SUBMISSIONS_TOKEN", "")
+print("Token Acquired")
+
+# Phase config (copied from EvalAI config intent)
+PHASES = [
+    {"label": "Dev (qeury-dev2024)", "codename": "test-dev2024"},
+    {"label": "Standard (query-standard2024)", "codename": "test-standard2024"},
+    {"label": "Challenge (query-challenge2024)", "codename": "test-challenge2024"},
+]
+
+CHALLENGE_TYPES = ["Object Detection", "Instance Segmentation"]
+
+# Leaderboard columns from your EvalAI yaml
+LEADERBOARD_METRICS = ["bbox_mAP", "bbox_AP50", "segm_mAP", "segm_AP50"]
+DEFAULT_SORT_METRIC = "segm_AP50"
+print("Phases, challenge type and metrics setup")
+# =========================
+# HELPERS
+# =========================
+
+def _require_token() -> None:
+    if not SUBMISSIONS_TOKEN:
+        st.error(
+            "Missing SUBMISSIONS_TOKEN. Add it in Space Settings → Secrets "
+            "(token must have read/write access ONLY to the private DB dataset repo)."
+        )
+        st.stop()
+
+
+def _validate_submission_json(obj: Any) -> Tuple[bool, str]:
+    """
+    Validates the submission format from your instructions:
+    - Top-level must be a list
+    - Each item must be a dict containing:
+      image_id (int), score (number), category_id (int), area (number),
+      bbox ([x,y,w,h]), segmentation (list)
+    """
+    if not isinstance(obj, list):
+        return False, "Submission must be a JSON list of annotations."
+
+    required_keys = {"image_id", "score", "category_id", "area", "bbox", "segmentation"}
+
+    for i, ann in enumerate(obj):
+        if not isinstance(ann, dict):
+            return False, f"Annotation at index {i} must be an object/dict."
+
+        missing = required_keys - set(ann.keys())
+        if missing:
+            return False, f"Annotation at index {i} missing keys: {sorted(list(missing))}"
+
+        # Basic type checks
+        if not isinstance(ann["image_id"], int):
+            return False, f"image_id at index {i} must be an integer."
+        if not isinstance(ann["category_id"], int):
+            return False, f"category_id at index {i} must be an integer."
+
+        if not isinstance(ann["score"], (int, float)):
+            return False, f"score at index {i} must be a number."
+
+        if not isinstance(ann["area"], (int, float)):
+            return False, f"area at index {i} must be a number."
+
+        bbox = ann["bbox"]
+        if not (isinstance(bbox, list) and len(bbox) == 4 and all(isinstance(x, (int, float)) for x in bbox)):
+            return False, f"bbox at index {i} must be a list of 4 numbers: [x, y, w, h]."
+
+        segm = ann["segmentation"]
+        if not isinstance(segm, list):
+            return False, f"segmentation at index {i} must be a list."
+
+    return True, "OK"
+
+
+def _upload_json(api: HfApi, data: Dict[str, Any] | List[Any], path_in_repo: str) -> None:
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as tmp:
+        json.dump(data, tmp, ensure_ascii=False)
+        tmp_path = tmp.name
+
+    try:
+        api.upload_file(
+            path_or_fileobj=tmp_path,
+            path_in_repo=path_in_repo,
+            repo_id=DB_REPO_ID,
+            repo_type=DB_REPO_TYPE,
+            token=SUBMISSIONS_TOKEN,
+            commit_message=f"Add {path_in_repo}",
+        )
+    finally:
+        try:
+            os.remove(tmp_path)
+        except OSError:
+            pass
+
+
+def _create_submission_record(
+    *,
+    pred: List[Dict[str, Any]],
+    team: str,
+    model_name: str,
+    phase_codename: str,
+    challenge_type: str,
+    original_filename: str,
+) -> str:
+    """
+    Writes pred/meta/status to the private DB dataset repo.
+    Returns submission_id.
+    """
+    _require_token()
+    api = HfApi()
+
+    submission_id = str(uuid.uuid4())
+    ts = int(time.time())
+
+    meta = {
+        "submission_id": submission_id,
+        "team": team.strip(),
+        "model": model_name.strip(),
+        "phase_codename": phase_codename,
+        "challenge_type": challenge_type,
+        "timestamp": ts,
+        "original_filename": original_filename,
+    }
+
+    status = {"state": "queued", "timestamp": ts}
+
+    base = f"submissions/{submission_id}"
+    _upload_json(api, pred, f"{base}/pred.json")
+    _upload_json(api, meta, f"{base}/meta.json")
+    _upload_json(api, status, f"{base}/status.json")
+
+    return submission_id
+
+
+def _download_leaderboard_jsonl() -> str | None:
+    """
+    Downloads leaderboard.jsonl from the DB repo.
+    Returns local path or None if missing.
+    """
+    _require_token()
+    try:
+        return hf_hub_download(
+            repo_id=DB_REPO_ID,
+            repo_type=DB_REPO_TYPE,
+            filename="leaderboard.jsonl",
+            token=SUBMISSIONS_TOKEN,
+        )
+    except HfHubHTTPError as e:
+        # Most common: 404 when file doesn't exist yet
+        if "404" in str(e):
+            return None
+        raise
+
+
+def _load_leaderboard_df() -> pd.DataFrame:
+    path = _download_leaderboard_jsonl()
+    if path is None:
+        return pd.DataFrame(columns=["team", "model", "phase_codename", *LEADERBOARD_METRICS, "timestamp"])
+
+    rows = []
+    with open(path, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                rows.append(json.loads(line))
+            except json.JSONDecodeError:
+                # Skip malformed lines rather than crashing the UI
+                continue
+
+    if not rows:
+        return pd.DataFrame(columns=["team", "model", "phase_codename", *LEADERBOARD_METRICS, "timestamp"])
+
+    df = pd.DataFrame(rows)
+
+    # Ensure columns exist
+    for col in ["team", "model", "phase_codename", "timestamp", *LEADERBOARD_METRICS]:
+        if col not in df.columns:
+            df[col] = None
+
+    # Sort descending by default metric
+    if DEFAULT_SORT_METRIC in df.columns:
+        df = df.sort_values(by=DEFAULT_SORT_METRIC, ascending=False, kind="mergesort")
+
+    return df
+
+
+# =========================
+# UI
+# =========================
+#2 FUNCTIONS WERE HERE, RENDER_OVERVIEW AND RENDER EVAL
+
+def page_submit():
+    st.header("🚀 Submit your Predictions")
+
+    col1, col2 = st.columns([2, 1])
+
+    with col2:
+        st.subheader("Submission Info")
+        team = st.text_input("Team / Display Name", value=st.session_state.get("team", ""))
+        model_name = st.text_input("Model Name", value=st.session_state.get("model_name", ""))
+
+        phase_label = st.selectbox("Phase", [p["label"] for p in PHASES])
+        phase_codename = next(p["codename"] for p in PHASES if p["label"] == phase_label)
+
+        challenge_type = st.radio("Challenge type", CHALLENGE_TYPES, horizontal=False)
+
+        st.session_state["team"] = team
+        st.session_state["model_name"] = model_name
+
+        st.caption("Your submission will be queued for evaluation. Scores appear on the leaderboard after processing.")
+
+    with col1:
+        st.subheader("Upload Submission File")
+        uploaded_file = st.file_uploader("Choose a JSON file", type=["json"])
+
+        if uploaded_file is None:
+            st.info("Upload a JSON file that contains a list of annotations.")
+            return
+
+        # Parse JSON
+        try:
+            raw = uploaded_file.getvalue().decode("utf-8")
+            pred_obj = json.loads(raw)
+        except Exception:
+            st.error("Could not parse JSON. Please upload a valid JSON file.")
+            return
+
+        ok, msg = _validate_submission_json(pred_obj)
+        if not ok:
+            st.error(f"Invalid submission format: {msg}")
+            return
+
+        st.success("Submission file looks valid ✅")
+
+        submit_clicked = st.button("Submit (Queue for Evaluation)", type="primary")
+
+        if submit_clicked:
+            if not team.strip():
+                st.error("Please enter Team / Display Name.")
+                return
+            if not model_name.strip():
+                st.error("Please enter Model Name.")
+                return
+
+            with st.spinner("Uploading submission to the private database repo..."):
+                try:
+                    submission_id = _create_submission_record(
+                        pred=pred_obj,
+                        team=team,
+                        model_name=model_name,
+                        phase_codename=phase_codename,
+                        challenge_type=challenge_type,
+                        original_filename=uploaded_file.name,
+                    )
+                except Exception as e:
+                    st.error(f"Upload failed: {e}")
+                    return
+
+            st.balloons()
+            st.success("Submission queued successfully!")
+            st.code(f"Submission ID: {submission_id}")
+
+            st.info("Next: the private evaluator will score your submission and update the leaderboard.")
+
+
+def page_leaderboard():
+    st.header("🏆 Leaderboard")
+    st.write(f"Ranked by **{DEFAULT_SORT_METRIC}** (descending).")
+
+    with st.spinner("Loading leaderboard from private database repo..."):
+        try:
+            df = _load_leaderboard_df()
+        except Exception as e:
+            st.error(f"Could not load leaderboard: {e}")
+            return
+
+    if df.empty:
+        st.info("No scored submissions yet. Submit a model to get started!")
+        return
+
+    # Add Rank column
+    df_display = df.copy()
+    df_display.insert(0, "Rank", range(1, len(df_display) + 1))
+
+    # Optional: pretty timestamp
+    if "timestamp" in df_display.columns:
+        df_display["timestamp"] = pd.to_datetime(df_display["timestamp"], unit="s", errors="coerce")
+
+    st.dataframe(
+        df_display,
+        column_config={
+            "Rank": st.column_config.Column("Rank", width="small"),
+            "team": "Team",
+            "model": "Model",
+            "phase_codename": "Phase",
+            "bbox_mAP": st.column_config.NumberColumn("bbox_mAP", format="%.4f"),
+            "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"),
+            "timestamp": st.column_config.DatetimeColumn("Scored at", format="D MMM YYYY, h:mm a"),
+        },
+        use_container_width=True,
+        hide_index=True,
+    )
+
+
+def main():
+
+    st.write("✅ App booted and rendering UI")
     
-#     st.sidebar.title("AI Benchmark Arena 🏆")
-
-#     # Warn early if DB repo isn't configured
-#     if DB_REPO_ID.startswith("NidhiS09/"):
-#         st.sidebar.warning("Set DB_REPO_ID env var or hardcode your private DB dataset repo id in main.py.")
-
-#     menu = ["Submit Model", "Leaderboard"]
-#     choice = st.sidebar.radio("Navigation", menu)
-
-#     st.sidebar.markdown("---")
-#     st.sidebar.caption("This Space queues submissions to a private DB repo and reads leaderboard results from it.")
-
-#     render_overview()
-#     render_eval_details()
-#     st.markdown("---")
-
-#     if choice == "Submit Model":
-#         page_submit()
-#     else:
-#         page_leaderboard()
-
-#     def render_overview():
-#     with st.expander("ℹ️ Overview of the AI Benchmark Arena"):
-#         st.markdown(
-#             """
-#         **Note:** This Hugging Face Space queues submissions for evaluation and persists results in a private database repo.
-#             """
-#         )
-#         # Keep this optional so missing image doesn't crash the Space
-#         try:
-#             overview_image = Image.open("src/overview_image.png").resize((600, 600))
-#             st.image(overview_image, caption="Example of an object localization task")
-#         except Exception:
-#             st.info("Overview image not found at src/overview_image.png (optional).")
-
-
-#     def render_eval_details():
-#     with st.expander("📐 How is the Score Calculated?"):
-#         st.markdown(
-#             """
-#         Your submission is evaluated offline by a private evaluator against hidden ground-truth annotations.
-#         The leaderboard reports:
-
-#         - bbox_mAP
-#         - bbox_AP50
-#         - segm_mAP
-#         - segm_AP50 (default ranking)
-
-#         Raw submissions are kept private; only scores and metadata are shown.
-#             """
-#         )
-
-
-# if __name__ == "__main__":
-#     main()
+    st.sidebar.title("AI Benchmark Arena 🏆")
+
+    # Warn early if DB repo isn't configured
+    if DB_REPO_ID.startswith("NidhiS09/"):
+        st.sidebar.warning("Set DB_REPO_ID env var or hardcode your private DB dataset repo id in main.py.")
+
+    menu = ["Submit Model", "Leaderboard"]
+    choice = st.sidebar.radio("Navigation", menu)
+
+    st.sidebar.markdown("---")
+    st.sidebar.caption("This Space queues submissions to a private DB repo and reads leaderboard results from it.")
+
+    render_overview()
+    render_eval_details()
+    st.markdown("---")
+
+    if choice == "Submit Model":
+        page_submit()
+    else:
+        page_leaderboard()
+
+    def render_overview():
+    with st.expander("ℹ️ Overview of the AI Benchmark Arena"):
+        st.markdown(
+            """
+        **Note:** This Hugging Face Space queues submissions for evaluation and persists results in a private database repo.
+            """
+        )
+        # Keep this optional so missing image doesn't crash the Space
+        try:
+            overview_image = Image.open("src/overview_image.png").resize((600, 600))
+            st.image(overview_image, caption="Example of an object localization task")
+        except Exception:
+            st.info("Overview image not found at src/overview_image.png (optional).")
+
+
+    def render_eval_details():
+    with st.expander("📐 How is the Score Calculated?"):
+        st.markdown(
+            """
+        Your submission is evaluated offline by a private evaluator against hidden ground-truth annotations.
+        The leaderboard reports:
+
+        - bbox_mAP
+        - bbox_AP50
+        - segm_mAP
+        - segm_AP50 (default ranking)
+
+        Raw submissions are kept private; only scores and metadata are shown.
+            """
+        )
+
+
+if __name__ == "__main__":
+    main()