Delete utils

utils/auth.py

@@ -1,33 +0,0 @@
-import os
-import logging
-import streamlit as st
-
-logger = logging.getLogger(__name__)
-
-def check_token(user_token: str):
-    ACCESS_TOKEN = os.getenv("ACCESS_TOKEN")
-    if not ACCESS_TOKEN:
-        logger.critical("ACCESS_TOKEN not set in environment.")
-        return False, "Server error: Access token not configured."
-    if user_token == ACCESS_TOKEN:
-        logger.info("Access token validated successfully.")
-        return True, ""
-    logger.warning("Invalid access token attempt.")
-    return False, "Invalid token."
-
-def gated_access() -> bool:
-    if "authenticated" not in st.session_state:
-        st.session_state["authenticated"] = False
-
-    if not st.session_state["authenticated"]:
-        st.markdown("## Access Required")
-        token_input = st.text_input("Enter Access Token", type="password")
-        if st.button("Unlock App"):
-            ok, error_msg = check_token(token_input)
-            if ok:
-                st.session_state["authenticated"] = True
-                st.rerun()
-            else:
-                st.error(error_msg)
-        return False
-    return True

utils/dataframe.py

@@ -1,59 +0,0 @@
-import json
-import pandas as pd
-from typing import Any, Dict, List
-
-def safe_dataframe(df: pd.DataFrame) -> pd.DataFrame:
-    for col in df.columns:
-        df[col] = df[col].astype(str)
-    return df
-
-def analysis_to_csv(analysis: Dict[str, Any]) -> str:
-    rows = []
-    for sb in analysis.get("storyboard", []):
-        rows.append({"Section": "Storyboard", **sb})
-    for sc in analysis.get("script", []):
-        rows.append({"Section": "Script", **sc})
-    for met in analysis.get("video_analysis", {}).get("video_metrics", []):
-        rows.append({"Section": "Metrics", **met})
-    for imp in analysis.get("timestamp_improvements", []):
-        rows.append({"Section": "Improvements", **imp})
-    if not rows:
-        return ""
-    df = pd.DataFrame(rows)
-    return df.to_csv(index=False)
-
-def _normalize_list(value: Any) -> List[str]:
-    if value is None:
-        return []
-    if isinstance(value, list):
-        return [str(v) for v in value]
-    return [s for s in str(value).splitlines() if s.strip()]
-
-def _to_dataframe(items: Any, columns_map: Dict[str, str]) -> pd.DataFrame:
-    if not isinstance(items, list) or not items:
-        return pd.DataFrame(columns=list(columns_map.values()))
-    df = pd.DataFrame(items)
-    df = df.rename(columns=columns_map)
-    ordered_cols = [columns_map[k] for k in columns_map.keys() if columns_map[k] in df.columns]
-    df = df.reindex(columns=ordered_cols)
-    return df
-
-def _mean_effectiveness(metrics: List[Dict[str, Any]]) -> float:
-    if not metrics:
-        return 0.0
-    scores = []
-    for m in metrics:
-        s = str(m.get("effectiveness_score", "0/10")).split("/")[0]
-        try:
-            scores.append(int(s))
-        except Exception:
-            pass
-    return round(sum(scores) / len(scores), 2) if scores else 0.0
-
-def _search_dataframe(df: pd.DataFrame, query: str) -> pd.DataFrame:
-    if not query or df.empty:
-        return df
-    mask = pd.Series([False]*len(df))
-    for col in df.columns:
-        mask = mask | df[col].astype(str).str.contains(query, case=False, na=False)
-    return df[mask]

utils/video.py

@@ -1,19 +0,0 @@
-import cv2
-import base64
-import logging
-
-logger = logging.getLogger(__name__)
-
-def get_video_thumbnail_base64(video_path: str, time_sec: int = 1) -> str:
-    try:
-        cap = cv2.VideoCapture(video_path)
-        cap.set(cv2.CAP_PROP_POS_MSEC, time_sec * 1000)
-        success, frame = cap.read()
-        cap.release()
-        if not success:
-            return ""
-        _, buffer = cv2.imencode(".jpg", frame)
-        return base64.b64encode(buffer).decode("utf-8")
-    except Exception:
-        logger.exception("Thumbnail extraction failed")
-        return ""