Update src/main.py

src/main.py

@@ -1,399 +1,404 @@
-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")
+st.write("hello")
+
+
+
+# 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")
     
-    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()