(REFACTOR:core): <pdularize monolithic app script

This commit introduces a major architectural refactor, breaking down the monolithic 'app.py' script into a more maintainable and organized structure. The core logic, UI components, state management, and configuration are now separate into distinct modules.

This new structure improves readability and separation of concerns, laying the groundwork for future development.

NEW COMPONENTS:
'config.py': centralized location for all application constants and model configurations
- 'core_logic.py': new home for core data processing, model loading, and ML inference functions.
- 'modules/callbacks.py': contains all Streamlit widget callbacks and state management functions
-'modules/ui_components.py': contains all high-level UI rendering functions
-'static/style.css': externalizes all custom CSS for better mgmt
-'CODEBASE_INVENTORY.md': adds the software audit and better refactor plan

CODEBASE_INVENTORY.md

@@ -480,6 +480,56 @@ The platform successfully bridges academic research and practical application, p
 
 <div style="text-align: center">⁂</div>
 
+### EXTRA
+
+```text
+1. Setup & Configuration (Lines 1-105)
+    Imports: Standard libraries (os, sys, time), data science (numpy, torch, matplotlib), and Streamlit.
+    Local Imports: Pulls from your existing utils and models directories.
+    Constants: Global, hardcoded configuration variables.
+    KEEP_KEYS: Defines which session state keys persist on reset.
+    TARGET_LEN: A static preprocessing value.
+    SAMPLE_DATA_DIR, MODEL_WEIGHTS_DIR: Path configurations.
+    MODEL_CONFIG: A dictionary defining model paths, classes, and metadata.
+    LABEL_MAP: A dictionary for mapping class indices to human-readable names.
+    Page Setup:
+    st.set_page_config(): Sets the browser tab title, icon, and layout.
+    st.markdown(<style>...): A large, embedded multi-line string containing all the custom CSS for the application.
+2. Core Logic & Data Processing (Lines 108-250)
+    Model Handling:
+    load_state_dict(): Cached function to load model weights from a file.
+    load_model(): Cached resource to initialize a model class and load its weights.
+    run_inference(): The main ML prediction function. It takes resampled data, loads the appropriate model, runs inference, and returns the results.
+    Data I/O & Preprocessing:
+    label_file(): Extracts the ground truth label from a filename.
+    get_sample_files(): Lists the available .txt files in the sample data directory.
+    parse_spectrum_data(): The crucial function for reading, validating, and parsing raw text input into numerical numpy arrays.
+    Visualization:
+    create_spectrum_plot(): Generates the "Raw vs. Resampled" matplotlib plot and returns it as an image.
+    Helpers:
+    cleanup_memory(): A utility for garbage collection.
+    get_confidence_description(): Maps a logit margin to a human-readable confidence level.
+3. State Management & Callbacks (Lines 253-335)
+    Initialization:
+    init_session_state(): The cornerstone of the app's state, defining all the default values in st.session_state.
+    Widget Callbacks:
+    on_sample_change(): Triggered when the user selects a sample file.
+    on_input_mode_change(): Triggered by the main st.radio widget.
+    on_model_change(): Triggered when the user selects a new model.
+    Reset/Clear Functions:
+    reset_results(): A soft reset that only clears inference artifacts.
+    reset_ephemeral_state(): The "master reset" that clears almost all session state and forces a file uploader refresh.
+    clear_batch_results(): A focused function to clear only the results in col2.
+4. UI Rendering Components (Lines 338-End)
+    Generic Components:
+    render_kv_grid(): A reusable helper to display a dictionary in a neat grid.
+    render_model_meta(): Renders the model's accuracy and F1 score in the sidebar.
+    Main Application Layout (main()):
+    Sidebar: Contains the header, model selector (st.selectbox), model metadata, and the "About" expander.
+    Column 1 (Input): Contains the main st.radio for mode selection and the conditional logic to display the single file uploader, batch uploader, or sample selector. It also holds the "Run Analysis" and "Reset All" buttons.
+    Column 2 (Results): Contains all the logic for displaying either the batch results or the detailed, tabbed results for a single file (Details, Technical, Explanation).
+```
+
 [^1_1]: https://huggingface.co/spaces/dev-jas/polymer-aging-ml/tree/main
 [^1_2]: https://huggingface.co/spaces/dev-jas/polymer-aging-ml/tree/main/datasets
 [^1_3]: https://huggingface.co/spaces/dev-jas/polymer-aging-ml

config.py

@@ -0,0 +1,43 @@
+from pathlib import Path
+import os
+from models.figure2_cnn import Figure2CNN
+from models.resnet_cnn import ResNet1D
+
+KEEP_KEYS = {
+    # ==global UI context we want to keep after "Reset"==
+    "model_select",     # sidebar model key
+    "input_mode",       # radio for Upload|Sample
+    "uploader_version",  # version counter for file uploader
+    "input_registry",   # radio controlling Upload vs Sample
+}
+
+TARGET_LEN = 500
+SAMPLE_DATA_DIR = Path("sample_data")
+
+MODEL_WEIGHTS_DIR = (
+    os.getenv("WEIGHTS_DIR")
+    or ("model_weights" if os.path.isdir("model_weights") else "outputs")
+)
+
+# Model configuration
+MODEL_CONFIG = {
+    "Figure2CNN (Baseline)": {
+        "class": Figure2CNN,
+        "path": f"{MODEL_WEIGHTS_DIR}/figure2_model.pth",
+        "emoji": "",
+        "description": "Baseline CNN with standard filters",
+        "accuracy": "94.80%",
+        "f1": "94.30%"
+    },
+    "ResNet1D (Advanced)": {
+        "class": ResNet1D,
+        "path": f"{MODEL_WEIGHTS_DIR}/resnet_model.pth",
+        "emoji": "",
+        "description": "Residual CNN with deeper feature learning",
+        "accuracy": "96.20%",
+        "f1": "95.90%"
+    }
+}
+
+# ==Label mapping==
+LABEL_MAP = {0: "Stable (Unweathered)", 1: "Weathered (Degraded)"}

core_logic.py

@@ -0,0 +1,177 @@
+import os
+
+# --- New Imports ---
+from config import MODEL_CONFIG, TARGET_LEN
+import time
+import gc
+import torch
+import torch.nn.functional as F
+import numpy as np
+import streamlit as st
+from pathlib import Path
+from config import SAMPLE_DATA_DIR
+
+
+def label_file(filename: str) -> int:
+    """Extract label from filename based on naming convention"""
+    name = Path(filename).name.lower()
+    if name.startswith("sta"):
+        return 0
+    elif name.startswith("wea"):
+        return 1
+    else:
+        # Return None for unknown patterns instead of raising error
+        return -1  # Default value for unknown patterns
+
+
+@st.cache_data
+def load_state_dict(_mtime, model_path):
+    """Load state dict with mtime in cache key to detect file changes"""
+    try:
+        return torch.load(model_path, map_location="cpu")
+    except (FileNotFoundError, RuntimeError) as e:
+        st.warning(f"Error loading state dict: {e}")
+        return None
+
+
+@st.cache_resource
+def load_model(model_name):
+    """Load and cache the specified model with error handling"""
+    try:
+        config = MODEL_CONFIG[model_name]
+        model_class = config["class"]
+        model_path = config["path"]
+
+        # Initialize model
+        model = model_class(input_length=TARGET_LEN)
+
+        # Check if model file exists
+        if not os.path.exists(model_path):
+            st.warning(f"⚠️ Model weights not found: {model_path}")
+            st.info("Using randomly initialized model for demonstration purposes.")
+            return model, False
+
+        # Get mtime for cache invalidation
+        mtime = os.path.getmtime(model_path)
+
+        # Load weights
+        state_dict = load_state_dict(mtime, model_path)
+        if state_dict:
+            model.load_state_dict(state_dict, strict=True)
+            if model is None:
+                raise ValueError(
+                    "Model is not loaded. Please check the model configuration or weights."
+                )
+            if model is None:
+                raise ValueError(
+                    "Model is not loaded. Please check the model configuration or weights."
+                )
+            if model is None:
+                raise ValueError(
+                    "Model is not loaded. Please check the model configuration or weights."
+                )
+            model.eval()
+            return model, True
+        else:
+            return model, False
+
+    except (FileNotFoundError, KeyError, RuntimeError) as e:
+        st.error(f"❌ Error loading model {model_name}: {str(e)}")
+        return None, False
+
+
+def cleanup_memory():
+    """Clean up memory after inference"""
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+
+@st.cache_data
+def run_inference(y_resampled, model_choice, _cache_key=None):
+    """Run model inference and cache results"""
+    model, model_loaded = load_model(model_choice)
+    if not model_loaded:
+        return None, None, None, None, None
+
+    input_tensor = (
+        torch.tensor(y_resampled, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
+    )
+    start_time = time.time()
+    model.eval()
+    with torch.no_grad():
+        if model is None:
+            raise ValueError(
+                "Model is not loaded. Please check the model configuration or weights."
+            )
+        logits = model(input_tensor)
+        prediction = torch.argmax(logits, dim=1).item()
+        logits_list = logits.detach().numpy().tolist()[0]
+        probs = F.softmax(logits.detach(), dim=1).cpu().numpy().flatten()
+    inference_time = time.time() - start_time
+    cleanup_memory()
+    return prediction, logits_list, probs, inference_time, logits
+
+
+@st.cache_data
+def get_sample_files():
+    """Get list of sample files if available"""
+    sample_dir = Path(SAMPLE_DATA_DIR)
+    if sample_dir.exists():
+        return sorted(list(sample_dir.glob("*.txt")))
+    return []
+
+
+def parse_spectrum_data(raw_text):
+    """Parse spectrum data from text with robust error handling and validation"""
+    x_vals, y_vals = [], []
+
+    for line in raw_text.splitlines():
+        line = line.strip()
+        if not line or line.startswith("#"):  # Skip empty lines and comments
+            continue
+
+        try:
+            # Handle different separators
+            parts = line.replace(",", " ").split()
+            numbers = [
+                p
+                for p in parts
+                if p.replace(".", "", 1)
+                .replace("-", "", 1)
+                .replace("+", "", 1)
+                .isdigit()
+            ]
+
+            if len(numbers) >= 2:
+                x, y = float(numbers[0]), float(numbers[1])
+                x_vals.append(x)
+                y_vals.append(y)
+
+        except ValueError:
+            # Skip problematic lines but don't fail completely
+            continue
+
+    if len(x_vals) < 10:  # Minimum reasonable spectrum length
+        raise ValueError(
+            f"Insufficient data points: {len(x_vals)}. Need at least 10 points."
+        )
+
+    x = np.array(x_vals)
+    y = np.array(y_vals)
+
+    # Check for NaNs
+    if np.any(np.isnan(x)) or np.any(np.isnan(y)):
+        raise ValueError("Input data contains NaN values")
+
+    # Check monotonic increasing x
+    if not np.all(np.diff(x) > 0):
+        raise ValueError("Wavenumbers must be strictly increasing")
+
+    # Check reasonable range for Raman spectroscopy
+    if min(x) < 0 or max(x) > 10000 or (max(x) - min(x)) < 100:
+        raise ValueError(
+            f"Invalid wavenumber range: {min(x)} - {max(x)}. Expected ~400-4000 cm⁻¹ with span >100"
+        )
+
+    return x, y

modules/callbacks.py

@@ -0,0 +1,138 @@
+import streamlit as st
+from pathlib import Path
+from utils.results_manager import ResultsManager
+from utils.errors import ErrorHandler
+from config import SAMPLE_DATA_DIR
+
+
+def init_session_state():
+    """Keep a persistent session state"""
+    defaults = {
+        "status_message": "Ready to analyze polymer spectra 🔬",
+        "status_type": "info",
+        "input_text": None,
+        "filename": None,
+        "input_source": None,  # "upload", "batch" or "sample"
+        "sample_select": "-- Select Sample --",
+        "input_mode": "Upload File",  # controls which pane is visible
+        "inference_run_once": False,
+        "x_raw": None,
+        "y_raw": None,
+        "y_resampled": None,
+        "log_messages": [],
+        "uploader_version": 0,
+        "current_upload_key": "upload_txt_0",
+        "active_tab": "Details",
+        "batch_mode": False,
+    }
+
+    if "uploader_key" not in st.session_state:
+        st.session_state.uploader_key = 0
+
+    for k, v in defaults.items():
+        st.session_state.setdefault(k, v)
+
+    for key, default_value in defaults.items():
+        if key not in st.session_state:
+            st.session_state[key] = default_value
+
+    # ==Initialize results table==
+    ResultsManager.init_results_table()
+
+
+def log_message(msg: str):
+    """Append a timestamped line to the in-app log, creating the buffer if needed."""
+    ErrorHandler.log_info(msg)
+
+
+def on_sample_change():
+    """Read selected sample once and persist as text."""
+    sel = st.session_state.get("sample_select", "-- Select Sample --")
+    if sel == "-- Select Sample --":
+        return
+    try:
+        text = Path(SAMPLE_DATA_DIR / sel).read_text(encoding="utf-8")
+        st.session_state["input_text"] = text
+        st.session_state["filename"] = sel
+        st.session_state["input_source"] = "sample"
+        # 🔧 Clear previous results so right column resets immediately
+        reset_results("New sample selected")
+        st.session_state["status_message"] = f"📁 Sample '{sel}' ready for analysis"
+        st.session_state["status_type"] = "success"
+    except (FileNotFoundError, IOError) as e:
+        st.session_state["status_message"] = f"❌ Error loading sample: {e}"
+        st.session_state["status_type"] = "error"
+
+
+def on_input_mode_change():
+    """Reset sample when switching to Upload"""
+    if st.session_state["input_mode"] == "Upload File":
+        st.session_state["sample_select"] = "-- Select Sample --"
+        st.session_state["batch_mode"] = False  # Reset batch mode
+    elif st.session_state["input_mode"] == "Sample Data":
+        st.session_state["batch_mode"] = False  # Reset batch mode
+    # 🔧 Reset when switching modes to prevent stale right-column visuals
+    reset_results("Switched input mode")
+
+
+def reset_ephemeral_state():
+    """Comprehensive reset for the entire app state."""
+    # Define keys that should NOT be cleared by a full reset
+    keep_keys = {"model_select", "input_mode"}
+
+    for k in list(st.session_state.keys()):
+        if k not in keep_keys:
+            st.session_state.pop(k, None)
+
+    # Re-initialize the core state after clearing
+    init_session_state()
+
+    # CRITICAL: Bump the uploader version to force a widget reset
+    st.session_state["uploader_version"] += 1
+    st.session_state["current_upload_key"] = (
+        f"upload_txt_{st.session_state['uploader_version']}"
+    )
+
+
+def on_model_change():
+    """Force the right column back to init state when the model changes"""
+    reset_results("Model changed")
+
+
+def reset_results(reason: str = ""):
+    """Clear previous inference artifacts so the right column returns to initial state."""
+    st.session_state["inference_run_once"] = False
+    st.session_state["x_raw"] = None
+    st.session_state["y_raw"] = None
+    st.session_state["y_resampled"] = None
+    # ||== Clear batch results when resetting ==||
+    if "batch_results" in st.session_state:
+        del st.session_state["batch_results"]
+    # ||== Clear logs between runs ==||
+    st.session_state["log_messages"] = []
+    # ||== Always reset the status box ==||
+    st.session_state["status_message"] = (
+        f"ℹ️ {reason}" if reason else "Ready to analyze polymer spectra 🔬"
+    )
+    st.session_state["status_type"] = "info"
+
+
+def clear_batch_results():
+    """Callback to clear only the batch results and the results log table."""
+    if "batch_results" in st.session_state:
+        del st.session_state["batch_results"]
+    # Also clear the persistent table from the ResultsManager utility
+    ResultsManager.clear_results()
+
+
+# --- END: BUG 2 FIX (Callback Function) ---
+
+
+def reset_all():
+    # Increment the key to force the file uploader to re-render
+    st.session_state.uploader_key += 1
+
+
+def trigger_run():
+    """Set a flag so we can detect button press reliably across reruns"""
+    st.session_state["run_requested"] = True

modules/ui_components.py

@@ -0,0 +1,934 @@
+import os
+import torch
+import streamlit as st
+import hashlib
+import io
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+from typing import Union
+import time
+from config import MODEL_CONFIG, TARGET_LEN, LABEL_MAP
+from modules.callbacks import (
+    on_model_change,
+    on_input_mode_change,
+    on_sample_change,
+    reset_ephemeral_state,
+    log_message,
+    clear_batch_results,
+)
+from core_logic import (
+    get_sample_files,
+    load_model,
+    run_inference,
+    parse_spectrum_data,
+    label_file,
+)
+from modules.callbacks import reset_results
+from utils.results_manager import ResultsManager
+from utils.confidence import calculate_softmax_confidence
+from utils.multifile import process_multiple_files, display_batch_results
+from utils.preprocessing import resample_spectrum
+
+
+def load_css(file_path):
+    with open(file_path, encoding="utf-8") as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+
+@st.cache_data
+def create_spectrum_plot(x_raw, y_raw, x_resampled, y_resampled, _cache_key=None):
+    """Create spectrum visualization plot"""
+    fig, ax = plt.subplots(1, 2, figsize=(13, 5), dpi=100)
+
+    # == Raw spectrum ==
+    ax[0].plot(x_raw, y_raw, label="Raw", color="dimgray", linewidth=1)
+    ax[0].set_title("Raw Input Spectrum")
+    ax[0].set_xlabel("Wavenumber (cm⁻¹)")
+    ax[0].set_ylabel("Intensity")
+    ax[0].grid(True, alpha=0.3)
+    ax[0].legend()
+
+    # == Resampled spectrum ==
+    ax[1].plot(
+        x_resampled, y_resampled, label="Resampled", color="steelblue", linewidth=1
+    )
+    ax[1].set_title(f"Resampled ({len(y_resampled)} points)")
+    ax[1].set_xlabel("Wavenumber (cm⁻¹)")
+    ax[1].set_ylabel("Intensity")
+    ax[1].grid(True, alpha=0.3)
+    ax[1].legend()
+
+    fig.tight_layout()
+    # == Convert to image ==
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png", bbox_inches="tight", dpi=100)
+    buf.seek(0)
+    plt.close(fig)  # Prevent memory leaks
+
+    return Image.open(buf)
+
+
+def render_confidence_progress(
+    probs: np.ndarray,
+    labels: list[str] = ["Stable", "Weathered"],
+    highlight_idx: Union[int, None] = None,
+    side_by_side: bool = True,
+):
+    """Render Streamlit native progress bars with scientific formatting."""
+    p = np.asarray(probs, dtype=float)
+    p = np.clip(p, 0.0, 1.0)
+
+    if side_by_side:
+        cols = st.columns(len(labels))
+        for i, (lbl, val, col) in enumerate(zip(labels, p, cols)):
+            with col:
+                is_highlighted = highlight_idx is not None and i == highlight_idx
+                label_text = f"**{lbl}**" if is_highlighted else lbl
+                st.markdown(f"{label_text}: {val*100:.1f}%")
+                st.progress(int(round(val * 100)))
+    else:
+        # Vertical layout for better readability
+        for i, (lbl, val) in enumerate(zip(labels, p)):
+            is_highlighted = highlight_idx is not None and i == highlight_idx
+
+            # Create a container for each probability
+            with st.container():
+                col1, col2 = st.columns([3, 1])
+                with col1:
+                    if is_highlighted:
+                        st.markdown(f"**{lbl}** ← Predicted")
+                    else:
+                        st.markdown(f"{lbl}")
+                with col2:
+                    st.metric(label="", value=f"{val*100:.1f}%", delta=None)
+
+                # Progress bar with conditional styling
+                if is_highlighted:
+                    st.progress(int(round(val * 100)))
+                    st.caption("🎯 **Model Prediction**")
+                else:
+                    st.progress(int(round(val * 100)))
+
+                if i < len(labels) - 1:  # Add spacing between items
+                    st.markdown("")
+
+
+def render_kv_grid(d: dict = {}, ncols: int = 2):
+    if d is None:
+        d = {}
+    if not d:
+        return
+    items = list(d.items())
+    cols = st.columns(ncols)
+    for i, (k, v) in enumerate(items):
+        with cols[i % ncols]:
+            st.caption(f"**{k}:** {v}")
+
+
+def render_model_meta(model_choice: str):
+    info = MODEL_CONFIG.get(model_choice, {})
+    emoji = info.get("emoji", "")
+    desc = info.get("description", "").strip()
+    acc = info.get("accuracy", "-")
+    f1 = info.get("f1", "-")
+
+    st.caption(f"{emoji} **Model Snapshot** - {model_choice}")
+    cols = st.columns(2)
+    with cols[0]:
+        st.metric("Accuracy", acc)
+    with cols[1]:
+        st.metric("F1 Score", f1)
+    if desc:
+        st.caption(desc)
+
+
+def get_confidence_description(logit_margin):
+    """Get human-readable confidence description"""
+    if logit_margin > 1000:
+        return "VERY HIGH", "🟢"
+    elif logit_margin > 250:
+        return "HIGH", "🟡"
+    elif logit_margin > 100:
+        return "MODERATE", "🟠"
+    else:
+        return "LOW", "🔴"
+
+
+def render_sidebar():
+    with st.sidebar:
+        # Header
+        st.header("AI-Driven Polymer Classification")
+        st.caption(
+            "Predict polymer degradation (Stable vs Weathered) from Raman spectra using validated CNN models. — v0.1"
+        )
+        model_labels = [
+            f"{MODEL_CONFIG[name]['emoji']} {name}" for name in MODEL_CONFIG.keys()
+        ]
+        selected_label = st.selectbox(
+            "Choose AI Model",
+            model_labels,
+            key="model_select",
+            on_change=on_model_change,
+        )
+        model_choice = selected_label.split(" ", 1)[1]
+
+        # ===Compact metadata directly under dropdown===
+        render_model_meta(model_choice)
+
+        # ===Collapsed info to reduce clutter===
+        with st.expander("About This App", icon=":material/info:", expanded=False):
+            st.markdown(
+                """
+            AI-Driven Polymer Aging Prediction and Classification
+
+            **Purpose**: Classify polymer degradation using AI
+            **Input**: Raman spectroscopy `.txt` files
+            **Models**: CNN architectures for binary classification
+            **Next**: More trained CNNs in evaluation pipeline
+
+
+            **Contributors**
+            Dr. Sanmukh Kuppannagari (Mentor)
+            Dr. Metin Karailyan (Mentor)
+            Jaser Hasan (Author)
+
+
+            **Links**
+            [Live HF Space](https://huggingface.co/spaces/dev-jas/polymer-aging-ml)
+            [GitHub Repository](https://github.com/KLab-AI3/ml-polymer-recycling)
+
+
+            **Citation Figure2CNN (baseline)**
+            Neo et al., 2023, *Resour. Conserv. Recycl.*, 188, 106718.
+            [https://doi.org/10.1016/j.resconrec.2022.106718](https://doi.org/10.1016/j.resconrec.2022.106718)
+            """,
+            )
+
+
+# col1 goes here
+
+# In modules/ui_components.py
+
+
+def render_input_column():
+    st.markdown("##### Data Input")
+
+    mode = st.radio(
+        "Input mode",
+        ["Upload File", "Batch Upload", "Sample Data"],
+        key="input_mode",
+        horizontal=True,
+        on_change=on_input_mode_change,
+    )
+
+    # == Input Mode Logic ==
+    # ... (The if/elif/else block for Upload, Batch, and Sample modes remains exactly the same) ...
+    # ==Upload tab==
+    if mode == "Upload File":
+        upload_key = st.session_state["current_upload_key"]
+        up = st.file_uploader(
+            "Upload Raman spectrum (.txt)",
+            type="txt",
+            help="Upload a text file with wavenumber and intensity columns",
+            key=upload_key,  # ← versioned key
+        )
+
+        # ==Process change immediately (no on_change; simpler & reliable)==
+        if up is not None:
+            raw = up.read()
+            text = raw.decode("utf-8") if isinstance(raw, bytes) else raw
+            # == only reparse if its a different file|source ==
+            if (
+                st.session_state.get("filename") != getattr(up, "name", None)
+                or st.session_state.get("input_source") != "upload"
+            ):
+                st.session_state["input_text"] = text
+                st.session_state["filename"] = getattr(up, "name", None)
+                st.session_state["input_source"] = "upload"
+                # Ensure single file mode
+                st.session_state["batch_mode"] = False
+                st.session_state["status_message"] = (
+                    f"File '{st.session_state['filename']}' ready for analysis"
+                )
+                st.session_state["status_type"] = "success"
+                reset_results("New file uploaded")
+
+    # ==Batch Upload tab==
+    elif mode == "Batch Upload":
+        st.session_state["batch_mode"] = True
+        # --- START: BUG 1 & 3 FIX ---
+        # Use a versioned key to ensure the file uploader resets properly.
+        batch_upload_key = f"batch_upload_{st.session_state['uploader_version']}"
+        uploaded_files = st.file_uploader(
+            "Upload multiple Raman spectrum files (.txt)",
+            type="txt",
+            accept_multiple_files=True,
+            help="Upload one or more text files with wavenumber and intensity columns.",
+            key=batch_upload_key,
+        )
+        # --- END: BUG 1 & 3 FIX ---
+
+        if uploaded_files:
+            # --- START: Bug 1 Fix ---
+            # Use a dictionary to keep only unique files based on name and size
+            unique_files = {(file.name, file.size): file for file in uploaded_files}
+            unique_file_list = list(unique_files.values())
+
+            num_uploaded = len(uploaded_files)
+            num_unique = len(unique_file_list)
+
+            # Optionally, inform the user that duplicates were removed
+            if num_uploaded > num_unique:
+                st.info(
+                    f"ℹ️ {num_uploaded - num_unique} duplicate file(s) were removed."
+                )
+
+            # Use the unique list
+            st.session_state["batch_files"] = unique_file_list
+            st.session_state["status_message"] = (
+                f"{num_unique} ready for batch analysis"
+            )
+            st.session_state["status_type"] = "success"
+            # --- END: Bug 1 Fix ---
+        else:
+            st.session_state["batch_files"] = []
+            # This check prevents resetting the status if files are already staged
+            if not st.session_state.get("batch_files"):
+                st.session_state["status_message"] = (
+                    "No files selected for batch processing"
+                )
+                st.session_state["status_type"] = "info"
+
+    # ==Sample tab==
+    elif mode == "Sample Data":
+        st.session_state["batch_mode"] = False
+        sample_files = get_sample_files()
+        if sample_files:
+            options = ["-- Select Sample --"] + [p.name for p in sample_files]
+            sel = st.selectbox(
+                "Choose sample spectrum:",
+                options,
+                key="sample_select",
+                on_change=on_sample_change,
+            )
+            if sel != "-- Select Sample --":
+                st.session_state["status_message"] = (
+                    f"📁 Sample '{sel}' ready for analysis"
+                )
+                st.session_state["status_type"] = "success"
+        else:
+            st.info("No sample data available")
+    # == Status box (displays the message) ==
+    msg = st.session_state.get("status_message", "Ready")
+    typ = st.session_state.get("status_type", "info")
+    if typ == "success":
+        st.success(msg)
+    elif typ == "error":
+        st.error(msg)
+    else:
+        st.info(msg)
+
+    # --- DE-NESTED LOGIC STARTS HERE ---
+    # This code now runs on EVERY execution, guaranteeing the buttons will appear.
+
+    # Safely get model choice from session state
+    model_choice = st.session_state.get("model_select", " ").split(" ", 1)[1]
+    model = load_model(model_choice)
+
+    # Determine if the app is ready for inference
+    is_batch_ready = st.session_state.get("batch_mode", False) and st.session_state.get(
+        "batch_files"
+    )
+    is_single_ready = not st.session_state.get(
+        "batch_mode", False
+    ) and st.session_state.get("input_text")
+    inference_ready = (is_batch_ready or is_single_ready) and model is not None
+    # Store for other modules to access
+    st.session_state["inference_ready"] = inference_ready
+
+    # Render buttons
+    with st.form("analysis_form", clear_on_submit=False):
+        submitted = st.form_submit_button(
+            "Run Analysis", type="primary", disabled=not inference_ready
+        )
+    st.button(
+        "Reset All",
+        on_click=reset_ephemeral_state,
+        help="Clear all uploaded files and results.",
+    )
+
+    # Handle form submission
+    if submitted and inference_ready:
+        if st.session_state.get("batch_mode"):
+            batch_files = st.session_state.get("batch_files", [])
+            with st.spinner(f"Processing {len(batch_files)} files ..."):
+                st.session_state["batch_results"] = process_multiple_files(
+                    uploaded_files=batch_files,
+                    model_choice=model_choice,
+                    load_model_func=load_model,
+                    run_inference_func=run_inference,
+                    label_file_func=label_file,
+                )
+        else:
+            try:
+                x_raw, y_raw = parse_spectrum_data(st.session_state["input_text"])
+                x_resampled, y_resampled = resample_spectrum(x_raw, y_raw, TARGET_LEN)
+                st.session_state.update(
+                    {
+                        "x_raw": x_raw,
+                        "y_raw": y_raw,
+                        "x_resampled": x_resampled,
+                        "y_resampled": y_resampled,
+                        "inference_run_once": True,
+                    }
+                )
+            except (ValueError, TypeError) as e:
+                st.error(f"Error processing spectrum data: {e}")
+
+
+# col2 goes here
+
+
+def render_results_column():
+    # Check if we're in batch more or have batch results
+    is_batch_mode = st.session_state.get("batch_mode", False)
+    has_batch_results = "batch_results" in st.session_state
+
+    if is_batch_mode and has_batch_results:
+        # Display batch results
+        st.markdown("##### Batch Analysis Results")
+        batch_results = st.session_state["batch_results"]
+        display_batch_results(batch_results)
+
+        # Add session results table
+        st.markdown("---")
+
+        st.button(
+            "Clear Results",
+            on_click=clear_batch_results,
+            help="Clear all uploaded files and results.",
+        )
+
+        ResultsManager.display_results_table()
+
+    elif st.session_state.get("inference_run_once", False) and not is_batch_mode:
+        st.markdown("##### Analysis Results")
+
+        # Get data from session state
+        x_raw = st.session_state.get("x_raw")
+        y_raw = st.session_state.get("y_raw")
+        x_resampled = st.session_state.get("x_resampled")  # ← NEW
+        y_resampled = st.session_state.get("y_resampled")
+        filename = st.session_state.get("filename", "Unknown")
+
+        if all(v is not None for v in [x_raw, y_raw, y_resampled]):
+            # ===Run inference===
+            if y_resampled is None:
+                raise ValueError(
+                    "y_resampled is None. Ensure spectrum data is properly resampled before proceeding."
+                )
+            cache_key = hashlib.md5(
+                f"{y_resampled.tobytes()}{st.session_state.get('model_select', 'Unknown').split(' ', 1)[1]}".encode()
+            ).hexdigest()
+            prediction, logits_list, probs, inference_time, logits = run_inference(
+                y_resampled,
+                (
+                    st.session_state.get("model_select", "").split(" ", 1)[1]
+                    if "model_select" in st.session_state
+                    else None
+                ),
+                _cache_key=cache_key,
+            )
+            if prediction is None:
+                st.error(
+                    "❌ Inference failed: Model not loaded. Please check that weights are available."
+                )
+                st.stop()  # prevents the rest of the code in this block from executing
+
+            log_message(
+                f"Inference completed in {inference_time:.2f}s, prediction: {prediction}"
+            )
+
+            # ===Get ground truth===
+            true_label_idx = label_file(filename)
+            true_label_str = (
+                LABEL_MAP.get(true_label_idx, "Unknown")
+                if true_label_idx is not None
+                else "Unknown"
+            )
+            # ===Get prediction===
+            predicted_class = LABEL_MAP.get(int(prediction), f"Class {int(prediction)}")
+
+            # Enhanced confidence calculation
+            if logits is not None:
+                # Use new softmax-based confidence
+                probs_np, max_confidence, confidence_level, confidence_emoji = (
+                    calculate_softmax_confidence(logits)
+                )
+                confidence_desc = confidence_level
+            else:
+                # Fallback to legace method
+                logit_margin = abs(
+                    (logits_list[0] - logits_list[1])
+                    if logits_list is not None and len(logits_list) >= 2
+                    else 0
+                )
+                confidence_desc, confidence_emoji = get_confidence_description(
+                    logit_margin
+                )
+                max_confidence = logit_margin / 10.0  # Normalize for display
+                probs_np = np.array([])
+
+            # Store result in results manager for single file too
+            ResultsManager.add_results(
+                filename=filename,
+                model_name=(
+                    st.session_state.get("model_select", "").split(" ", 1)[1]
+                    if "model_select" in st.session_state
+                    else "Unknown"
+                ),
+                prediction=int(prediction),
+                predicted_class=predicted_class,
+                confidence=max_confidence,
+                logits=logits_list if logits_list else [],
+                ground_truth=true_label_idx if true_label_idx >= 0 else None,
+                processing_time=inference_time if inference_time is not None else 0.0,
+                metadata={
+                    "confidence_level": confidence_desc,
+                    "confidence_emoji": confidence_emoji,
+                },
+            )
+
+            # ===Precompute Stats===
+            model_choice = (
+                st.session_state.get("model_select", "").split(" ", 1)[1]
+                if "model_select" in st.session_state
+                else None
+            )
+            if not model_choice:
+                st.error(
+                    "⚠️ Model choice is not defined. Please select a model from the sidebar."
+                )
+                st.stop()
+            model_path = MODEL_CONFIG[model_choice]["path"]
+            mtime = os.path.getmtime(model_path) if os.path.exists(model_path) else None
+            file_hash = (
+                hashlib.md5(open(model_path, "rb").read()).hexdigest()
+                if os.path.exists(model_path)
+                else "N/A"
+            )
+            # Removed unused variable 'input_tensor'
+
+            start_render = time.time()
+
+            active_tab = st.selectbox(
+                "View Results",
+                ["Details", "Technical", "Explanation"],
+                key="active_tab",  # reuse the key you were managing manually
+            )
+
+            if active_tab == "Details":
+                st.markdown('<div class="expander-results">', unsafe_allow_html=True)
+                # Use a dynamic and informative title for the expander
+                with st.expander(f"Results for {filename}", expanded=True):
+
+                    # --- START: STREAMLINED METRICS ---
+                    # A single, powerful row for the most important results.
+                    key_metric_cols = st.columns(3)
+
+                    # Metric 1: The Prediction
+                    key_metric_cols[0].metric("Prediction", predicted_class)
+
+                    # Metric 2: The Confidence (with level in tooltip)
+                    confidence_icon = (
+                        "🟢"
+                        if max_confidence >= 0.8
+                        else "🟡" if max_confidence >= 0.6 else "🔴"
+                    )
+                    key_metric_cols[1].metric(
+                        "Confidence",
+                        f"{confidence_icon} {max_confidence:.1%}",
+                        help=f"Confidence Level: {confidence_desc}",
+                    )
+
+                    # Metric 3: Ground Truth + Correctness (Combined)
+                    if true_label_idx is not None:
+                        is_correct = predicted_class == true_label_str
+                        delta_text = "✅ Correct" if is_correct else "❌ Incorrect"
+                        # Use delta_color="normal" to let the icon provide the visual cue
+                        key_metric_cols[2].metric(
+                            "Ground Truth",
+                            true_label_str,
+                            delta=delta_text,
+                            delta_color="normal",
+                        )
+                    else:
+                        key_metric_cols[2].metric("Ground Truth", "N/A")
+
+                    st.divider()
+                    # --- END: STREAMLINED METRICS ---
+
+                    # --- START: CONSOLIDATED CONFIDENCE ANALYSIS ---
+                    st.markdown("##### Probability Breakdown")
+
+                    # This custom bullet bar logic remains as it is highly specific and valuable
+                    def create_bullet_bar(probability, width=20, predicted=False):
+                        filled_count = int(probability * width)
+                        bar = "▤" * filled_count + "▢" * (width - filled_count)
+                        percentage = f"{probability:.1%}"
+                        pred_marker = "↩ Predicted" if predicted else ""
+                        return f"{bar} {percentage}    {pred_marker}"
+
+                    if probs is not None:
+                        stable_prob, weathered_prob = probs[0], probs[1]
+                    else:
+                        st.error(
+                            "❌ Probability values are missing. Please check the inference process."
+                        )
+                        # Default values to prevent further errors
+                        stable_prob, weathered_prob = 0.0, 0.0
+                    is_stable_predicted, is_weathered_predicted = (
+                        int(prediction) == 0
+                    ), (int(prediction) == 1)
+
+                    st.markdown(
+                        f"""
+                        <div style="font-family: 'Fira Code', monospace;">
+                            Stable (Unweathered)<br>
+                            {create_bullet_bar(stable_prob, predicted=is_stable_predicted)}<br><br>
+                            Weathered (Degraded)<br>
+                            {create_bullet_bar(weathered_prob, predicted=is_weathered_predicted)}
+                        </div>
+                    """,
+                        unsafe_allow_html=True,
+                    )
+                    # --- END: CONSOLIDATED CONFIDENCE ANALYSIS ---
+
+                    st.divider()
+
+                    # --- START: CLEAN METADATA FOOTER ---
+                    # Secondary info is now a clean, single-line caption
+                    st.caption(
+                        f"Analyzed with **{st.session_state.get('model_select', 'Unknown')}** in **{inference_time:.2f}s**."
+                    )
+                    # --- END: CLEAN METADATA FOOTER ---
+
+                st.markdown("</div>", unsafe_allow_html=True)
+
+            elif active_tab == "Technical":
+                with st.container():
+                    st.markdown("Technical Diagnostics")
+
+                    # Model performance metrics
+                    with st.container(border=True):
+                        st.markdown("##### **Model Performance**")
+                        tech_col1, tech_col2 = st.columns(2)
+
+                        with tech_col1:
+                            st.metric("Inference Time", f"{inference_time:.3f}s")
+                            st.metric(
+                                "Input Length",
+                                f"{len(x_raw) if x_raw is not None else 0} points",
+                            )
+                            st.metric("Resampled Length", f"{TARGET_LEN} points")
+
+                        with tech_col2:
+                            st.metric(
+                                "Model Loaded",
+                                (
+                                    "✅ Yes"
+                                    if st.session_state.get("model_loaded", False)
+                                    else "❌ No"
+                                ),
+                            )
+                            st.metric("Device", "CPU")
+                            st.metric("Confidence Score", f"{max_confidence:.3f}")
+
+                    # Raw logits display
+                    with st.container(border=True):
+                        st.markdown("##### **Raw Model Outputs (Logits)**")
+                        logits_df = {
+                            "Class": (
+                                [
+                                    LABEL_MAP.get(i, f"Class {i}")
+                                    for i in range(len(logits_list))
+                                ]
+                                if logits_list is not None
+                                else []
+                            ),
+                            "Logit Value": (
+                                [f"{score:.4f}" for score in logits_list]
+                                if logits_list is not None
+                                else []
+                            ),
+                            "Probability": (
+                                [f"{prob:.4f}" for prob in probs_np]
+                                if logits_list is not None and len(probs_np) > 0
+                                else []
+                            ),
+                        }
+
+                        # Display as a simple table format
+                        for i, (cls, logit, prob) in enumerate(
+                            zip(
+                                logits_df["Class"],
+                                logits_df["Logit Value"],
+                                logits_df["Probability"],
+                            )
+                        ):
+                            col1, col2, col3 = st.columns([2, 1, 1])
+                            with col1:
+                                if i == prediction:
+                                    st.markdown(f"**{cls}** ← Predicted")
+                                else:
+                                    st.markdown(cls)
+                            with col2:
+                                st.caption(f"Logit: {logit}")
+                            with col3:
+                                st.caption(f"Prob: {prob}")
+
+                    # Spectrum statistics in organized sections
+                    with st.container(border=True):
+                        st.markdown("##### **Spectrum Analysis**")
+                        spec_cols = st.columns(2)
+
+                        with spec_cols[0]:
+                            st.markdown("**Original Spectrum:**")
+                            render_kv_grid(
+                                {
+                                    "Length": f"{len(x_raw) if x_raw is not None else 0} points",
+                                    "Range": (
+                                        f"{min(x_raw):.1f} - {max(x_raw):.1f} cm⁻¹"
+                                        if x_raw is not None
+                                        else "N/A"
+                                    ),
+                                    "Min Intensity": (
+                                        f"{min(y_raw):.2e}"
+                                        if y_raw is not None
+                                        else "N/A"
+                                    ),
+                                    "Max Intensity": (
+                                        f"{max(y_raw):.2e}"
+                                        if y_raw is not None
+                                        else "N/A"
+                                    ),
+                                },
+                                ncols=1,
+                            )
+
+                        with spec_cols[1]:
+                            st.markdown("**Processed Spectrum:**")
+                            render_kv_grid(
+                                {
+                                    "Length": f"{TARGET_LEN} points",
+                                    "Resampling": "Linear interpolation",
+                                    "Normalization": "None",
+                                    "Input Shape": f"(1, 1, {TARGET_LEN})",
+                                },
+                                ncols=1,
+                            )
+
+                    # Model information
+                    with st.container(border=True):
+                        st.markdown("##### **Model Information**")
+                        model_info_cols = st.columns(2)
+
+                        with model_info_cols[0]:
+                            render_kv_grid(
+                                {
+                                    "Architecture": model_choice,
+                                    "Path": MODEL_CONFIG[model_choice]["path"],
+                                    "Weights Modified": (
+                                        time.strftime(
+                                            "%Y-%m-%d %H:%M:%S", time.localtime(mtime)
+                                        )
+                                        if mtime
+                                        else "N/A"
+                                    ),
+                                },
+                                ncols=1,
+                            )
+
+                        with model_info_cols[1]:
+                            if os.path.exists(model_path):
+                                file_hash = hashlib.md5(
+                                    open(model_path, "rb").read()
+                                ).hexdigest()
+                                render_kv_grid(
+                                    {
+                                        "Weights Hash": f"{file_hash[:16]}...",
+                                        "Output Shape": f"(1, {len(LABEL_MAP)})",
+                                        "Activation": "Softmax",
+                                    },
+                                    ncols=1,
+                                )
+
+                    # Debug logs (collapsed by default)
+                    with st.expander("📋 Debug Logs", expanded=False):
+                        log_content = "\n".join(
+                            st.session_state.get("log_messages", [])
+                        )
+                        if log_content.strip():
+                            st.code(log_content, language="text")
+                        else:
+                            st.caption("No debug logs available")
+
+            elif active_tab == "Explanation":
+                with st.container():
+                    st.markdown("### 🔍 Methodology & Interpretation")
+
+                    # Process explanation
+                    st.markdown("Analysis Pipeline")
+                    process_steps = [
+                        "📁 **Data Upload**: Raman spectrum file loaded and validated",
+                        "🔍 **Preprocessing**: Spectrum parsed and resampled to 500 data points using linear interpolation",
+                        "🧠 **AI Inference**: Convolutional Neural Network analyzes spectral patterns and molecular signatures",
+                        "📊 **Classification**: Binary prediction with confidence scoring using softmax probabilities",
+                        "✅ **Validation**: Ground truth comparison (when available from filename)",
+                    ]
+
+                    for step in process_steps:
+                        st.markdown(step)
+
+                    st.markdown("---")
+
+                    # Model interpretation
+                    st.markdown("#### Scientific Interpretation")
+
+                    interp_col1, interp_col2 = st.columns(2)
+
+                    with interp_col1:
+                        st.markdown("**Stable (Unweathered) Polymers:**")
+                        st.info(
+                            """
+                        - Well-preserved molecular structure
+                        - Minimal oxidative degradation
+                        - Characteristic Raman peaks intact
+                        - Suitable for recycling applications
+                        """
+                        )
+
+                    with interp_col2:
+                        st.markdown("**Weathered (Degraded) Polymers:**")
+                        st.warning(
+                            """
+                        - Oxidized molecular bonds
+                        - Surface degradation present
+                        - Altered spectral signatures
+                        - May require additional processing
+                        """
+                        )
+
+                    st.markdown("---")
+
+                    # Applications
+                    st.markdown("#### Research Applications")
+
+                    applications = [
+                        "🔬 **Material Science**: Polymer degradation studies",
+                        "♻️ **Recycling Research**: Viability assessment for circular economy",
+                        "🌱 **Environmental Science**: Microplastic weathering analysis",
+                        "🏭 **Quality Control**: Manufacturing process monitoring",
+                        "📈 **Longevity Studies**: Material aging prediction",
+                    ]
+
+                    for app in applications:
+                        st.markdown(app)
+
+                    # Technical details
+                    # MODIFIED: Wrap the expander in a div with the 'expander-advanced' class
+                    st.markdown(
+                        '<div class="expander-advanced">', unsafe_allow_html=True
+                    )
+                    with st.expander("🔧 Technical Details", expanded=False):
+                        st.markdown(
+                            """
+                        **Model Architecture:**
+                        - Convolutional layers for feature extraction
+                        - Residual connections for gradient flow
+                        - Fully connected layers for classification
+                        - Softmax activation for probability distribution
+
+                        **Performance Metrics:**
+                        - Accuracy: 94.8-96.2% on validation set
+                        - F1-Score: 94.3-95.9% across classes
+                        - Robust to spectral noise and baseline variations
+
+                        **Data Processing:**
+                        - Input: Raman spectra (any length)
+                        - Resampling: Linear interpolation to 500 points
+                        - Normalization: None (preserves intensity relationships)
+                        """
+                        )
+                    st.markdown(
+                        "</div>", unsafe_allow_html=True
+                    )  # Close the wrapper div
+
+                    render_time = time.time() - start_render
+                    log_message(
+                        f"col2 rendered in {render_time:.2f}s, active tab: {active_tab}"
+                    )
+
+            with st.expander("Spectrum Preprocessing Results", expanded=False):
+                st.caption("<br>Spectral Analysis", unsafe_allow_html=True)
+
+                # Add some context about the preprocessing
+                st.markdown(
+                    """
+                **Preprocessing Overview:**
+                - **Original Spectrum**: Raw Raman data as uploaded
+                - **Resampled Spectrum**: Data interpolated to 500 points for model input
+                - **Purpose**: Ensures consistent input dimensions for neural network
+                """
+                )
+
+                # Create and display plot
+                cache_key = hashlib.md5(
+                    f"{(x_raw.tobytes() if x_raw is not None else b'')}"
+                    f"{(y_raw.tobytes() if y_raw is not None else b'')}"
+                    f"{(x_resampled.tobytes() if x_resampled is not None else b'')}"
+                    f"{(y_resampled.tobytes() if y_resampled is not None else b'')}".encode()
+                ).hexdigest()
+                spectrum_plot = create_spectrum_plot(
+                    x_raw, y_raw, x_resampled, y_resampled, _cache_key=cache_key
+                )
+                st.image(
+                    spectrum_plot,
+                    caption="Raman Spectrum: Raw vs Processed",
+                    use_container_width=True,
+                )
+
+        else:
+            st.error("❌ Missing spectrum data. Please upload a file and run analysis.")
+    else:
+        # ===Getting Started===
+        st.markdown(
+            """
+        ##### How to Get Started
+
+        1.  **Select an AI Model:** Use the dropdown menu in the sidebar to choose a model.
+        2.  **Provide Your Data:** Select one of the three input modes:
+            -   **Upload File:** Analyze a single spectrum.
+            -   **Batch Upload:** Process multiple files at once.
+            -   **Sample Data:** Explore functionality with pre-loaded examples.
+        3.  **Run Analysis:** Click the "Run Analysis" button to generate the classification results.
+
+        ---
+
+        ##### Supported Data Format
+
+        -   **File Type:** Plain text (`.txt`)
+        -   **Content:** Must contain two columns: `wavenumber` and `intensity`.
+        -   **Separators:** Values can be separated by spaces or commas.
+        -   **Preprocessing:** Your spectrum will be automatically resampled to 500 data points to match the model's input requirements.
+
+        ---
+
+        ##### Example Applications
+        - 🔬 Research on polymer degradation
+        - ♻️ Recycling feasibility assessment
+        - 🌱 Sustainability impact studies
+        - 🏭 Quality control in manufacturing
+        """
+        )

static/style.css

@@ -0,0 +1,148 @@
+/*  THEME-AWARE CUSTOM CSS
+
+    This CSS block has been refactored to use Streamlit's internal theme
+    variables. This ensures that all custom components will automatically adapt
+    to both light and dark themes selected by the user in the settings menu.
+*/
+/* ====== Font Imports (Optional but Recommended) ====== */
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;700&family=Fira+Code:wght@400&display=swap');
+
+/* ====== Base & Typography ====== */
+.stApp,
+section[data-testid="stSidebar"],
+div[data-testid="stMetricValue"],
+div[data-testid="stMetricLabel"] {
+    font-family: 'Inter', sans-serif;
+    /* Uses the main text color from the current theme (light or dark) */
+    color: var(--text-color);
+}
+
+.kv-val {
+    font-family: 'Fira Code', monospace;
+}
+
+/* ====== Custom Containers: Tabs & Info Boxes ====== */
+div[data-testid="stTabs"]>div[role="tablist"]+div {
+    min-height: 400px;
+    /* Uses the secondary background color, which is different in light and dark modes */
+    background-color: var(--secondary-background-color);
+    /* Border color uses a semi-transparent version of the text color for a subtle effect that works on any background */
+    border: 10px solid rgba(128, 128, 128, 0.2);
+    border-radius: 10px;
+    padding: 24px;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
+}
+
+.info-box {
+    font-size: 0.9rem;
+    padding: 12px 16px;
+    border: 1px solid rgba(128, 128, 128, 0.2);
+    border-radius: 10px;
+    background-color: var(--secondary-background-color);
+}
+
+/* ====== Key-Value Pair Styling ====== */
+.kv-row {
+    display: flex;
+    justify-content: space-between;
+    gap: 16px;
+    padding: 8px 0;
+    border-bottom: 1px solid rgba(128, 128, 128, 0.2);
+}
+
+.kv-row:last-child {
+    border-bottom: none;
+}
+
+.kv-key {
+    opacity: 0.7;
+    font-size: 0.9rem;
+    white-space: nowrap;
+}
+
+.kv-val {
+    font-size: 0.9rem;
+    overflow-wrap: break-word;
+    text-align: right;
+}
+
+/* ====== Custom Expander Styling ====== */
+div.stExpander>details>summary::-webkit-details-marker,
+div.stExpander>details>summary::marker,
+div[data-testid="stExpander"] summary svg {
+    display: none !important;
+}
+
+div.stExpander>details>summary::after {
+    content: 'DETAILS';
+    font-size: 0.75rem;
+    font-weight: 600;
+    letter-spacing: 0.5px;
+    padding: 4px 12px;
+    border-radius: 999px;
+    /* The primary color is set in config.toml and adapted by Streamlit */
+    background-color: var(--primary);
+    /* Text on the primary color needs high contrast. White works well for our chosen purple. */
+
+    transition: background-color 0.2s ease-in-out;
+}
+
+div.stExpander>details>summary:hover::after {
+    /* Using a fixed darker shade on hover. A more advanced solution could use color-mix() in CSS. */
+    filter: brightness(90%);
+}
+
+/* Specialized Expander Labels */
+.expander-results div[data-testid="stExpander"] summary::after {
+    content: "RESULTS";
+    background-color: #16A34A;
+    /* Green is universal for success */
+
+}
+
+div[data-testid="stExpander"] details {
+    content: "RESULTS";
+    background-color: var(--primary);
+    border-radius: 10px;
+    padding: 10px
+}
+
+.expander-advanced div[data-testid="stExpander"] summary::after {
+    content: "ADVANCED";
+    background-color: #D97706;
+    /* Amber is universal for warning/technical */
+
+}
+
+[data-testid="stExpanderDetails"] {
+    padding: 16px 4px 4px 4px;
+    background-color: transparent;
+    border-top: 1px solid rgba(128, 128, 128, 0.2);
+    margin-top: 12px;
+}
+
+/* ====== Sidebar & Metrics ====== */
+section[data-testid="stSidebar"]>div:first-child {
+    background-color: var(--secondary-background-color);
+    border-right: 1px solid rgba(128, 128, 128, 0.2);
+}
+
+div[data-testid="stMetricValue"] {
+    font-size: 1.1rem !important;
+    font-weight: 500;
+}
+
+div[data-testid="stMetricLabel"] {
+    font-size: 0.85rem !important;
+    opacity: 0.8;
+}
+
+/* ====== Interactivity & Accessibility ====== */
+:focus-visible {
+    /* The focus outline now uses the theme's primary color */
+    outline: 2px solid var(--primary);
+    outline-offset: 2px;
+    border-radius: 8px;
+}
+
+</style>