# app.py — Student Skill Radar (MongoDB, secrets-based)
import os
from datetime import date
from typing import Dict, List
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import streamlit as st
from pymongo import MongoClient
from urllib.parse import quote_plus

st.set_page_config(page_title="Student Skill Radar", layout="wide")

# ------------------- Constants -------------------
SKILLS = [
    "Problem-Solving", "Critical Thinking", "Analytical Reasoning",
    "Adaptability", "Continuous Learning", "Creativity",
    "Communication", "Collaboration", "Community Engagement",
    "Emotional Intelligence", "Ethical Decision-Making",
    "Time Management", "Tech Aptitude"
]

SKILL_GROUPS = {
    "Problem-Solving, Critical Thinking, Analytical Reasoning": [
        "Problem-Solving", "Critical Thinking", "Analytical Reasoning"
    ],
    "Adaptability, Continuous Learning, Creativity": [
        "Adaptability", "Continuous Learning", "Creativity"
    ],
    "Time Management": ["Time Management"],
    "Communication, Teamwork, Collaboration, Community Engagement": [
        "Communication", "Collaboration", "Community Engagement"
    ],
    "Emotional Intelligence, Ethical Decision Making": [
        "Emotional Intelligence", "Ethical Decision-Making"
    ],
    "Tech Aptitude": ["Tech Aptitude"]
}

SOURCE_TO_STAGE = {
    "onboarding_responses": "onboarding",
    "closing_responses": "closing",
}

# ------------------- Helpers -------------------
def safe_mean(vals):
    clean = [v for v in vals if v is not None and not pd.isna(v)]
    return float(np.mean(clean)) if clean else np.nan

def to_01_or_nan(x):
    try:
        v = float(x)
    except Exception:
        return np.nan
    if pd.isna(v):
        return np.nan
    return max(0.0, min(1.0, v))

def aggregate_groups_row(row: pd.Series) -> Dict[str, float]:
    return {
        g: safe_mean([row.get(s, np.nan) for s in members])
        for g, members in SKILL_GROUPS.items()
    }

def df_to_grouped(df_in: pd.DataFrame) -> pd.DataFrame:
    if df_in.empty:
        return df_in
    rows = []
    for _, r in df_in.iterrows():
        grp = aggregate_groups_row(r)
        out = {"label": r["label"]}
        for glabel in SKILL_GROUPS.keys():
            v = grp.get(glabel)
            out[glabel] = 0.0 if pd.isna(v) else float(v)
        rows.append(out)
    return pd.DataFrame(rows, columns=["label"] + list(SKILL_GROUPS.keys()))

def plot_radar(df: pd.DataFrame, grouped: bool, title: str, avg_label: str = None):
    if df.empty:
        return go.Figure()

    traces = []
    labels = list(SKILL_GROUPS.keys()) if grouped else SKILLS

    for _, r in df.iterrows():
        values = [0.0 if pd.isna(r.get(k)) else float(r.get(k)) for k in labels]
        is_avg = avg_label and (str(r["label"]) == avg_label)

        traces.append(go.Scatterpolar(
            r=values + [values[0]],
            theta=labels + [labels[0]],
            name=r["label"],
            fill="toself",
            line=dict(
                width=4 if is_avg else 2,
                dash="dash" if is_avg else "solid",
                color="red" if is_avg else None
            ),
            opacity=0.7 if is_avg else 0.5
        ))

    fig = go.Figure(traces)
    fig.update_layout(
        title=title or "Skill Radar",
        showlegend=True,
        polar=dict(
            radialaxis=dict(
                autorange=False, range=[0, 1], tick0=0, dtick=0.2,
                ticks="outside", showline=True, showgrid=True, visible=True
            )
        ),
        margin=dict(l=30, r=30, t=60, b=30),
    )
    return fig
    
def _vector_from_row(row: pd.Series, cols: list[str]) -> dict:
    return {k: (None if pd.isna(row.get(k)) else float(row.get(k))) for k in cols}

def _percent_change(new: float | None, old: float | None) -> float | None:
    if new is None or old is None:
        return None
    if old == 0:
        return None  # avoid div-by-zero; you can choose to show 100% if new>0
    return (new - old) / old * 100.0

def _merge_resp_and_likert_vector(resp_vec: dict, likert_grouped_vec: dict | None, grouped: bool, SKILL_TO_GROUPS: dict[str, list[str]], SKILL_GROUPS: dict[str, list[str]]) -> dict:
    """
    Returns a merged vector:
    - If grouped: keys are group labels
    - If ungrouped: keys are per-skill; Likert (group) is projected to skills by averaging groups a skill belongs to
    """
    if likert_grouped_vec is None:
        return resp_vec

    if grouped:
        out = {}
        for g in SKILL_GROUPS.keys():
            rv = resp_vec.get(g, None)
            lv = likert_grouped_vec.get(g, None)
            if rv is not None and lv is not None:
                out[g] = (rv + lv) / 2.0
            elif rv is not None:
                out[g] = rv
            else:
                out[g] = lv
        return out
    else:
        # project group likert to each skill
        out = {}
        for s in resp_vec.keys():
            rv = resp_vec.get(s, None)
            groups = SKILL_TO_GROUPS.get(s, [])
            lik_vals = [likert_grouped_vec.get(g) for g in groups if likert_grouped_vec.get(g) is not None]
            lv = float(np.mean(lik_vals)) if lik_vals else None
            if rv is not None and lv is not None:
                out[s] = (rv + lv) / 2.0
            elif rv is not None:
                out[s] = rv
            else:
                out[s] = lv
        return out

# ------------------- Mongo -------------------
def _get_secret(name: str) -> str | None:
    try:
        val = st.secrets.get(name)
        if val is not None:
            return str(val)
    except Exception:
        pass
    return os.getenv(name)

def _build_uri(db_name: str | None) -> str | None:
    user = _get_secret("MONGO_USER")
    pw = _get_secret("MONGO_PASS")
    cluster = _get_secret("MONGO_CLUSTER")
    if not (user and pw and cluster):
        return None
    return f"mongodb+srv://{quote_plus(user)}:{quote_plus(pw)}@{cluster}/{db_name}?retryWrites=true&w=majority&tls=true&tlsAllowInvalidCertificates=true"

@st.cache_resource(show_spinner=False)
def _client(uri: str):
    return MongoClient(uri, serverSelectionTimeoutMS=10000)

def mongo_distinct(uri: str, db: str, coll: str, field: str) -> List[str]:
    if not uri:
        return []
    try:
        return sorted([v for v in _client(uri)[db][coll].distinct(field) if isinstance(v, str) and v.strip()])
    except Exception:
        return []

def mongo_records(uri: str, db: str, coll: str, student: str | None, source: str | None) -> List[dict]:
    if not uri:
        return []
    q = {}
    if student and student != "(All)":
        q["student"] = student
    if source and source != "(All)":
        q["source"] = source
    try:
        docs = list(_client(uri)[db][coll].find(q, {"_id": 0, "student": 1, "source": 1, "skills": 1}))
        rows = []
        for d in docs:
            base = {"student": str(d.get("student", "")), "source": str(d.get("source", ""))}
            for k in SKILLS:
                base[k] = to_01_or_nan((d.get("skills") or {}).get(k, np.nan))
            rows.append(base)
        return rows
    except Exception:
        return []

# ---------- Likert helpers ----------
def _norm_01(v):
    try:
        return max(0.0, min(1.0, float(v) / 5.0 if float(v) > 1 else float(v)))
    except Exception:
        return None

def mongo_get_likert_grouped(uri: str, db: str, coll: str, student: str, stage: str) -> dict:
    if not (uri and student and stage):
        return {}
    try:
        doc = _client(uri)[db][coll].find_one({"student_name": student, "stage": stage}, {"_id": 0, "average_skill_scores": 1})
        avg = (doc or {}).get("average_skill_scores") or {}
        return {g: _norm_01(avg.get(g)) for g in SKILL_GROUPS.keys()}
    except Exception:
        return {}
        
# ---- Analyses (Markdown) helpers ----
ANALYSES_DIR = os.getenv("ANALYSES_DIR", "student_analyses")  # folder in your HF Space

def _normalize_name(s: str) -> str:
    # Lower, remove non-alphanumerics, collapse spaces/underscores
    import re, unicodedata
    s = unicodedata.normalize("NFKC", s or "").strip().lower()
    s = re.sub(r"[^\w\s]", "", s)
    s = re.sub(r"[\s_]+", " ", s).strip()
    return s

@st.cache_data(show_spinner=False)
def _build_analysis_index(analyses_dir: str) -> dict:
    """Return dict: normalized_name -> file_path for *.md under analyses_dir."""
    import os, glob
    index = {}
    if not os.path.isdir(analyses_dir):
        return index
    for path in glob.glob(os.path.join(analyses_dir, "*.md")):
        base = os.path.splitext(os.path.basename(path))[0]  # "Student_Name"
        # accept both "Student Name" and "Student_Name" as same
        norm = _normalize_name(base.replace("_", " "))
        index[norm] = path
    return index

@st.cache_data(show_spinner=False)
def _load_markdown(path: str) -> str:
    try:
        with open(path, "r", encoding="utf-8") as f:
            return f.read()
    except Exception:
        return ""

# ------------------- UI -------------------
st.title("📊 Student Skill Radar")

with st.sidebar:
    db_name = st.text_input("Database name", value="student_skills")
    coll_name = st.text_input("Collection name", value="responses_IFE_2025")
    summaries_coll = st.text_input("Likert summaries collection", value="likert_summaries_IFE_2025")

    mongo_uri = _build_uri(db_name)
    students = ["(All)"] + (mongo_distinct(mongo_uri, db_name, coll_name, "student") if mongo_uri else [])
    sources = ["(All)"] + (mongo_distinct(mongo_uri, db_name, coll_name, "source") if mongo_uri else [])

    student_choice = st.selectbox("Select student", students)
    source_choice = st.selectbox("Select source/week", sources)
    # start_dt = st.date_input("Start date", value=None)
    # end_dt = st.date_input("End date", value=None)
    grouped = st.toggle("Grouped skills", value=True)
    overlay_sources = st.toggle("Overlay all sources when '(All)' selected", value=False)
    chart_title = st.text_input("Chart title", value="")



# start_str = start_dt.strftime("%Y-%m-%d") if isinstance(start_dt, date) else None
# end_str = end_dt.strftime("%Y-%m-%d") if isinstance(end_dt, date) else None

# ------------------- Fetch + merge -------------------
records = mongo_records(mongo_uri, db_name, coll_name, student_choice, source_choice) if mongo_uri else []
df_raw = pd.DataFrame(records) if records else pd.DataFrame()

if not df_raw.empty:
    df_raw["label"] = df_raw["student"].astype(str) + " — " + df_raw["source"].astype(str)
    df_resp = df_raw.groupby("label", dropna=False)[SKILLS].mean().reset_index()
    if grouped:
        df_resp = df_to_grouped(df_resp)
else:
    df_resp = pd.DataFrame()

# ---------- Merge Likert scores (works for grouped and ungrouped) ----------
from statistics import mean

# map each skill to the group(s) it belongs to (almost always one)
SKILL_TO_GROUPS = {s: [g for g, members in SKILL_GROUPS.items() if s in members] for s in SKILLS}

def _likert_for_skill(likert_grouped: dict, skill: str) -> float | None:
    groups = SKILL_TO_GROUPS.get(skill, [])
    vals = [likert_grouped.get(g) for g in groups if likert_grouped.get(g) is not None]
    return mean(vals) if vals else None

if not df_resp.empty and summaries_coll:
    merged_rows = []

    # choose which columns we're merging
    cols = list(SKILL_GROUPS.keys()) if grouped else SKILLS

    for _, r in df_resp.iterrows():
        label = str(r["label"])
        student, stage = label.split(" — ", 1) if " — " in label else (label, None)
        stage = SOURCE_TO_STAGE.get(stage.strip()) if stage else None

        # only onboarding/closing have Likert summaries
        likert_g = (
            mongo_get_likert_grouped(mongo_uri, db_name, summaries_coll, student.strip(), stage)
            if stage in ("onboarding", "closing") else {}
        )

        out = {"label": label}

        if grouped:
            # combine grouped columns directly
            for g in SKILL_GROUPS.keys():
                resp_val = None if pd.isna(r.get(g)) else float(r.get(g))
                likert_val = likert_g.get(g, None)
                if resp_val is not None and likert_val is not None:
                    out[g] = (resp_val + likert_val) / 2.0
                elif resp_val is not None:
                    out[g] = resp_val
                elif likert_val is not None:
                    out[g] = likert_val
                else:
                    out[g] = np.nan
        else:
            # map group Likert down to each skill, then combine
            for s in SKILLS:
                resp_val = None if pd.isna(r.get(s)) else float(r.get(s))
                likert_val = _likert_for_skill(likert_g, s)
                if resp_val is not None and likert_val is not None:
                    out[s] = (resp_val + likert_val) / 2.0
                elif resp_val is not None:
                    out[s] = resp_val
                elif likert_val is not None:
                    out[s] = likert_val
                else:
                    out[s] = np.nan

        merged_rows.append(out)

    df_final = pd.DataFrame(merged_rows, columns=["label"] + cols)
else:
    df_final = df_resp


# Overlay mode
# if grouped and not df_final.empty and source_choice == "(All)" and not overlay_sources:
#     df_final["_student"] = df_final["label"].apply(lambda s: s.split(" — ", 1)[0])
#     df_final = df_final.groupby("_student", dropna=False)[list(SKILL_GROUPS.keys())].mean().reset_index()
#     df_final = df_final.rename(columns={"_student": "label"})
# ---------------- Overlay vs Combine ----------------
if not df_final.empty and source_choice == "(All)":
    if overlay_sources:
        # Overlay ON → keep one line per source (do nothing)
        pass
    else:
        # Overlay OFF → combine all sources into one line per student
        df_final["_student"] = df_final["label"].apply(lambda s: s.split(" — ", 1)[0])

        if grouped:
            cols = list(SKILL_GROUPS.keys())
        else:
            cols = SKILLS

        df_final = (
            df_final
            .groupby("_student", dropna=False)[cols]
            .mean()
            .reset_index()
            .rename(columns={"_student": "label"})
        )

# ------------------- Output -------------------
# fig = plot_radar(df_final, grouped, chart_title)
# st.plotly_chart(fig, use_container_width=True)
# ============== Build per-stage vectors for comparisons (LIKERT-AWARE) ==============

# Columns to use based on mode
COLS = list(SKILL_GROUPS.keys()) if grouped else SKILLS

# Map each skill to its group(s) once (used to project group Likert down to skills)
SKILL_TO_GROUPS = {s: [g for g, members in SKILL_GROUPS.items() if s in members] for s in SKILLS}

def _project_likert_to_cols(likert_grouped: dict | None, cols: list[str], grouped_flag: bool) -> dict:
    """Return a vector aligned to COLS from group-level Likert. If ungrouped, project to skills."""
    if not likert_grouped:
        return {k: None for k in cols}
    if grouped_flag:
        return {k: (likert_grouped.get(k) if k in likert_grouped else None) for k in cols}
    # ungrouped → average the groups a skill belongs to
    out = {}
    for s in cols:
        gs = SKILL_TO_GROUPS.get(s, [])
        vals = [likert_grouped.get(g) for g in gs if likert_grouped.get(g) is not None]
        out[s] = float(np.mean(vals)) if vals else None
    return out

def _merge_resp_and_likert(resp_vec: dict, likert_vec: dict) -> dict:
    """Average where both exist; else take whichever exists."""
    out = {}
    for k in resp_vec.keys():
        rv = resp_vec.get(k, None)
        lv = likert_vec.get(k, None)
        if rv is not None and lv is not None:
            out[k] = (rv + lv) / 2.0
        elif rv is not None:
            out[k] = rv
        else:
            out[k] = lv
    return out

def _mean_vectors(vecs: list[dict]) -> dict:
    """Element-wise mean ignoring None; returns None if all Nones for a key."""
    if not vecs:
        return {}
    keys = list(vecs[0].keys())
    out = {}
    for k in keys:
        vals = [v.get(k) for v in vecs if v.get(k) is not None]
        out[k] = (float(np.mean(vals)) if vals else None)
    return out

def _resp_mean_for_sources(df_src: pd.DataFrame, student: str | None, sources: list[str], cols: list[str]) -> dict:
    """Mean of response scores across docs for (student,sources). If student None → cohort."""
    if df_src.empty:
        return {k: None for k in cols}
    sub = df_src.copy()
    if student:
        sub = sub[sub["student"] == student]
    sub = sub[sub["source"].isin(sources)]
    if sub.empty:
        return {k: None for k in cols}
    m = sub[cols].mean(numeric_only=True)
    return {k: (None if pd.isna(m.get(k)) else float(m.get(k))) for k in cols}

def _likert_grouped_for_student_stage(student: str, stage: str) -> dict | None:
    """Get normalized (0–1) group-level Likert for onboarding/closing only."""
    if stage not in ("onboarding", "closing"):
        return None
    lg = mongo_get_likert_grouped(mongo_uri, db_name, summaries_coll, student, stage)
    return lg if lg else None

def _student_stage_vectors(df_src: pd.DataFrame, stu: str, cols: list[str], grouped_flag: bool) -> dict:
    """Per-student vectors with Likert merged for onboarding/closing; combined includes closing(merged)."""
    # Onboarding = RESP(onboarding) ⊕ Likert(onboarding)
    onb_resp = _resp_mean_for_sources(df_src, stu, ["onboarding_responses"], cols)
    onb_lik  = _project_likert_to_cols(_likert_grouped_for_student_stage(stu, "onboarding"), cols, grouped_flag)
    onb = _merge_resp_and_likert(onb_resp, onb_lik)

    # Closing = RESP(closing) ⊕ Likert(closing)
    cls_resp = _resp_mean_for_sources(df_src, stu, ["closing_responses"], cols)
    cls_lik  = _project_likert_to_cols(_likert_grouped_for_student_stage(stu, "closing"), cols, grouped_flag)
    cls = _merge_resp_and_likert(cls_resp, cls_lik)

    # Combined = mean( RESP(week2), RESP(week3), CLOSING(merged) )
    w2 = _resp_mean_for_sources(df_src, stu, ["week_2_responses"], cols)
    w3 = _resp_mean_for_sources(df_src, stu, ["week_3_responses"], cols)
    combo = _mean_vectors([w2, w3, cls])  # <- note: closing already merged with Likert

    return {"onboarding": onb, "closing": cls, "combined": combo}

def _stage_vectors_for_current_selection(df_src: pd.DataFrame, student_choice: str | None, cols: list[str], grouped_flag: bool) -> dict:
    """
    If a student is selected → return their vectors.
    If cohort (“(All)”) → average per-student vectors (Likert included where available).
    """
    if student_choice and student_choice != "(All)":
        return _student_stage_vectors(df_src, student_choice, cols, grouped_flag)

    # Cohort: compute for each student then average
    if df_src.empty:
        empty_vec = {k: None for k in cols}
        return {"onboarding": empty_vec, "closing": empty_vec, "combined": empty_vec}

    students = sorted(set(str(x) for x in df_src["student"].dropna().unique()))
    per_student = [_student_stage_vectors(df_src, s, cols, grouped_flag) for s in students]
    return {
        "onboarding": _mean_vectors([p["onboarding"] for p in per_student]),
        "closing":    _mean_vectors([p["closing"]    for p in per_student]),
        "combined":   _mean_vectors([p["combined"]   for p in per_student]),
    }

def _percent_change(new: float | None, old: float | None) -> float | None:
    if new is None or old is None:
        return None
    if old == 0:
        return None  # or return 100.0 if you prefer
    return (new - old) / old * 100.0

# Use df_raw (one row per doc) so overlay/aggregation doesn’t hide sources
# Ensure df_raw has the per-skill or per-group columns we need:
if grouped and not df_raw.empty:
    # build grouped view just for comparisons
    df_grouped_for_comp = df_raw.copy()
    # aggregate per-doc row to grouped columns
    df_grouped_for_comp = (
        df_grouped_for_comp
        .assign(**{
            g: df_grouped_for_comp.apply(lambda r: safe_mean([r.get(s, np.nan) for s in SKILL_GROUPS[g]]), axis=1)
            for g in SKILL_GROUPS.keys()
        })
    )
    df_src_for_comp = df_grouped_for_comp[["student", "source"] + list(SKILL_GROUPS.keys())]
else:
    df_src_for_comp = df_raw  # already per-skill

stage_vecs = _stage_vectors_for_current_selection(df_src_for_comp, student_choice, COLS, grouped)
vec_onb   = stage_vecs["onboarding"]
vec_cls   = stage_vecs["closing"]
vec_combo = stage_vecs["combined"]

pct_onb_to_cls   = {k: _percent_change(vec_cls.get(k),   vec_onb.get(k)) for k in COLS}
pct_onb_to_combo = {k: _percent_change(vec_combo.get(k), vec_onb.get(k)) for k in COLS}

# ------------------- Plot + table above stays the same -------------------
df_plot = df_final.copy()
avg_label = None

if not df_plot.empty:
    cols = list(SKILL_GROUPS.keys()) if grouped else SKILLS
    show_cohort_avg = st.toggle("Show cohort average (all students)", value=True)

    if show_cohort_avg:
        avg_vals = df_plot[cols].mean()
        avg_row = {"label": "Average (All Students)"}
        avg_row.update({k: float(avg_vals[k]) for k in cols})
        df_plot = pd.concat([df_plot, pd.DataFrame([avg_row])], ignore_index=True)
        avg_label = "Average (All Students)"

fig = plot_radar(df_plot, grouped, chart_title, avg_label=avg_label)
st.plotly_chart(fig, use_container_width=True)

st.caption(f"{len(df_final)} line(s) aggregated." if not df_final.empty else "No data.")



# ================== Dynamic Stage Summaries (only if student answered that week) ==================
import re
import unicodedata
from collections import Counter
from difflib import SequenceMatcher
import math
# Stage <-> Source mapping
STAGE_TO_SOURCE = {
    "onboarding": "onboarding_responses",
    "week_2": "week_2_responses",
    "week_3": "week_3_responses",
    "closing": "closing_responses",  # future-proof
}
SOURCE_TO_STAGE = {v: k for k, v in STAGE_TO_SOURCE.items()}

def _answer_total_score(resp: dict) -> float:
    skills = resp.get("skills") or {}
    total = 0.0
    for v in skills.values():
        try:
            total += float(v)
        except Exception:
            pass
    return total

def _responses_for_student_stage(uri, db, responses_coll, student: str, stage: str) -> list[dict]:
    """Return responses for a student at a stage (mapped to source) with non-empty answers."""
    if not (uri and student and stage):
        return []
    src = STAGE_TO_SOURCE.get(stage)
    if not src:
        return []
    try:
        c = _client(uri)
        docs = list(c[db][responses_coll].find(
            {"student": student, "source": src},
            {"_id": 0, "answer": 1, "skills": 1}
        ))
        # keep only responses with a non-empty answer
        return [d for d in docs if (d.get("answer") or "").strip()]
    except Exception:
        return []

def _normalize_quotes_spaces(s: str) -> str:
    if not s:
        return ""
    s = unicodedata.normalize("NFKC", s)
    s = s.replace("…", "...")
    s = re.sub(r"\s+", " ", s).strip()
    return s

def _clean_tokens(s: str) -> list[str]:
    s = _normalize_quotes_spaces(s).lower()
    # keep letters/digits/spaces; drop punctuation
    s = re.sub(r"[^\w\s]", " ", s)
    s = re.sub(r"\s+", " ", s).strip()
    return s.split()

def _vectorize(tokens: list[str]) -> Counter:
    return Counter(tokens)

def _cosine_sim(a: Counter, b: Counter) -> float:
    if not a or not b:
        return 0.0
    # dot
    dot = sum(a[k] * b.get(k, 0) for k in a)
    # norms
    na = math.sqrt(sum(v*v for v in a.values()))
    nb = math.sqrt(sum(v*v for v in b.values()))
    if na == 0.0 or nb == 0.0:
        return 0.0
    return dot / (na * nb)

def _seq_ratio(a: str, b: str) -> float:
    # SequenceMatcher returns 0..1
    return SequenceMatcher(None, a, b).ratio()

def _best_full_answer_for_quote(q: str, responses: list[dict]) -> str | None:
    """
    Return the best-matching full answer for a (possibly truncated/middle) quote.
    Uses semantic similarity: 0.6*cosine(token) + 0.4*SequenceMatcher.
    If multiple tie, picks the one with HIGHEST total skill score.
    """
    q_norm = _normalize_quotes_spaces(q)
    q_clean = _normalize_quotes_spaces(q).lower()
    q_tokens = _clean_tokens(q_norm)
    q_vec = _vectorize(q_tokens)

    best = None  # (combined_score, skill_total, full_answer)
    for r in responses:
        full = (r.get("answer") or "").strip()
        if not full:
            continue
        full_norm = _normalize_quotes_spaces(full)
        full_clean = full_norm.lower()
        full_tokens = _clean_tokens(full_norm)
        full_vec = _vectorize(full_tokens)

        cos = _cosine_sim(q_vec, full_vec)
        seq = _seq_ratio(q_clean, full_clean)
        combined = 0.6 * cos + 0.4 * seq

        # small boost if the normalized quote substring appears (cheap heuristic)
        if q_clean and q_clean in full_clean:
            combined += 0.05

        # compute skill total for tie-break
        skills = r.get("skills") or {}
        skill_total = 0.0
        for v in skills.values():
            try:
                skill_total += float(v)
            except Exception:
                pass

        cand = (combined, skill_total, full)
        if (best is None) or (cand[0] > best[0]) or (cand[0] == best[0] and cand[1] > best[1]):
            best = cand

    # Threshold so we don't replace with a bad match; tweak 0.45–0.65 as needed
    if best and best[0] >= 0.5:
        return best[2]
    return None

def _fix_cutoff_quotes(quotes: list[str], responses: list[dict]) -> list[str]:
    """
    Replace truncated/middle quotes with the best-matching full answer from `responses`
    (already filtered to student+stage). If no decent semantic match, keep original.
    """
    if not quotes:
        return []
    out = []
    for q in quotes:
        q_raw = (q or "").strip()
        if not q_raw:
            continue
        # If it looks truncated (ellipsis) OR is short, try semantic match
        looks_truncated = ("..." in q_raw) or (len(q_raw) < 100)
        if looks_truncated:
            full = _best_full_answer_for_quote(q_raw, responses)
            out.append(full if full else q_raw)
        else:
            out.append(q_raw)
    return out


def _top3_answers_by_skill_sum(responses: list[dict]) -> list[str]:
    """Pick up to 3 answers with the highest total skill score."""
    scored = []
    for r in responses:
        ans = (r.get("answer") or "").strip()
        if not ans:
            continue
        total = _answer_total_score(r)
        scored.append((total, ans))
    scored.sort(key=lambda x: x[0], reverse=True)
    return [ans for _, ans in scored[:3]]

def fetch_student_stage_summary(
    uri: str,
    db: str,
    summaries_coll: str,
    responses_coll: str,
    student: str,
    stage: str
):
    """
    Return summary dict for a student+stage ONLY if the student has responses for that week.
    Otherwise, return None (so we don't render the panel).
    """
    # 1) Require that the student answered that week (source derived from stage)
    responses = _responses_for_student_stage(uri, db, responses_coll, student, stage)
    if not responses:
        return None

    # 2) Pull summary doc (patterns nested)
    patterns = {}
    top_strengths = []
    notable_quotes = []
    try:
        c = _client(uri)
        doc = c[db][summaries_coll].find_one(
            {"student_name": student, "stage": stage},
            {"_id": 0, "patterns": 1, "top_strengths": 1, "notable_quotes": 1}
        ) or {}
        patterns = doc.get("patterns") or {}
        top_strengths = doc.get("top_strengths") or []
        notable_quotes = doc.get("notable_quotes") or []
    except Exception:
        pass

    most_consistent = patterns.get("most_consistent")
    most_developed = patterns.get("most_developed")

    # 3) Repair cut-off quotes; if none after fixing, fallback to top 3 highest-scoring answers
    notable_quotes = _fix_cutoff_quotes(notable_quotes, responses)
    if not notable_quotes:
        notable_quotes = _top3_answers_by_skill_sum(responses)

    return {
        "most_consistent": most_consistent,
        "most_developed": most_developed,
        "top_strengths": top_strengths,
        "notable_quotes": notable_quotes,
    }


# # ------------------- Output (Tabs) -------------------
# tab_summary, tab_analyses, tab_compare = st.tabs(["📈 Summary", "📝 Analyses","📊 Comparisons"])

tabs = st.tabs(["📈 Summary", "📝 Analyses", "📊 Comparisons"])
with tabs[0]:
# ---------- Render the summary panel dynamically ----------
    if mongo_uri and student_choice != "(All)" and source_choice != "(All)":
        stage = SOURCE_TO_STAGE.get(source_choice.strip())
        if stage:
            # set to your actual summaries collection name
            summaries_coll_name = "summaries_IFE_2025"
            summary = fetch_student_stage_summary(
                mongo_uri, db_name, summaries_coll_name, coll_name,
                student=student_choice, stage=stage
            )
            if summary:
                st.markdown("---")
                st.subheader(f"Summary — {student_choice} ({stage.replace('_', ' ').title()})")
                c1, c2 = st.columns(2)
                with c1:
                    st.markdown(f"**Most Consistent:** {summary.get('most_consistent') or '—'}")
                    st.markdown(f"**Most Developed:** {summary.get('most_developed') or '—'}")
                with c2:
                    strengths = summary.get("top_strengths") or []
                    st.markdown("**Top Strengths:** " + (", ".join(strengths) if strengths else "—"))
    
                st.markdown("**Notable Quotes:**")
                for q in (summary.get("notable_quotes") or [])[:3]:
                    st.markdown(f"> {q}")

with tabs[1]:
    st.subheader("Student Analysis")

    # Use the folder you defined at top (ANALYSES_DIR), or expose it in the sidebar if you prefer.
    idx = _build_analysis_index(ANALYSES_DIR)

    if student_choice == "(All)":
        st.info("Pick a specific student on the left to view their analysis.")
        # (Optional) show what's available so you can browse:
        if idx:
            st.caption("Available analyses:")
            st.write(", ".join(sorted({name.title() for name in idx.keys()})))
            file_path="full_class_summary.md"
            full_summary=_load_markdown(file_path)
            if full_summary.strip():
                st.markdown(full_summary, unsafe_allow_html=False)
                # Optional download button
                with open(file_path, "rb") as f:
                    st.download_button(
                        "Download analysis (.md)", f,
                        file_name=os.path.basename(file_path), mime="text/markdown"
                    )
            else:
                st.warning("Analysis file found but empty.")

    else:
        # Normalize the selected student name to match filenames
        norm = _normalize_name(student_choice)
        path = idx.get(norm)

        # If exact match not found, try simple underscore variant
        if not path:
            alt = student_choice.replace(" ", "_")
            path = idx.get(_normalize_name(alt))

        if path:
            md = _load_markdown(path)
            if md.strip():
                st.markdown(md, unsafe_allow_html=False)
                system = '''### 🔵🔵 Skill Indicator System
    
    | Symbol  | Meaning                                      |
    |---------|----------------------------------------------|
    | 🔵      | Clear evidence of the skill that week        |
    | 🔵🔵    | Strong or standout performance that week     |
    | ⚪⚪     | Little to no evidence for that skill that week|
    
    '''
                st.markdown(system)
                # Optional download button
                with open(path, "rb") as f:
                    st.download_button(
                        "Download analysis (.md)", f,
                        file_name=os.path.basename(path), mime="text/markdown"
                    )

            else:
                st.warning("Analysis file found but empty.")
        else:
            st.warning(f"No analysis found for **{student_choice}** in `{ANALYSES_DIR}` yet.")
            if idx:
                st.caption("Available analyses:")
                st.write(", ".join(sorted({name.title() for name in idx.keys()})))


with tabs[2]:
    st.subheader("Onboarding vs Closing — % Change")
    df1 = pd.DataFrame({
        "Dimension": COLS,
        "Onboarding": [vec_onb.get(k) for k in COLS],
        "Closing": [vec_cls.get(k) for k in COLS],
        "% Change": [pct_onb_to_cls.get(k) for k in COLS],
    })
    st.dataframe(df1.style.format({"Onboarding": "{:.2f}", "Closing": "{:.2f}", "% Change": "{:+.1f}%"}), use_container_width=True)

    st.subheader("Onboarding vs (Week2+Week3+Closing) — % Change")
    df2 = pd.DataFrame({
        "Dimension": COLS,
        "Onboarding": [vec_onb.get(k) for k in COLS],
        "Weeks 2+3+Closing (combined)": [vec_combo.get(k) for k in COLS],
        "% Change": [pct_onb_to_combo.get(k) for k in COLS],
    })
    st.dataframe(df2.style.format({"Onboarding": "{:.2f}", "Weeks 2+3+Closing (combined)": "{:.2f}", "% Change": "{:+.1f}%"}), use_container_width=True)

    # Optional bar chart: % change Onboarding -> Closing
    try:
        fig_delta = go.Figure()
        fig_delta.add_bar(x=COLS, y=[pct_onb_to_cls.get(k) if pct_onb_to_cls.get(k) is not None else 0 for k in COLS], name="%Δ Onb→Closing")
        fig_delta.update_layout(title="% Change: Onboarding → Closing", xaxis_title="Dimension", yaxis_title="% change", margin=dict(l=20, r=20, t=50, b=20))
        st.plotly_chart(fig_delta, use_container_width=True)
    except Exception:
        pass


# # app.py — Student Skill Radar (MongoDB, secrets-based, no CSV)
# import os
# from datetime import date
# from typing import Dict, List

# import numpy as np
# import pandas as pd
# import plotly.graph_objects as go
# import streamlit as st
# from pymongo import MongoClient
# from urllib.parse import quote_plus

# st.set_page_config(page_title="Student Skill Radar", layout="wide")

# # ------------------- Constants -------------------
# SKILLS = [
#     "Problem-Solving",
#     "Critical Thinking",
#     "Analytical Reasoning",
#     "Adaptability",
#     "Continuous Learning",
#     "Creativity",
#     "Communication",
#     "Collaboration",
#     "Community Engagement",
#     "Emotional Intelligence",
#     "Ethical Decision-Making",
#     "Time Management",
#     "Tech Aptitude",
# ]

# SKILL_GROUPS = {
#     "Problem-Solving, Critical Thinking, Analytical Reasoning": [
#         "Problem-Solving", "Critical Thinking", "Analytical Reasoning"
#     ],
#     "Adaptability, Continuous Learning, Creativity": [
#         "Adaptability", "Continuous Learning", "Creativity"
#     ],
#     "Time Management": ["Time Management"],
#     "Communication, Teamwork, Collaboration, Community Engagement": [
#         "Communication", "Collaboration", "Community Engagement"
#     ],
#     "Emotional Intelligence, Ethical Decision Making": [
#         "Emotional Intelligence", "Ethical Decision-Making"
#     ],
#     "Tech Aptitude": ["Tech Aptitude"],
# }

# # ------------------- Helpers -------------------
# def safe_mean(vals):
#     clean = [v for v in vals if v is not None and not pd.isna(v)]
#     return float(np.mean(clean)) if clean else np.nan

# def to_01_or_nan(x):
#     try:
#         v = float(x)
#     except Exception:
#         return np.nan
#     if pd.isna(v):
#         return np.nan
#     return max(0.0, min(1.0, v))

# def aggregate_groups_row(row: pd.Series) -> Dict[str, float]:
#     return {
#         g: safe_mean([row.get(s, np.nan) for s in members])
#         for g, members in SKILL_GROUPS.items()
#     }

# def summarize(records: List[dict], level: str = "student") -> pd.DataFrame:
#     df = pd.DataFrame(records) if records else pd.DataFrame()
#     if df.empty:
#         return df
#     if level == "student+source":
#         df["label"] = df["student"].astype(str) + " — " + df["source"].astype(str)
#     else:
#         df["label"] = df["student"].astype(str)
#     # groupby mean skips NaNs by default
#     return df.groupby("label", dropna=False)[SKILLS].mean().reset_index()

# def plot_radar(df: pd.DataFrame, grouped: bool, title: str):
#     if df.empty:
#         return go.Figure()

#     traces = []
#     if grouped:
#         labels = list(SKILL_GROUPS.keys())
#         for _, r in df.iterrows():
#             grp = aggregate_groups_row(r)
#             values = [0.0 if pd.isna(grp[k]) else float(grp[k]) for k in labels]
#             traces.append(go.Scatterpolar(
#                 r=values + [values[0]],
#                 theta=labels + [labels[0]],
#                 name=r["label"],
#                 fill="toself",
#             ))
#     else:
#         labels = SKILLS
#         for _, r in df.iterrows():
#             values = []
#             for k in SKILLS:
#                 v = r.get(k, np.nan)
#                 values.append(0.0 if pd.isna(v) else float(v))
#             traces.append(go.Scatterpolar(
#                 r=values + [values[0]],
#                 theta=labels + [labels[0]],
#                 name=r["label"],
#                 fill="toself",
#             ))

#     fig = go.Figure(traces)
#     fig.update_layout(
#         title=title or "Skill Radar",
#         showlegend=True,
#         polar=dict(
#             radialaxis=dict(
#                 autorange=False,
#                 range=[0, 1],
#                 tick0=0,
#                 dtick=0.2,
#                 ticks="outside",
#                 showline=True,
#                 showgrid=True,
#                 visible=True,
#             )
#         ),
#         margin=dict(l=30, r=30, t=60, b=30),
#     )
#     return fig

# # ------------------- Mongo Access (secrets-only) -------------------
# def _get_secret(name: str) -> str | None:
#     try:
#         val = st.secrets.get(name)
#         if val is not None:
#             return str(val)
#     except Exception:
#         pass
#     return os.getenv(name)

# def _build_uri(db_name: str | None) -> str | None:
#     user = _get_secret("MONGO_USER")
#     pw = _get_secret("MONGO_PASS")
#     cluster = _get_secret("MONGO_CLUSTER")
#     if not (user and pw and cluster):
#         return None
#     user_q = quote_plus(user)
#     pw_q = quote_plus(pw)
#     db_path = f"/{db_name}" if db_name else ""
#     return (
#         f"mongodb+srv://{user_q}:{pw_q}@{cluster}{db_path}"
#         f"?retryWrites=true&w=majority&tls=true&tlsAllowInvalidCertificates=true"
#     )

# @st.cache_resource(show_spinner=False)
# def _client(uri: str):
#     return MongoClient(uri, serverSelectionTimeoutMS=10000)

# # @st.cache_data(show_spinner=False)
# def mongo_distinct(uri: str, db: str, coll: str, field: str) -> List[str]:
#     if not uri:
#         return []
#     try:
#         c = _client(uri)
#         vals = c[db][coll].distinct(field)
#         return sorted([v for v in vals if isinstance(v, str) and v.strip()])
#     except Exception:
#         return []

# # @st.cache_data(show_spinner=False)
# def mongo_records(
#     uri: str,
#     db: str,
#     coll: str,
#     student: str | None,
#     source: str | None,
#     start: str | None,
#     end: str | None,
# ) -> List[dict]:
#     """Return flat rows with one column per skill; missing skills -> NaN (ignored in means)."""
#     if not uri:
#         return []
#     q = {}
#     if student and student != "(All)":
#         q["student"] = student
#     if source and source != "(All)":
#         q["source"] = source
#     if start or end:
#         q["date"] = {}
#         if start:
#             q["date"]["$gte"] = start
#         if end:
#             q["date"]["$lte"] = end
#     try:
#         c = _client(uri)
#         proj = {"_id": 0, "student": 1, "source": 1, "date": 1, "skills": 1}
#         docs = list(c[db][coll].find(q, proj))
#         rows = []
#         for d in docs:
#             base = {
#                 "student": str(d.get("student", "")),
#                 "source": str(d.get("source", "")),
#                 "date": str(d.get("date", "")),
#             }
#             sd = d.get("skills") or {}
#             for k in SKILLS:
#                 base[k] = to_01_or_nan(sd.get(k, np.nan))
#             rows.append(base)
#         return rows
#     except Exception:
#         return []

# # ------------------- UI -------------------
# st.title("📊 Student Skill Radar")

# with st.sidebar:
#     st.subheader("MongoDB Settings")
#     db_name = st.text_input("Database name", value="student_skills")
#     coll_name = st.text_input("Collection name", value="responses_IFE_2025")

#     mongo_uri = _build_uri(db_name)

#     if not mongo_uri:
#         st.warning("Missing MONGO_USER, MONGO_PASS, or MONGO_CLUSTER in secrets/env.")
#     else:
#         try:
#             _client(mongo_uri).admin.command("ping")
#             st.success("Connected via secrets ✅")
#         except Exception as e:
#             st.error(f"Mongo connection failed: {e}")

#     # Filters
#     students = ["(All)"] + (mongo_distinct(mongo_uri, db_name, coll_name, "student") if mongo_uri else [])
#     sources = ["(All)"] + (mongo_distinct(mongo_uri, db_name, coll_name, "source") if mongo_uri else [])

#     student_choice = st.selectbox("Select student", students)
#     source_choice = st.selectbox("Select source/week", sources)

#     c1, c2 = st.columns(2)
#     start_dt = c1.date_input("Start date", value=None)
#     end_dt = c2.date_input("End date", value=None)

#     agg_level = st.selectbox("Aggregation level", ["student", "student+source"], index=0)
#     grouped = st.toggle("Grouped skills (skill clusters)", value=True)
#     chart_title = st.text_input("Chart title", value="")

# # Convert dates to strings (YYYY-MM-DD)
# start_str = start_dt.strftime("%Y-%m-%d") if isinstance(start_dt, date) else None
# end_str = end_dt.strftime("%Y-%m-%d") if isinstance(end_dt, date) else None

# # Fetch + aggregate
# records = mongo_records(mongo_uri, db_name, coll_name, student_choice, source_choice, start_str, end_str) if mongo_uri else []
# df = summarize(records, level=agg_level) if records else pd.DataFrame()

# # ------------------- Output -------------------
# fig = plot_radar(df, grouped, chart_title)
# st.plotly_chart(fig, use_container_width=True)
# st.caption(f"{len(df)} line(s) aggregated." if not df.empty else "No data. Adjust filters or check Mongo connection.")