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| import io, json, os, base64, math | |
| from pathlib import Path | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import joblib | |
| # Matplotlib for PREVIEW modal and for 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, mean_absolute_error | |
| # ========================= | |
| # Constants | |
| # ========================= | |
| FEATURES = ["Q, gpm", "SPP(psi)", "T (kft.lbf)", "WOB (klbf)", "ROP (ft/h)"] | |
| TARGET = "UCS" | |
| MODELS_DIR = Path("models") | |
| DEFAULT_MODEL = MODELS_DIR / "ucs_rf.joblib" | |
| MODEL_FALLBACKS = [MODELS_DIR / "model.joblib", MODELS_DIR / "model.pkl"] | |
| COLORS = {"pred": "#1f77b4", "actual": "#f2b702", "ref": "#5a5a5a"} | |
| # ---- Plot sizing controls ---- | |
| CROSS_W = 250 # px (matplotlib figure size; Streamlit will still scale) | |
| CROSS_H = 250 | |
| TRACK_H = 1000 # px (plotly height; width auto-fits column) | |
| # NEW: Add a TRACK_W variable to control the width | |
| TRACK_W = 500 # px (plotly width) | |
| FONT_SZ = 13 | |
| BOLD_FONT = "Arial Black, Arial, sans-serif" # used for bold axis titles & ticks | |
| # ========================= | |
| # Page / CSS | |
| # ========================= | |
| st.set_page_config(page_title="ST_GeoMech_UCS", page_icon="logo.png", layout="wide") | |
| # General CSS (logo helpers etc.) | |
| st.markdown(""" | |
| <style> | |
| .brand-logo { width: 200px; height: auto; object-fit: contain; } | |
| .sidebar-header { display:flex; align-items:center; gap:12px; } | |
| .sidebar-header .text h1 { font-size: 1.05rem; margin:0; line-height:1.1; } | |
| .sidebar-header .text .tag { font-size: .85rem; color:#6b7280; margin:2px 0 0; } | |
| .centered-container { | |
| display: flex; | |
| flex-direction: column; | |
| align-items: center; | |
| text-align: center; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # CSS to make sticky headers work correctly by overriding Streamlit's overflow property | |
| st.markdown(""" | |
| <style> | |
| /* This targets the main content area */ | |
| .main .block-container { | |
| overflow: unset !important; | |
| } | |
| /* This targets the vertical block that holds all your elements */ | |
| div[data-testid="stVerticalBlock"] { | |
| overflow: unset !important; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Hide uploader helper text ("Drag and drop file here", limits, etc.) | |
| st.markdown(""" | |
| <style> | |
| /* Older builds (helper wrapped in a Markdown container) */ | |
| section[data-testid="stFileUploader"] div[data-testid="stMarkdownContainer"]{display:none !important;} | |
| /* 1.31–1.34: helper is the first child in the dropzone */ | |
| section[data-testid="stFileUploader"] [data-testid="stFileUploaderDropzone"] > div:first-child{display:none !important;} | |
| /* 1.35+: explicit helper container */ | |
| section[data-testid="stFileUploader"] [data-testid="stFileUploaderInstructions"]{display:none !important;} | |
| /* Fallback: any paragraph/small text inside the uploader */ | |
| section[data-testid="stFileUploader"] p, section[data-testid="stFileUploader"] small{display:none !important;} | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Make the Preview expander title & tabs sticky (pinned to the top) | |
| st.markdown(""" | |
| <style> | |
| div[data-testid="stExpander"] > details > summary { | |
| position: sticky; | |
| top: 0; | |
| z-index: 10; | |
| background: #fff; | |
| border-bottom: 1px solid #eee; | |
| } | |
| div[data-testid="stExpander"] div[data-baseweb="tab-list"] { | |
| position: sticky; | |
| top: 42px; /* adjust if your expander header height differs */ | |
| z-index: 9; | |
| background: #fff; | |
| padding-top: 6px; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Center text in all pandas Styler tables (headers + cells) | |
| TABLE_CENTER_CSS = [ | |
| dict(selector="th", props=[("text-align", "center")]), | |
| dict(selector="td", props=[("text-align", "center")]), | |
| ] | |
| # NEW: CSS for the message box | |
| st.markdown(""" | |
| <style> | |
| .st-message-box { | |
| background-color: #f0f2f6; | |
| color: #333333; | |
| padding: 10px; | |
| border-radius: 10px; | |
| border: 1px solid #e6e9ef; | |
| } | |
| .st-message-box.st-success { | |
| background-color: #d4edda; | |
| color: #155724; | |
| border-color: #c3e6cb; | |
| } | |
| .st-message-box.st-warning { | |
| background-color: #fff3cd; | |
| color: #856404; | |
| border-color: #ffeeba; | |
| } | |
| .st-message-box.st-error { | |
| background-color: #f8d7da; | |
| color: #721c24; | |
| border-color: #f5c6cb; | |
| } | |
| </style> | |
| """, 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""" | |
| <div class="centered-container"> | |
| <img src="{inline_logo('logo.png')}" style="width: 200px; height: auto; object-fit: contain;"> | |
| <div style='font-weight:800;font-size:1.2rem; margin-top: 10px;'>ST_GeoMech_UCS</div> | |
| <div style='color:#667085;'>Smart Thinking • Secure Access</div> | |
| </div> | |
| """, 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") | |
| return float(np.corrcoef(a, p)[0, 1]) | |
| def load_model(model_path: str): | |
| return joblib.load(model_path) | |
| 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 ensure_cols(df, cols): | |
| 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: int = 100) -> float: | |
| return step * math.floor(xmin / step) if np.isfinite(xmin) else xmin | |
| def df_centered_rounded(df: pd.DataFrame, hide_index=True): | |
| """Center headers & cells; format numeric columns to 2 decimals.""" | |
| 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) | |
| # ========================= | |
| # Cross plot (Matplotlib, fixed limits & ticks) | |
| # ========================= | |
| def cross_plot_static(actual, pred): | |
| a = pd.Series(actual, dtype=float) | |
| p = pd.Series(pred, dtype=float) | |
| fixed_min, fixed_max = 6000, 10000 | |
| ticks = np.arange(fixed_min, fixed_max + 1, 1000) | |
| 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([fixed_min, fixed_max], [fixed_min, fixed_max], | |
| linestyle="--", linewidth=1.2, color=COLORS["ref"]) | |
| ax.set_xlim(fixed_min, fixed_max) | |
| ax.set_ylim(fixed_min, fixed_max) | |
| ax.set_xticks(ticks) | |
| ax.set_yticks(ticks) | |
| ax.set_aspect("equal", adjustable="box") # true 45° | |
| fmt = FuncFormatter(lambda x, _: f"{int(x):,}") | |
| ax.xaxis.set_major_formatter(fmt) | |
| ax.yaxis.set_major_formatter(fmt) | |
| ax.set_xlabel("Actual UCS (psi)", fontweight="bold", fontsize=4, color="black") | |
| ax.set_ylabel("Predicted UCS (psi)", fontweight="bold", fontsize=4, color="black") | |
| ax.tick_params(labelsize=2, 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_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 (UCS) range & ticks | |
| x_series = pd.Series(df.get("UCS_Pred", 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=100) | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatter( | |
| x=df["UCS_Pred"], y=y, mode="lines", | |
| line=dict(color=COLORS["pred"], width=1.8), | |
| name="UCS_Pred", | |
| hovertemplate="UCS_Pred: %{x:.0f}<br>"+ylab+": %{y}<extra></extra>" | |
| )) | |
| 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="UCS (actual)", | |
| hovertemplate="UCS (actual): %{x:.0f}<br>"+ylab+": %{y}<extra></extra>" | |
| )) | |
| fig.update_layout( | |
| height=TRACK_H, | |
| width=TRACK_W, # Set the width here | |
| autosize=False, # Disable autosizing to respect the width | |
| 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="" | |
| ) | |
| # Bold, black axis titles & ticks | |
| fig.update_xaxes( | |
| title_text="UCS (psi)", | |
| 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", | |
| 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 | |
| # ---------- Preview modal (matplotlib) ---------- | |
| def preview_tracks(df: pd.DataFrame, cols: list[str]): | |
| 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 | |
| fig, axes = plt.subplots(1, n, figsize=(2.2*n, 7.0), sharey=True, dpi=100) | |
| if n == 1: axes = [axes] | |
| idx = np.arange(1, len(df) + 1) | |
| for ax, col in zip(axes, cols): | |
| ax.plot(df[col], idx, '-', lw=1.4, color="#333") | |
| ax.set_xlabel(col); ax.xaxis.set_label_position('top'); ax.xaxis.tick_top(); ax.invert_yaxis() | |
| ax.grid(True, linestyle=":", alpha=0.3) | |
| for s in ax.spines.values(): s.set_visible(True) | |
| axes[0].set_ylabel("Point Index") | |
| return fig | |
| # Modal wrapper (Streamlit compatibility) | |
| 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 = book[name] | |
| t1, t2 = st.tabs(["Tracks", "Summary"]) | |
| with t1: | |
| st.pyplot(preview_tracks(df, FEATURES), use_container_width=True) | |
| with t2: | |
| tbl = (df[FEATURES] | |
| .agg(['min','max','mean','std']) | |
| .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"})) | |
| df_centered_rounded(tbl.reset_index(names="Feature")) | |
| # ========================= | |
| # 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/ucs_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() | |
| meta_path = MODELS_DIR / "meta.json" | |
| if meta_path.exists(): | |
| try: | |
| meta = json.loads(meta_path.read_text(encoding="utf-8")) | |
| FEATURES = meta.get("features", FEATURES); TARGET = meta.get("target", TARGET) | |
| except Exception: | |
| pass | |
| # ========================= | |
| # 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) # New state variable | |
| # ========================= | |
| # Branding in Sidebar | |
| # ========================= | |
| st.sidebar.markdown(f""" | |
| <div class="centered-container"> | |
| <img src="{inline_logo('logo.png')}" style="width: 200px; height: auto; object-fit: contain;"> | |
| <div style='font-weight:800;font-size:1.2rem;'>ST_GeoMech_UCS</div> | |
| <div style='color:#667085;'>Real-Time UCS Tracking While Drilling</div> | |
| </div> | |
| """, unsafe_allow_html=True | |
| ) | |
| # ========================= | |
| # Reusable Sticky Header Function | |
| # ========================= | |
| def sticky_header(title, message): | |
| st.markdown( | |
| f""" | |
| <style> | |
| .sticky-container {{ | |
| position: sticky; | |
| top: 0; | |
| background-color: white; | |
| z-index: 100; | |
| padding-top: 10px; | |
| padding-bottom: 10px; | |
| border-bottom: 1px solid #eee; | |
| }} | |
| </style> | |
| <div class="sticky-container"> | |
| <h3>{title}</h3> | |
| <p>{message}</p> | |
| </div> | |
| """, | |
| 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 UCS from drilling data.") | |
| st.subheader("How It Works") | |
| st.markdown( | |
| "1) **Upload your data to build the case and preview the performance of our model.** \n" | |
| "2) Click **Run Model** to compute metrics and plots. \n" | |
| "3) **Proceed to Validation** (with actual UCS) or **Proceed to Prediction** (no UCS)." | |
| ) | |
| 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 # Set state to show modal | |
| 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() | |
| # Apply sticky header | |
| 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('<div class="st-message-box st-error">Workbook must include Train/Training/training2 and Test/Testing/testing2 sheets.</div>', unsafe_allow_html=True) | |
| st.stop() | |
| tr = book[sh_train].copy(); te = book[sh_test].copy() | |
| if not (ensure_cols(tr, FEATURES+[TARGET]) and ensure_cols(te, FEATURES+[TARGET])): | |
| st.markdown('<div class="st-message-box st-error">Missing required columns.</div>', unsafe_allow_html=True) | |
| st.stop() | |
| tr["UCS_Pred"] = model.predict(tr[FEATURES]) | |
| te["UCS_Pred"] = 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["UCS_Pred"]), | |
| "RMSE": rmse(tr[TARGET], tr["UCS_Pred"]), | |
| "MAE": mean_absolute_error(tr[TARGET], tr["UCS_Pred"]) | |
| } | |
| st.session_state.results["m_test"]={ | |
| "R": pearson_r(te[TARGET], te["UCS_Pred"]), | |
| "RMSE": rmse(te[TARGET], te["UCS_Pred"]), | |
| "MAE": mean_absolute_error(te[TARGET], te["UCS_Pred"]) | |
| } | |
| 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('<div class="st-message-box st-success">Case has been built and results are displayed below.</div>', unsafe_allow_html=True) | |
| def _dev_block(df, m): | |
| c1,c2,c3 = st.columns(3) | |
| c1.metric("R", f"{m['R']:.2f}") | |
| c2.metric("RMSE", f"{m['RMSE']:.2f}") | |
| c3.metric("MAE", f"{m['MAE']:.2f}") | |
| # NEW: Footer for metric abbreviations | |
| st.markdown(""" | |
| <div style='text-align: left; font-size: 0.8em; color: #6b7280; margin-top: -16px; margin-bottom: 8px;'> | |
| <strong>R:</strong> Pearson Correlation Coefficient<br> | |
| <strong>RMSE:</strong> Root Mean Square Error<br> | |
| <strong>MAE:</strong> Mean Absolute Error | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # 2-column layout, big gap (prevents overlap) | |
| col_cross, col_track = st.columns([3, 2], gap="large") | |
| with col_cross: | |
| st.pyplot(cross_plot_static(df[TARGET], df["UCS_Pred"]), use_container_width=False) | |
| with col_track: | |
| st.plotly_chart( | |
| track_plot(df, include_actual=True), | |
| use_container_width=False, # Set to False to honor the width in track_plot() | |
| config={"displayModeBar": False, "scrollZoom": True} | |
| ) | |
| 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"]) | |
| # ========================= | |
| # VALIDATION (with actual UCS) | |
| # ========================= | |
| 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 # Set state to show modal | |
| 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() | |
| 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 **UCS** 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 = book[name].copy() | |
| if not ensure_cols(df, FEATURES+[TARGET]): st.markdown('<div class="st-message-box st-error">Missing required columns.</div>', unsafe_allow_html=True); st.stop() | |
| df["UCS_Pred"] = 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][0])|(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][0])|(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["UCS_Pred"]), | |
| "RMSE": rmse(df[TARGET], df["UCS_Pred"]), | |
| "MAE": mean_absolute_error(df[TARGET], df["UCS_Pred"]) | |
| } | |
| st.session_state.results["sv_val"]={"n":len(df),"pred_min":float(df["UCS_Pred"].min()),"pred_max":float(df["UCS_Pred"].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']:.2f}") | |
| c2.metric("RMSE", f"{m['RMSE']:.2f}") | |
| c3.metric("MAE", f"{m['MAE']:.2f}") | |
| # NEW: Footer for metric abbreviations | |
| st.markdown(""" | |
| <div style='text-align: left; font-size: 0.8em; color: #6b7280; margin-top: -16px; margin-bottom: 8px;'> | |
| <strong>R:</strong> Pearson Correlation Coefficient<br> | |
| <strong>RMSE:</strong> Root Mean Square Error<br> | |
| <strong>MAE:</strong> Mean Absolute Error | |
| </div> | |
| """, unsafe_allow_html=True) | |
| col_cross, col_track = st.columns([3, 2], gap="large") | |
| with col_cross: | |
| st.pyplot( | |
| cross_plot_static(st.session_state.results["Validate"][TARGET], | |
| st.session_state.results["Validate"]["UCS_Pred"]), | |
| use_container_width=False | |
| ) | |
| with col_track: | |
| st.plotly_chart( | |
| track_plot(st.session_state.results["Validate"], include_actual=True), | |
| use_container_width=False, # Set to False to honor the width in track_plot() | |
| config={"displayModeBar": False, "scrollZoom": True} | |
| ) | |
| sv = st.session_state.results["sv_val"] | |
| if sv["oor"] > 0: st.markdown('<div class="st-message-box st-warning">Some inputs fall outside **training min–max** ranges.</div>', 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 UCS) | |
| # ========================= | |
| if st.session_state.app_step == "predict": | |
| st.sidebar.header("Prediction (No Actual UCS)") | |
| 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 # Set state to show modal | |
| 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 **UCS**).") | |
| if go_btn and up is not None: | |
| book = read_book_bytes(up.getvalue()); name = list(book.keys())[0] | |
| df = book[name].copy() | |
| if not ensure_cols(df, FEATURES): st.markdown('<div class="st-message-box st-error">Missing required columns.</div>', unsafe_allow_html=True); st.stop() | |
| df["UCS_Pred"] = 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][0])|(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["UCS_Pred"].min()), | |
| "pred_max":float(df["UCS_Pred"].max()), | |
| "pred_mean":float(df["UCS_Pred"].mean()), | |
| "pred_std":float(df["UCS_Pred"].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"],2), | |
| round(sv["pred_max"],2), | |
| round(sv["pred_mean"],2), | |
| round(sv["pred_std"],2), | |
| f'{sv["oor"]:.1f}%'] | |
| }) | |
| st.markdown('<div class="st-message-box st-success">Predictions ready ✓</div>', 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, # Set to False to honor the width in track_plot() | |
| config={"displayModeBar": False, "scrollZoom": True} | |
| ) | |
| # ========================= | |
| # Run preview modal after all other elements | |
| # ========================= | |
| if st.session_state.show_preview_modal: | |
| # Get the correct book based on the current app 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 is not None: | |
| book_to_preview = read_book_bytes(up.getvalue()) | |
| # Use a try-except block to handle cases where 'up' might be None | |
| # and the logic tries to access its attributes. | |
| try: | |
| if st.session_state.app_step == "validate" and up is not None: | |
| book_to_preview = read_book_bytes(up.getvalue()) | |
| elif st.session_state.app_step == "predict" and up is not None: | |
| book_to_preview = read_book_bytes(up.getvalue()) | |
| except NameError: | |
| book_to_preview = {} | |
| with st.expander("Preview data", expanded=True): | |
| if not book_to_preview: | |
| st.markdown('<div class="st-message-box">No data loaded yet.</div>', 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 = book_to_preview[name] | |
| t1, t2 = st.tabs(["Tracks", "Summary"]) | |
| with t1: | |
| st.pyplot(preview_tracks(df, FEATURES), use_container_width=True) | |
| with t2: | |
| tbl = (df[FEATURES] | |
| .agg(['min','max','mean','std']) | |
| .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"})) | |
| df_centered_rounded(tbl.reset_index(names="Feature")) | |
| # Reset the state variable after the modal is displayed | |
| st.session_state.show_preview_modal = False | |
| # ========================= | |
| # Footer | |
| # ========================= | |
| st.markdown("---") | |
| st.markdown( | |
| """ | |
| <div style='text-align:center; color:#6b7280; line-height:1.6'> | |
| ST_GeoMech_UCS • © Smart Thinking<br/> | |
| <strong>Visit our website:</strong> <a href='https://www.smartthinking.com.sa' target='_blank'>smartthinking.com.sa</a> | |
| </div> | |
| """, | |
| unsafe_allow_html=True | |
| ) |