# app.py — ST_GeoMech_YM (mirrors your UCS GUI, adapted for Young's Modulus)
import io, json, os, base64, math
from pathlib import Path
import streamlit as st
import pandas as pd
import numpy as np
import joblib
from datetime import datetime
# Matplotlib for PREVIEW modal and the CROSS-PLOT (static)
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import plotly.graph_objects as go
from sklearn.metrics import mean_squared_error
# =========================
# Constants (Ym variant)
# =========================
APP_NAME = "ST_GeoMech_YM"
TAGLINE = "Real-Time Young's Modulus Tracking"
FEATURES = ["WOB(klbf)", "TORQUE(kft.lbf)", "SPP(psi)", "RPM(1/min)", "ROP(ft/h)", "Flow Rate, gpm"]
TARGET = "Actual Ym"
PRED_COL = "Ym_Pred"
MODELS_DIR = Path("models")
DEFAULT_MODEL = MODELS_DIR / "ym_rf.joblib"
MODEL_FALLBACKS = [MODELS_DIR / "model.joblib", MODELS_DIR / "model.pkl"]
COLORS = {"pred": "#1f77b4", "actual": "#f2b702", "ref": "#5a5a5a"}
# ---- Plot sizing controls ----
CROSS_W = 350
CROSS_H = 350
TRACK_H = 1000
TRACK_W = 500
FONT_SZ = 13
BOLD_FONT = "Arial Black, Arial, sans-serif"
# =========================
# Page / CSS
# =========================
st.set_page_config(page_title=APP_NAME, page_icon="logo.png", layout="wide")
st.markdown("""
""", unsafe_allow_html=True)
# Sticky helpers
st.markdown("""
""", unsafe_allow_html=True)
# Hide uploader helper text
st.markdown("""
""", unsafe_allow_html=True)
# Make the Preview expander title & tabs sticky (pinned to the top)
st.markdown("""
""", unsafe_allow_html=True)
# Center text in all pandas Styler tables
TABLE_CENTER_CSS = [
dict(selector="th", props=[("text-align", "center")]),
dict(selector="td", props=[("text-align", "center")]),
]
# Message box CSS
st.markdown("""
""", unsafe_allow_html=True)
# =========================
# Password gate
# =========================
def inline_logo(path="logo.png") -> str:
try:
p = Path(path)
if not p.exists(): return ""
return f"data:image/png;base64,{base64.b64encode(p.read_bytes()).decode('ascii')}"
except Exception:
return ""
def add_password_gate() -> None:
try:
required = st.secrets.get("APP_PASSWORD", "")
except Exception:
required = os.environ.get("APP_PASSWORD", "")
if not required:
st.warning("Set APP_PASSWORD in Secrets (or environment) and restart.")
st.stop()
if st.session_state.get("auth_ok", False):
return
st.sidebar.markdown(f"""
})
{APP_NAME}
Smart Thinking • Secure Access
""", unsafe_allow_html=True
)
pwd = st.sidebar.text_input("Access key", type="password", placeholder="••••••••")
if st.sidebar.button("Unlock", type="primary"):
if pwd == required:
st.session_state.auth_ok = True
st.rerun()
else:
st.error("Incorrect key.")
st.stop()
add_password_gate()
# =========================
# Utilities
# =========================
def rmse(y_true, y_pred) -> float:
return float(np.sqrt(mean_squared_error(y_true, y_pred)))
def pearson_r(y_true, y_pred) -> float:
a = np.asarray(y_true, dtype=float)
p = np.asarray(y_pred, dtype=float)
if a.size < 2: return float("nan")
# Guard constant series
if np.all(a == a[0]) or np.all(p == p[0]): return float("nan")
return float(np.corrcoef(a, p)[0, 1])
@st.cache_resource(show_spinner=False)
def load_model(model_path: str):
return joblib.load(model_path)
@st.cache_data(show_spinner=False)
def parse_excel(data_bytes: bytes):
bio = io.BytesIO(data_bytes)
xl = pd.ExcelFile(bio)
return {sh: xl.parse(sh) for sh in xl.sheet_names}
def read_book_bytes(b: bytes): return parse_excel(b) if b else {}
def _normalize_columns(df: pd.DataFrame) -> pd.DataFrame:
out = df.copy()
out.columns = [c.strip() for c in out.columns]
# Fix flow-rate typo variants
out = out.rename(columns={
"Fow Rate, gpm": "Flow Rate, gpm",
"Fow Rate, gpm ": "Flow Rate, gpm"
})
return out
def ensure_cols(df: pd.DataFrame, cols: list[str]) -> bool:
miss = [c for c in cols if c not in df.columns]
if miss:
st.error(f"Missing columns: {miss}\nFound: {list(df.columns)}")
return False
return True
def find_sheet(book, names):
low2orig = {k.lower(): k for k in book.keys()}
for nm in names:
if nm.lower() in low2orig: return low2orig[nm.lower()]
return None
def _nice_tick0(xmin: float, step: float = 0.1) -> float:
# Rounded start tick for continuous Ym scales (unit-agnostic)
return step * math.floor(xmin / step) if np.isfinite(xmin) else xmin
def df_centered_rounded(df: pd.DataFrame, hide_index=True):
out = df.copy()
numcols = out.select_dtypes(include=[np.number]).columns
styler = (
out.style
.format({c: "{:.2f}" for c in numcols})
.set_properties(**{"text-align": "center"})
.set_table_styles(TABLE_CENTER_CSS)
)
st.dataframe(styler, use_container_width=True, hide_index=hide_index)
# === Excel export helpers =================================================
def _excel_engine() -> str:
try:
import xlsxwriter # noqa: F401
return "xlsxwriter"
except Exception:
return "openpyxl"
def _excel_safe_name(name: str) -> str:
bad = '[]:*?/\\'
safe = ''.join('_' if ch in bad else ch for ch in str(name))
return safe[:31]
def mape(y_true, y_pred, eps: float = 1e-8) -> float:
a = np.asarray(y_true, dtype=float)
p = np.asarray(y_pred, dtype=float)
denom = np.where(np.abs(a) < eps, np.nan, np.abs(a)) # ignore near-zero actuals
return float(np.nanmean(np.abs(a - p) / denom) * 100.0) # percent
def _round_numeric(df: pd.DataFrame, ndigits: int = 3) -> pd.DataFrame:
out = df.copy()
for c in out.columns:
if pd.api.types.is_float_dtype(out[c]) or pd.api.types.is_integer_dtype(out[c]):
out[c] = pd.to_numeric(out[c], errors="coerce").round(ndigits)
return out
def _summary_table(df: pd.DataFrame, cols: list[str]) -> pd.DataFrame:
cols = [c for c in cols if c in df.columns]
if not cols:
return pd.DataFrame()
tbl = (df[cols]
.agg(['min','max','mean','std'])
.T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"})
.reset_index(names="Field"))
return _round_numeric(tbl, 3)
def _train_ranges_df(ranges: dict[str, tuple[float, float]]) -> pd.DataFrame:
if not ranges:
return pd.DataFrame()
df = pd.DataFrame(ranges).T.reset_index()
df.columns = ["Feature", "Min", "Max"]
return _round_numeric(df, 3)
def _excel_autofit(writer, sheet_name: str, df: pd.DataFrame, min_w: int = 8, max_w: int = 40):
"""Auto-fit columns when using xlsxwriter."""
try:
import xlsxwriter # noqa: F401
except Exception:
return
ws = writer.sheets[sheet_name]
for i, col in enumerate(df.columns):
series = df[col].astype(str)
max_len = max([len(str(col))] + series.map(len).tolist())
ws.set_column(i, i, max(min_w, min(max_len + 2, max_w)))
ws.freeze_panes(1, 0)
def _add_sheet(sheets: dict, order: list, name: str, df: pd.DataFrame, ndigits: int):
if df is None or df.empty:
return
sheets[name] = _round_numeric(df, ndigits)
order.append(name)
def _available_sections() -> list[str]:
"""Compute which sections exist (offered in the export dropdown)."""
res = st.session_state.get("results", {})
sections = []
if "Train" in res: sections += ["Training","Training_Metrics","Training_Summary"]
if "Test" in res: sections += ["Testing","Testing_Metrics","Testing_Summary"]
if "Validate" in res: sections += ["Validation","Validation_Metrics","Validation_Summary","Validation_OOR"]
if "PredictOnly" in res: sections += ["Prediction","Prediction_Summary"]
if st.session_state.get("train_ranges"): sections += ["Training_Ranges"]
sections += ["Info"]
return sections
def build_export_workbook(selected: list[str], ndigits: int = 3, do_autofit: bool = True) -> tuple[bytes|None, str|None, list[str]]:
"""Builds an in-memory Excel workbook for selected sheets; fixed rounding to 3 decimals."""
res = st.session_state.get("results", {})
if not res:
return None, None, []
sheets: dict[str, pd.DataFrame] = {}
order: list[str] = []
# Training
if "Training" in selected and "Train" in res:
_add_sheet(sheets, order, "Training", res["Train"], ndigits)
if "Training_Metrics" in selected and res.get("m_train"):
_add_sheet(sheets, order, "Training_Metrics", pd.DataFrame([res["m_train"]]), ndigits)
if "Training_Summary" in selected and "Train" in res:
tr_cols = FEATURES + [c for c in [TARGET, PRED_COL] if c in res["Train"].columns]
_add_sheet(sheets, order, "Training_Summary", _summary_table(res["Train"], tr_cols), ndigits)
# Testing
if "Testing" in selected and "Test" in res:
_add_sheet(sheets, order, "Testing", res["Test"], ndigits)
if "Testing_Metrics" in selected and res.get("m_test"):
_add_sheet(sheets, order, "Testing_Metrics", pd.DataFrame([res["m_test"]]), ndigits)
if "Testing_Summary" in selected and "Test" in res:
te_cols = FEATURES + [c for c in [TARGET, PRED_COL] if c in res["Test"].columns]
_add_sheet(sheets, order, "Testing_Summary", _summary_table(res["Test"], te_cols), ndigits)
# Validation
if "Validation" in selected and "Validate" in res:
_add_sheet(sheets, order, "Validation", res["Validate"], ndigits)
if "Validation_Metrics" in selected and res.get("m_val"):
_add_sheet(sheets, order, "Validation_Metrics", pd.DataFrame([res["m_val"]]), ndigits)
if "Validation_Summary" in selected and res.get("sv_val"):
_add_sheet(sheets, order, "Validation_Summary", pd.DataFrame([res["sv_val"]]), ndigits)
if "Validation_OOR" in selected and isinstance(res.get("oor_tbl"), pd.DataFrame) and not res["oor_tbl"].empty:
_add_sheet(sheets, order, "Validation_OOR", res["oor_tbl"].reset_index(drop=True), ndigits)
# Prediction
if "Prediction" in selected and "PredictOnly" in res:
_add_sheet(sheets, order, "Prediction", res["PredictOnly"], ndigits)
if "Prediction_Summary" in selected and res.get("sv_pred"):
_add_sheet(sheets, order, "Prediction_Summary", pd.DataFrame([res["sv_pred"]]), ndigits)
# Training ranges
if "Training_Ranges" in selected and st.session_state.get("train_ranges"):
rr = _train_ranges_df(st.session_state["train_ranges"])
_add_sheet(sheets, order, "Training_Ranges", rr, ndigits)
# Info
if "Info" in selected:
info = pd.DataFrame([
{"Key": "AppName", "Value": APP_NAME},
{"Key": "Tagline", "Value": TAGLINE},
{"Key": "Target", "Value": TARGET},
{"Key": "PredColumn", "Value": PRED_COL},
{"Key": "Features", "Value": ", ".join(FEATURES)},
{"Key": "ExportedAt", "Value": datetime.now().strftime("%Y-%m-%d %H:%M:%S")},
])
_add_sheet(sheets, order, "Info", info, ndigits)
if not order:
return None, None, []
bio = io.BytesIO()
engine = _excel_engine()
with pd.ExcelWriter(bio, engine=engine) as writer:
for name in order:
df = sheets[name]
sheet = _excel_safe_name(name)
df.to_excel(writer, sheet_name=sheet, index=False)
if do_autofit:
_excel_autofit(writer, sheet, df)
bio.seek(0)
fname = f"YM_Export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
return bio.getvalue(), fname, order
# --------- SIMPLE export UI (dropdown checklist, starts empty) ----------
def render_export_button(phase_key: str) -> None:
"""
Export UI — one multiselect dropdown that starts EMPTY.
The download button is disabled until at least one sheet is selected.
"""
res = st.session_state.get("results", {})
if not res:
return
st.divider()
st.markdown("### Export to Excel")
options = _available_sections() # only what exists right now
selected_sheets = st.multiselect(
"Sheets to include",
options=options,
default=[],
placeholder="Choose option(s)",
help="Pick the sheets you want to include in the Excel export.",
key=f"sheets_{phase_key}",
)
if not selected_sheets:
st.caption("Select one or more sheets above to enable the export.")
st.download_button(
label="⬇️ Export Excel",
data=b"",
file_name="YM_Export.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
disabled=True,
key=f"download_{phase_key}",
)
return
data, fname, names = build_export_workbook(selected=selected_sheets, ndigits=3, do_autofit=True)
if names:
st.caption("Will include: " + ", ".join(names))
st.download_button(
"⬇️ Export Excel",
data=(data or b""),
file_name=(fname or "YM_Export.xlsx"),
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
disabled=(data is None),
key=f"download_{phase_key}",
)
# =========================
# Cross plot (Matplotlib) — auto-scaled for Ym
# =========================
def cross_plot_static(actual, pred, xlabel="Actual Ym", ylabel="Predicted Ym"):
a = pd.Series(actual, dtype=float)
p = pd.Series(pred, dtype=float)
lo = float(min(a.min(), p.min()))
hi = float(max(a.max(), p.max()))
pad = 0.03 * (hi - lo if hi > lo else 1.0)
lo2, hi2 = lo - pad, hi + pad
ticks = np.linspace(lo2, hi2, 5)
dpi = 110
fig, ax = plt.subplots(figsize=(CROSS_W / dpi, CROSS_H / dpi), dpi=dpi, constrained_layout=False)
ax.scatter(a, p, s=14, c=COLORS["pred"], alpha=0.9, linewidths=0)
ax.plot([lo2, hi2], [lo2, hi2], linestyle="--", linewidth=1.2, color=COLORS["ref"])
ax.set_xlim(lo2, hi2)
ax.set_ylim(lo2, hi2)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_aspect("equal", adjustable="box")
# Generic numeric formatting (2 decimals)
fmt = FuncFormatter(lambda x, _: f"{x:.2f}")
ax.xaxis.set_major_formatter(fmt)
ax.yaxis.set_major_formatter(fmt)
ax.set_xlabel(xlabel, fontweight="bold", fontsize=10, color="black")
ax.set_ylabel(ylabel, fontweight="bold", fontsize=10, color="black")
ax.tick_params(labelsize=6, colors="black")
ax.grid(True, linestyle=":", alpha=0.3)
for spine in ax.spines.values():
spine.set_linewidth(1.1)
spine.set_color("#444")
fig.subplots_adjust(left=0.16, bottom=0.16, right=0.98, top=0.98)
return fig
# =========================
# Track plot (Plotly)
# =========================
def track_plot(df, include_actual=True):
# Depth (or index) on Y
depth_col = next((c for c in df.columns if 'depth' in str(c).lower()), None)
if depth_col is not None:
y = pd.Series(df[depth_col]).astype(float)
ylab = depth_col
y_range = [float(y.max()), float(y.min())] # reverse
else:
y = pd.Series(np.arange(1, len(df) + 1))
ylab = "Point Index"
y_range = [float(y.max()), float(y.min())]
# X range from prediction/actual
x_series = pd.Series(df.get(PRED_COL, pd.Series(dtype=float))).astype(float)
if include_actual and TARGET in df.columns:
x_series = pd.concat([x_series, pd.Series(df[TARGET]).astype(float)], ignore_index=True)
x_lo, x_hi = float(x_series.min()), float(x_series.max())
x_pad = 0.03 * (x_hi - x_lo if x_hi > x_lo else 1.0)
xmin, xmax = x_lo - x_pad, x_hi + x_pad
tick0 = _nice_tick0(xmin, step=max((xmax - xmin) / 10.0, 0.1))
fig = go.Figure()
if PRED_COL in df.columns:
fig.add_trace(go.Scatter(
x=df[PRED_COL], y=y, mode="lines",
line=dict(color=COLORS["pred"], width=1.8),
name=PRED_COL,
hovertemplate=f"{PRED_COL}: "+"%{x:.0f}
"+ylab+": %{y}"
))
if include_actual and TARGET in df.columns:
fig.add_trace(go.Scatter(
x=df[TARGET], y=y, mode="lines",
line=dict(color=COLORS["actual"], width=2.0, dash="dot"),
name=f"{TARGET} (actual)",
hovertemplate=f"{TARGET}: "+"%{x:.0f}
"+ylab+": %{y}"
))
fig.update_layout(
height=TRACK_H,
width=TRACK_W,
autosize=False,
paper_bgcolor="#fff", plot_bgcolor="#fff",
margin=dict(l=64, r=16, t=36, b=48), hovermode="closest",
font=dict(size=FONT_SZ, color="#000"),
legend=dict(x=0.98, y=0.05, xanchor="right", yanchor="bottom",
bgcolor="rgba(255,255,255,0.75)", bordercolor="#ccc", borderwidth=1),
legend_title_text=""
)
# X axis with NO decimals
fig.update_xaxes(
title_text="Ym",
title_font=dict(size=20, family=BOLD_FONT, color="#000"),
tickfont=dict(size=15, family=BOLD_FONT, color="#000"),
side="top",
range=[xmin, xmax],
ticks="outside",
tickformat=",.0f", # integers, thousands separated
tickmode="auto",
tick0=tick0,
showline=True, linewidth=1.2, linecolor="#444", mirror=True,
showgrid=True, gridcolor="rgba(0,0,0,0.12)", automargin=True
)
fig.update_yaxes(
title_text=ylab,
title_font=dict(size=20, family=BOLD_FONT, color="#000"),
tickfont=dict(size=15, family=BOLD_FONT, color="#000"),
range=y_range,
ticks="outside",
showline=True, linewidth=1.2, linecolor="#444", mirror=True,
showgrid=True, gridcolor="rgba(0,0,0,0.12)", automargin=True
)
return fig
def preview_tracks(df: pd.DataFrame, cols: list[str]):
"""
Render quick-look tracks for the selected columns with DISTINCT colors per input,
and reserve the Y-axis across all plots so depth/index is aligned.
"""
cols = [c for c in cols if c in df.columns]
n = len(cols)
if n == 0:
fig, ax = plt.subplots(figsize=(4, 2))
ax.text(0.5, 0.5, "No selected columns", ha="center", va="center")
ax.axis("off")
return fig
# Depth or fallback to point index
depth_col = next((c for c in df.columns if 'depth' in str(c).lower()), None)
if depth_col is not None:
idx = pd.to_numeric(df[depth_col], errors="coerce")
y_label = depth_col
else:
idx = pd.Series(np.arange(1, len(df) + 1))
y_label = "Point Index"
# Y range is reserved across all subplots
y_min, y_max = float(idx.min()), float(idx.max())
fig, axes = plt.subplots(1, n, figsize=(2.2 * n, 7.0), sharey=True, dpi=100)
if n == 1:
axes = [axes]
# Stable color palette
cmap = plt.get_cmap("tab20")
col_colors = {col: cmap(i % cmap.N) for i, col in enumerate(cols)}
for i, (ax, col) in enumerate(zip(axes, cols)):
x = pd.to_numeric(df[col], errors="coerce")
ax.plot(x, idx, '-', lw=1.6, color=col_colors[col])
ax.set_xlabel(col)
ax.xaxis.set_label_position('top')
ax.xaxis.tick_top()
ax.set_ylim(y_max, y_min) # reserve and invert depth axis
ax.grid(True, linestyle=":", alpha=0.3)
# Only show y-axis label + ticks on the first subplot
if i == 0:
ax.set_ylabel(y_label)
else:
ax.tick_params(labelleft=False)
ax.set_ylabel("")
fig.tight_layout()
return fig
# Modal wrapper
try:
dialog = st.dialog
except AttributeError:
def dialog(title):
def deco(fn):
def wrapper(*args, **kwargs):
with st.expander(title, expanded=True):
return fn(*args, **kwargs)
return wrapper
return deco
def preview_modal(book: dict[str, pd.DataFrame]):
if not book:
st.info("No data loaded yet."); return
names = list(book.keys())
tabs = st.tabs(names)
for t, name in zip(tabs, names):
with t:
df = _normalize_columns(book[name])
t1, t2 = st.tabs(["Tracks", "Summary"])
with t1:
st.pyplot(preview_tracks(df, FEATURES), use_container_width=True)
with t2:
present = [c for c in FEATURES if c in df.columns]
if present:
tbl = (df[present]
.agg(['min','max','mean','std'])
.T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"}))
df_centered_rounded(tbl.reset_index(names="Feature"))
else:
st.info("No expected feature columns found to summarize.")
# =========================
# Load model
# =========================
def ensure_model() -> Path|None:
for p in [DEFAULT_MODEL, *MODEL_FALLBACKS]:
if p.exists() and p.stat().st_size > 0: return p
url = os.environ.get("MODEL_URL", "")
if not url: return None
try:
import requests
DEFAULT_MODEL.parent.mkdir(parents=True, exist_ok=True)
with requests.get(url, stream=True, timeout=30) as r:
r.raise_for_status()
with open(DEFAULT_MODEL, "wb") as f:
for chunk in r.iter_content(1<<20):
if chunk: f.write(chunk)
return DEFAULT_MODEL
except Exception:
return None
mpath = ensure_model()
if not mpath:
st.error("Model not found. Upload models/ym_rf.joblib (or set MODEL_URL).")
st.stop()
try:
model = load_model(str(mpath))
except Exception as e:
st.error(f"Failed to load model: {e}")
st.stop()
# ---------- Load meta (optional) ----------
meta = {}
meta_candidates = [MODELS_DIR / "meta.json", MODELS_DIR / "ym_meta.json"]
meta_path = next((p for p in meta_candidates if p.exists()), None)
if meta_path:
try:
meta = json.loads(meta_path.read_text(encoding="utf-8"))
FEATURES = meta.get("features", FEATURES)
TARGET = meta.get("target", TARGET)
except Exception as e:
st.warning(f"Could not parse meta file ({meta_path.name}): {e}")
# Optional: version mismatch warning
import numpy as _np, sklearn as _skl
mv = meta.get("versions", {})
if mv:
msg = []
if mv.get("numpy") and mv["numpy"] != _np.__version__:
msg.append(f"NumPy {mv['numpy']} expected, running {_np.__version__}")
if mv.get("scikit_learn") and mv["scikit_learn"] != _skl.__version__:
msg.append(f"scikit-learn {mv['scikit_learn']} expected, running {_skl.__version__}")
if msg:
st.warning("Environment mismatch: " + " | ".join(msg))
# =========================
# Session state
# =========================
st.session_state.setdefault("app_step", "intro")
st.session_state.setdefault("results", {})
st.session_state.setdefault("train_ranges", None)
st.session_state.setdefault("dev_file_name","")
st.session_state.setdefault("dev_file_bytes",b"")
st.session_state.setdefault("dev_file_loaded",False)
st.session_state.setdefault("dev_preview",False)
st.session_state.setdefault("show_preview_modal", False)
# =========================
# Branding in Sidebar
# =========================
st.sidebar.markdown(f"""
{APP_NAME}
{TAGLINE}
""", unsafe_allow_html=True
)
# =========================
# Reusable Sticky Header Function
# =========================
def sticky_header(title, message):
st.markdown(
f"""
""",
unsafe_allow_html=True
)
# =========================
# INTRO
# =========================
if st.session_state.app_step == "intro":
st.header("Welcome!")
st.markdown("This software is developed by *Smart Thinking AI-Solutions Team* to estimate Young's Modulus (Ym) from drilling data.")
st.subheader("How It Works")
st.markdown(
"1) **Upload your data to build the case and preview the model performance.** \n"
"2) Click **Run Model** to compute metrics and plots. \n"
"3) **Proceed to Validation** (with actual Ym) or **Proceed to Prediction** (no Ym)."
)
if st.button("Start Showcase", type="primary"):
st.session_state.app_step = "dev"; st.rerun()
# =========================
# CASE BUILDING
# =========================
if st.session_state.app_step == "dev":
st.sidebar.header("Case Building")
up = st.sidebar.file_uploader("Upload Your Data File", type=["xlsx","xls"])
if up is not None:
st.session_state.dev_file_bytes = up.getvalue()
st.session_state.dev_file_name = up.name
st.session_state.dev_file_loaded = True
st.session_state.dev_preview = False
if st.session_state.dev_file_loaded:
tmp = read_book_bytes(st.session_state.dev_file_bytes)
if tmp:
df0 = next(iter(tmp.values()))
st.sidebar.caption(f"**Data loaded:** {st.session_state.dev_file_name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
if st.sidebar.button("Preview data", use_container_width=True, disabled=not st.session_state.dev_file_loaded):
st.session_state.show_preview_modal = True
st.session_state.dev_preview = True
run = st.sidebar.button("Run Model", type="primary", use_container_width=True)
if st.sidebar.button("Proceed to Validation ▶", use_container_width=True): st.session_state.app_step="validate"; st.rerun()
if st.sidebar.button("Proceed to Prediction ▶", use_container_width=True): st.session_state.app_step="predict"; st.rerun()
if st.session_state.dev_file_loaded and st.session_state.dev_preview:
sticky_header("Case Building", "Previewed ✓ — now click **Run Model**.")
elif st.session_state.dev_file_loaded:
sticky_header("Case Building", "📄 **Preview uploaded data** using the sidebar button, then click **Run Model**.")
else:
sticky_header("Case Building", "**Upload your data to build a case, then run the model to review development performance.**")
if run and st.session_state.dev_file_bytes:
book = read_book_bytes(st.session_state.dev_file_bytes)
sh_train = find_sheet(book, ["Train","Training","training2","train","training"])
sh_test = find_sheet(book, ["Test","Testing","testing2","test","testing"])
if sh_train is None or sh_test is None:
st.markdown('Workbook must include Train/Training/training2 and Test/Testing/testing2 sheets.
', unsafe_allow_html=True)
st.stop()
tr = _normalize_columns(book[sh_train].copy())
te = _normalize_columns(book[sh_test].copy())
if not (ensure_cols(tr, FEATURES+[TARGET]) and ensure_cols(te, FEATURES+[TARGET])):
st.markdown('Missing required columns.
', unsafe_allow_html=True)
st.stop()
tr[PRED_COL] = model.predict(tr[FEATURES])
te[PRED_COL] = model.predict(te[FEATURES])
st.session_state.results["Train"]=tr; st.session_state.results["Test"]=te
st.session_state.results["m_train"]={
"R": pearson_r(tr[TARGET], tr[PRED_COL]),
"RMSE": rmse(tr[TARGET], tr[PRED_COL]),
"MAPE": mape(tr[TARGET], tr[PRED_COL])
}
st.session_state.results["m_test"]={
"R": pearson_r(te[TARGET], te[PRED_COL]),
"RMSE": rmse(te[TARGET], te[PRED_COL]),
"MAPE": mape(te[TARGET], te[PRED_COL])
}
tr_min = tr[FEATURES].min().to_dict(); tr_max = tr[FEATURES].max().to_dict()
st.session_state.train_ranges = {f:(float(tr_min[f]), float(tr_max[f])) for f in FEATURES}
st.markdown('Case has been built and results are displayed below.
', unsafe_allow_html=True)
def _dev_block(df, m):
c1,c2,c3 = st.columns(3)
c1.metric("R", f"{m['R']:.3f}")
c2.metric("RMSE", f"{m['RMSE']:.2f}")
c3.metric("MAPE (%)", f"{m['MAPE']:.2f}")
st.markdown("""
R: Pearson Correlation Coefficient
RMSE: Root Mean Square Error
MAPE: Mean Absolute Percentage Error
""", unsafe_allow_html=True)
col_track, col_cross = st.columns([2, 3], gap="large")
with col_track:
st.plotly_chart(
track_plot(df, include_actual=True),
use_container_width=False,
config={"displayModeBar": False, "scrollZoom": True}
)
with col_cross:
st.pyplot(cross_plot_static(df[TARGET], df[PRED_COL]), use_container_width=False)
if "Train" in st.session_state.results or "Test" in st.session_state.results:
tab1, tab2 = st.tabs(["Training", "Testing"])
if "Train" in st.session_state.results:
with tab1:
_dev_block(st.session_state.results["Train"], st.session_state.results["m_train"])
if "Test" in st.session_state.results:
with tab2:
_dev_block(st.session_state.results["Test"], st.session_state.results["m_test"])
# Export UI for this phase (dropdown checklist starts empty)
render_export_button(phase_key="dev")
# =========================
# VALIDATION (with actual Ym)
# =========================
if st.session_state.app_step == "validate":
st.sidebar.header("Validate the Model")
up = st.sidebar.file_uploader("Upload Validation Excel", type=["xlsx","xls"])
if up is not None:
book = read_book_bytes(up.getvalue())
if book:
df0 = next(iter(book.values()))
st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
if st.sidebar.button("Preview data", use_container_width=True, disabled=(up is None)):
st.session_state.show_preview_modal = True
go_btn = st.sidebar.button("Predict & Validate", type="primary", use_container_width=True)
if st.sidebar.button("⬅ Back to Case Building", use_container_width=True): st.session_state.app_step="dev"; st.rerun()
if st.sidebar.button("Proceed to Prediction ▶", use_container_width=True): st.session_state.app_step="predict"; st.rerun()
sticky_header("Validate the Model", "Upload a dataset with the same **features** and **Actual Ym** to evaluate performance.")
if go_btn and up is not None:
book = read_book_bytes(up.getvalue())
name = find_sheet(book, ["Validation","Validate","validation2","Val","val"]) or list(book.keys())[0]
df = _normalize_columns(book[name].copy())
if not ensure_cols(df, FEATURES+[TARGET]):
st.markdown('Missing required columns.
', unsafe_allow_html=True); st.stop()
df[PRED_COL] = model.predict(df[FEATURES])
st.session_state.results["Validate"]=df
ranges = st.session_state.train_ranges; oor_pct = 0.0; tbl=None
if ranges:
any_viol = pd.DataFrame({f:(df[f]ranges[f][1]) for f in FEATURES}).any(axis=1)
oor_pct = float(any_viol.mean()*100.0)
if any_viol.any():
tbl = df.loc[any_viol, FEATURES].copy()
for c in FEATURES:
if pd.api.types.is_numeric_dtype(tbl[c]): tbl[c] = tbl[c].round(2)
tbl["Violations"] = pd.DataFrame({f:(df[f]ranges[f][1]) for f in FEATURES}).loc[any_viol].apply(lambda r:", ".join([c for c,v in r.items() if v]), axis=1)
st.session_state.results["m_val"]={
"R": pearson_r(df[TARGET], df[PRED_COL]),
"RMSE": rmse(df[TARGET], df[PRED_COL]),
"MAPE": mape(df[TARGET], df[PRED_COL])
}
st.session_state.results["sv_val"]={"n":len(df), "pred_min":float(df[PRED_COL].min()), "pred_max":float(df[PRED_COL].max()), "oor":oor_pct}
st.session_state.results["oor_tbl"]=tbl
if "Validate" in st.session_state.results:
m = st.session_state.results["m_val"]
c1,c2,c3 = st.columns(3)
c1.metric("R", f"{m['R']:.3f}")
c2.metric("RMSE", f"{m['RMSE']:.2f}")
c3.metric("MAPE (%)", f"{m['MAPE']:.2f}")
st.markdown("""
R: Pearson Correlation Coefficient
RMSE: Root Mean Square Error
MAPE: Mean Absolute Percentage Error
""", unsafe_allow_html=True)
col_track, col_cross = st.columns([2, 3], gap="large")
with col_track:
st.plotly_chart(
track_plot(st.session_state.results["Validate"], include_actual=True),
use_container_width=False,
config={"displayModeBar": False, "scrollZoom": True}
)
with col_cross:
st.pyplot(
cross_plot_static(st.session_state.results["Validate"][TARGET],
st.session_state.results["Validate"][PRED_COL]),
use_container_width=False
)
# Export UI for this phase (dropdown checklist starts empty)
render_export_button(phase_key="validate")
sv = st.session_state.results["sv_val"]
if sv["oor"] > 0: st.markdown('Some inputs fall outside **training min–max** ranges.
', unsafe_allow_html=True)
if st.session_state.results["oor_tbl"] is not None:
st.write("*Out-of-range rows (vs. Training min–max):*")
df_centered_rounded(st.session_state.results["oor_tbl"])
# =========================
# PREDICTION (no actual Ym)
# =========================
if st.session_state.app_step == "predict":
st.sidebar.header("Prediction (No Actual Ym)")
up = st.sidebar.file_uploader("Upload Prediction Excel", type=["xlsx","xls"])
if up is not None:
book = read_book_bytes(up.getvalue())
if book:
df0 = next(iter(book.values()))
st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
if st.sidebar.button("Preview data", use_container_width=True, disabled=(up is None)):
st.session_state.show_preview_modal = True
go_btn = st.sidebar.button("Predict", type="primary", use_container_width=True)
if st.sidebar.button("⬅ Back to Case Building", use_container_width=True): st.session_state.app_step="dev"; st.rerun()
sticky_header("Prediction", "Upload a dataset with the feature columns (no **Actual Ym**).")
if go_btn and up is not None:
book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]
df = _normalize_columns(book[name].copy())
if not ensure_cols(df, FEATURES):
st.markdown('Missing required columns.
', unsafe_allow_html=True); st.stop()
df[PRED_COL] = model.predict(df[FEATURES])
st.session_state.results["PredictOnly"]=df
ranges = st.session_state.train_ranges; oor_pct = 0.0
if ranges:
any_viol = pd.DataFrame({f:(df[f]ranges[f][1]) for f in FEATURES}).any(axis=1)
oor_pct = float(any_viol.mean()*100.0)
st.session_state.results["sv_pred"]={
"n":len(df),
"pred_min":float(df[PRED_COL].min()),
"pred_max":float(df[PRED_COL].max()),
"pred_mean":float(df[PRED_COL].mean()),
"pred_std":float(df[PRED_COL].std(ddof=0)),
"oor":oor_pct
}
if "PredictOnly" in st.session_state.results:
df = st.session_state.results["PredictOnly"]; sv = st.session_state.results["sv_pred"]
col_left, col_right = st.columns([2,3], gap="large")
with col_left:
table = pd.DataFrame({
"Metric": ["# points","Pred min","Pred max","Pred mean","Pred std","OOR %"],
"Value": [sv["n"],
round(sv["pred_min"],3),
round(sv["pred_max"],3),
round(sv["pred_mean"],3),
round(sv["pred_std"],3),
f'{sv["oor"]:.1f}%']
})
st.markdown('Predictions ready ✓
', unsafe_allow_html=True)
df_centered_rounded(table, hide_index=True)
st.caption("**★ OOR** = % of rows whose input features fall outside the training min–max range.")
with col_right:
st.plotly_chart(
track_plot(df, include_actual=False),
use_container_width=False,
config={"displayModeBar": False, "scrollZoom": True}
)
# Export UI for this phase (dropdown checklist starts empty)
render_export_button(phase_key="predict")
# =========================
# Run preview modal after all other elements
# =========================
if st.session_state.show_preview_modal:
# Select the correct workbook bytes for this step
book_to_preview = {}
if st.session_state.app_step == "dev":
book_to_preview = read_book_bytes(st.session_state.dev_file_bytes)
elif st.session_state.app_step in ["validate", "predict"] and 'up' in locals() and up is not None:
book_to_preview = read_book_bytes(up.getvalue())
with st.expander("Preview data", expanded=True):
if not book_to_preview:
st.markdown('No data loaded yet.
', unsafe_allow_html=True)
else:
names = list(book_to_preview.keys())
tabs = st.tabs(names)
for t, name in zip(tabs, names):
with t:
df = _normalize_columns(book_to_preview[name])
t1, t2 = st.tabs(["Tracks", "Summary"])
with t1:
st.pyplot(preview_tracks(df, FEATURES), use_container_width=True)
with t2:
feat_present = [c for c in FEATURES if c in df.columns]
if not feat_present:
st.info("No feature columns found to summarize.")
else:
tbl = (
df[feat_present]
.agg(['min','max','mean','std'])
.T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"})
.reset_index(names="Feature")
)
df_centered_rounded(tbl)
st.session_state.show_preview_modal = False
# =========================
# Footer
# =========================
st.markdown("""
""", unsafe_allow_html=True)