Update LICENSE and add ML Pipeline Analysis Report

Refines the LICENSE file formatting and copyright notice to enhance clarity and compliance with the Apache License, Version 2.0.

Introduces a comprehensive ML Pipeline Analysis Report that evaluates the structure, data processing, and feature extraction of the polymer degradation classification pipeline using spectroscopy data, identifying key strengths and areas for improvement.

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PIPELINE_ANALYSIS_REPORT.md

@@ -0,0 +1,1016 @@
+# ML Pipeline Analysis Report
+
+## Executive Summary
+
+This report provides a comprehensive analysis of the machine learning pipeline for polymer degradation classification using Raman and FTIR spectroscopy data. The analysis focuses on codebase structure, data processing, feature extraction, model architecture, and specific UI bugs that impact functionality and user experience.
+
+---
+
+## Task 1: Codebase Structure Review
+
+### Overview
+
+Analyzing the organization, dependencies, and UI integration of the polymer aging ML platform to understand its architecture and identify structural issues.
+
+### Steps
+
+#### Step 1: Repository Structure Analysis
+
+**What**: Examined the overall codebase organization and file structure  
+**How**: Explored directory structure, key modules, and dependencies across the entire repository  
+**Why**: Understanding the architecture is essential for identifying bottlenecks and areas for improvement
+
+**Key Findings:**
+
+- **Modular Architecture**: Well-organized structure with separate modules for UI (`modules/`), models (`models/`), utilities (`utils/`), and preprocessing
+- **Streamlit-based UI**: Single-page application with tabbed interface (Standard Analysis, Model Comparison, Image Analysis, Performance Tracking)
+- **Model Registry System**: Centralized model management in `models/registry.py` with 6 available models
+- **Configuration Split**: Two configuration systems - `config.py` (legacy, 2 models) and `models/registry.py` (current, 6 models)
+
+#### Step 2: Dependency Analysis
+
+**What**: Reviewed imports, module relationships, and external dependencies  
+**How**: Analyzed import statements, requirements.txt, and cross-module dependencies  
+**Why**: Understanding dependencies helps identify potential conflicts and integration issues
+
+**Key Dependencies:**
+
+- **Core ML**: PyTorch, scikit-learn, NumPy, SciPy
+- **UI Framework**: Streamlit with custom styling
+- **Data Processing**: Pandas, matplotlib, seaborn for visualization
+- **Spectroscopy**: Custom preprocessing pipeline in `utils/preprocessing.py`
+
+#### Step 3: UI Integration Assessment
+
+**What**: Analyzed how UI components integrate with backend logic  
+**How**: Examined `modules/ui_components.py`, `app.py`, and state management  
+**Why**: UI-backend integration issues are the source of several reported bugs
+
+**Architecture Pattern:**
+
+- **Sidebar Controls**: Model selection, modality selection, input configuration
+- **Main Content**: Tabbed interface with distinct workflows
+- **State Management**: Streamlit session state with custom callback system
+- **Results Display**: Modular rendering with caching for performance
+
+### Task 1 Findings
+
+**Strengths:**
+
+- Clean modular architecture with separation of concerns
+- Comprehensive model registry supporting multiple architectures
+- Robust preprocessing pipeline with modality-specific parameters
+- Good error handling and caching mechanisms
+
+**Critical Issues Identified:**
+
+1. **Configuration Mismatch**: `config.py` defines only 2 models while `models/registry.py` has 6 models
+2. **UI-Backend Disconnect**: Sidebar uses `MODEL_CONFIG` (2 models) instead of registry (6 models)
+3. **Modality State Inconsistency**: Two separate modality selectors can have different values
+4. **Missing Model Weights**: Model loading expects weight files that may not exist
+
+### Task 1 Recommendations
+
+1. **Unify Model Configuration**: Replace `config.py` MODEL_CONFIG with registry-based model selection
+2. **Implement Consistent State Management**: Synchronize modality selection across UI components
+3. **Add Model Availability Checks**: Dynamically show only models with available weights
+4. **Improve Error Handling**: Better user feedback for missing dependencies or models
+
+### Task 1 Reflection
+
+The codebase shows good architectural principles but suffers from evolution-related inconsistencies. The split between legacy configuration and new registry system is the root cause of several UI bugs. The modular design makes fixes straightforward once issues are identified.
+
+### Transition to Next Task
+
+The structural analysis reveals that preprocessing is well-architected with modality-specific handling. Next, we'll examine the actual preprocessing implementation to assess effectiveness for Raman vs FTIR data.
+
+---
+
+## Task 2: Data Preprocessing Evaluation
+
+### Overview
+
+Evaluating the preprocessing pipeline for both Raman and FTIR spectroscopy data to identify modality-specific issues and optimization opportunities.
+
+### Steps
+
+#### Step 1: Preprocessing Pipeline Architecture Analysis
+
+**What**: Examined the preprocessing pipeline structure and modality handling  
+**How**: Analyzed `utils/preprocessing.py` and related test files  
+**Why**: Understanding the preprocessing flow is crucial for identifying performance bottlenecks and modality-specific issues
+
+**Pipeline Components:**
+
+1. **Input Validation**: File format, data points, wavenumber range validation
+2. **Resampling**: Linear interpolation to uniform 500-point grid
+3. **Baseline Correction**: Polynomial detrending (configurable degree)
+4. **Smoothing**: Savitzky-Golay filter for noise reduction
+5. **Normalization**: Min-max scaling with constant-signal protection
+6. **Modality-Specific Processing**: FTIR atmospheric and water vapor corrections
+
+#### Step 2: Modality-Specific Parameter Assessment
+
+**What**: Analyzed the different preprocessing parameters for Raman vs FTIR  
+**How**: Examined `MODALITY_PARAMS` and `MODALITY_RANGES` configurations  
+**Why**: Different spectroscopy techniques require different preprocessing approaches
+
+**Raman Parameters:**
+
+- Range: 200-4000 cm⁻¹ (typical Raman range)
+- Baseline degree: 2 (polynomial)
+- Smoothing window: 11 points
+- Cosmic ray removal: Disabled (potential issue)
+
+**FTIR Parameters:**
+
+- Range: 400-4000 cm⁻¹ (FTIR range)
+- Baseline degree: 2 (same as Raman)
+- Smoothing window: Different from Raman
+- Atmospheric correction: Available but optional
+- Water vapor correction: Available but optional
+
+#### Step 3: Validation and Quality Control Analysis
+
+**What**: Reviewed data quality assessment and validation mechanisms  
+**How**: Examined `modules/enhanced_data_pipeline.py` quality controller  
+**Why**: Data quality directly impacts model performance, especially for FTIR
+
+**Quality Metrics:**
+
+- Signal-to-noise ratio assessment
+- Baseline stability evaluation
+- Peak resolution analysis
+- Spectral range coverage validation
+- Instrumental artifact detection
+
+### Task 2 Findings
+
+**Raman Preprocessing Strengths:**
+
+- Appropriate wavenumber range for Raman spectroscopy
+- Standard polynomial baseline correction effective for most Raman data
+- Savitzky-Golay smoothing parameters well-tuned
+
+**Raman Preprocessing Issues:**
+
+- **Cosmic Ray Removal Disabled**: Major issue for Raman data quality
+- **Fixed Parameters**: No adaptive preprocessing based on signal quality
+- **Limited Noise Handling**: Could benefit from more sophisticated denoising
+
+**FTIR Preprocessing Strengths:**
+
+- Modality-specific wavenumber range (400-4000 cm⁻¹)
+- Atmospheric interference correction available
+- Water vapor band correction implemented
+
+**FTIR Preprocessing Critical Issues:**
+
+1. **Atmospheric Corrections Often Disabled**: Default configuration doesn't enable critical FTIR corrections
+2. **Insufficient Baseline Correction**: FTIR often requires more aggressive baseline handling
+3. **Limited CO₂/H₂O Handling**: Basic water vapor correction may be insufficient
+4. **No Beer-Lambert Law Considerations**: FTIR absorbance data needs different normalization
+
+### Task 2 Recommendations
+
+**For Raman Optimization:**
+
+1. **Enable Cosmic Ray Removal**: Implement and activate cosmic ray spike detection/removal
+2. **Adaptive Smoothing**: Dynamic smoothing parameters based on noise level
+3. **Advanced Denoising**: Consider wavelet denoising for weak signals
+
+**For FTIR Enhancement:**
+
+1. **Enable Atmospheric Corrections by Default**: Activate CO₂ and H₂O corrections
+2. **Improved Baseline Correction**: Implement rubber-band or airPLS baseline correction
+3. **Absorbance-Specific Normalization**: Use Beer-Lambert law appropriate scaling
+4. **Region-of-Interest Selection**: Focus on chemically relevant wavenumber regions
+
+### Task 2 Reflection
+
+The preprocessing pipeline is well-architected but conservative in its approach. Raman processing is adequate but misses cosmic ray removal - a critical step. FTIR processing has the right components but they're not properly enabled or optimized. The modular design makes improvements straightforward to implement.
+
+### Transition to Next Task
+
+With preprocessing issues identified, we now examine feature extraction methods to understand why FTIR performance is poor compared to Raman and identify optimization opportunities.
+
+---
+
+## Task 3: Feature Extraction Assessment
+
+### Overview
+
+Analyzing feature extraction methods for both modalities, focusing on why FTIR features are ineffective compared to Raman and identifying optimization strategies.
+
+### Steps
+
+#### Step 1: Current Feature Extraction Analysis
+
+**What**: Examined how spectral features are extracted and used by ML models  
+**How**: Analyzed model architectures, preprocessing outputs, and feature representation  
+**Why**: Feature quality directly impacts model performance and explains modality-specific effectiveness
+
+**Current Approach:**
+
+- **Raw Spectral Features**: Direct use of preprocessed intensity values
+- **Uniform Sampling**: All spectra resampled to 500 points regardless of modality
+- **No Domain-Specific Features**: Missing peak detection, band identification, or chemical markers
+- **Generic Architecture**: Same CNN architecture for both Raman and FTIR
+
+#### Step 2: Raman Feature Effectiveness Analysis
+
+**What**: Assessed why Raman features work reasonably well  
+**How**: Examined Raman spectroscopy characteristics and model performance  
+**Why**: Understanding Raman success can guide FTIR improvements
+
+**Raman Advantages:**
+
+- **Sharp Peaks**: Raman provides distinct, narrow peaks suitable for CNN pattern recognition
+- **Molecular Vibrations**: Direct correlation between polymer degradation and spectral changes
+- **Less Background**: Raman typically has cleaner backgrounds than FTIR
+- **Consistent Baseline**: Raman baselines are generally more stable
+
+#### Step 3: FTIR Feature Ineffectiveness Analysis
+
+**What**: Investigated specific reasons for poor FTIR performance  
+**How**: Analyzed FTIR characteristics, preprocessing limitations, and model architecture fit  
+**Why**: Identifying root causes enables targeted improvements
+
+**FTIR Challenges:**
+
+1. **Broad Absorption Bands**: FTIR features are broader and more overlapping than Raman peaks
+2. **Atmospheric Interference**: CO₂ and H₂O bands mask important polymer signals
+3. **Complex Baselines**: FTIR baselines drift more significantly than Raman
+4. **Beer-Lambert Effects**: Absorbance intensity relates logarithmically to concentration
+5. **Matrix Effects**: Sample preparation artifacts more pronounced in FTIR
+
+### Task 3 Findings
+
+**Why FTIR Features Are Ineffective:**
+
+1. **Inappropriate Preprocessing**:
+
+   - Min-max normalization ignores Beer-Lambert law principles
+   - Disabled atmospheric corrections leave interfering bands
+   - Insufficient baseline correction for FTIR drift characteristics
+
+2. **Suboptimal Feature Representation**:
+
+   - 500-point uniform sampling doesn't emphasize chemically relevant regions
+   - No derivative spectroscopy (essential for FTIR analysis)
+   - Missing peak integration or band ratio calculations
+
+3. **Architecture Mismatch**:
+
+   - CNN architectures optimized for sharp Raman peaks
+   - No attention mechanisms for broad FTIR absorption bands
+   - Insufficient receptive field for FTIR's broader spectral features
+
+4. **Missing Domain Knowledge**:
+   - No chemical group identification (C=O, C-H, O-H bands)
+   - Missing polymer-specific spectral markers
+   - No weathering-related spectral indicators
+
+**Why Raman Works Better:**
+
+- Sharp peaks match CNN's pattern recognition strengths
+- More stable baselines require less aggressive preprocessing
+- Direct molecular vibration information
+- Less atmospheric interference
+
+### Task 3 Recommendations
+
+**Immediate FTIR Improvements:**
+
+1. **Enable FTIR-Specific Preprocessing**: Activate atmospheric corrections, improve baseline handling
+2. **Implement Derivative Spectroscopy**: Add first/second derivatives to enhance peak resolution
+3. **Region-of-Interest Focus**: Weight chemically relevant wavenumber regions more heavily
+4. **Absorbance-Appropriate Normalization**: Use log-scale normalization respecting Beer-Lambert law
+
+**Advanced Feature Engineering:**
+
+1. **Peak Detection and Integration**: Extract meaningful chemical band areas
+2. **Band Ratio Calculations**: Calculate ratios indicative of polymer degradation
+3. **Spectral Deconvolution**: Separate overlapping absorption bands
+4. **Chemical Group Identification**: Automated detection of polymer functional groups
+
+**Architecture Modifications:**
+
+1. **Multi-Scale CNNs**: Different receptive fields for broad vs narrow features
+2. **Attention Mechanisms**: Focus on chemically relevant spectral regions
+3. **Hybrid Models**: Combine CNN backbone with spectroscopy-specific layers
+4. **Ensemble Approaches**: Separate models for different FTIR regions
+
+### Task 3 Reflection
+
+The analysis reveals that FTIR's poor performance stems from treating it identically to Raman despite fundamental differences in spectroscopic principles. FTIR requires domain-specific preprocessing, feature extraction, and potentially different architectures. The current generic approach works for Raman's sharp peaks but fails for FTIR's broad bands.
+
+### Transition to Next Task
+
+With feature extraction issues identified, we now analyze the ML models and training processes, particularly focusing on how the AI Model Selection UI integrates with the various architectures.
+
+---
+
+## Task 4: ML Models and Training Analysis
+
+### Overview
+
+Evaluating the machine learning models, their architectures, training/validation processes, and integration with the AI Model Selection UI to identify performance and usability issues.
+
+### Steps
+
+#### Step 1: Model Architecture Analysis
+
+**What**: Examined the available model architectures and their suitability for spectroscopy data  
+**How**: Analyzed model classes in `models/` directory and registry specifications  
+**Why**: Understanding model capabilities helps identify performance limitations and UI integration issues
+
+**Available Models in Registry (6 total):**
+
+1. **figure2**: Baseline CNN (500K params, 94.8% accuracy)
+2. **resnet**: ResNet1D with skip connections (100K params, 96.2% accuracy)
+3. **resnet18vision**: Adapted ResNet18 (11M params, 94.5% accuracy)
+4. **enhanced_cnn**: CNN with attention mechanisms (800K params, 97.5% accuracy)
+5. **efficient_cnn**: Lightweight CNN (200K params, 95.5% accuracy)
+6. **hybrid_net**: CNN-Transformer hybrid (1.2M params, 96.8% accuracy)
+
+**Models in UI Config (2 total):**
+
+- Only "Figure2CNN (Baseline)" and "ResNet1D (Advanced)" appear in sidebar
+
+#### Step 2: Training and Validation Process Assessment
+
+**What**: Analyzed model training methodology and validation approaches  
+**How**: Examined training scripts, performance metrics, and validation procedures  
+**Why**: Training quality affects model reliability and explains performance differences
+
+**Training Observations:**
+
+- **Ground Truth Validation**: Filename-based labeling system (sta* = stable, wea* = weathered)
+- **Performance Tracking**: Comprehensive metrics tracking in `utils/performance_tracker.py`
+- **Cross-Validation**: Framework present but validation rigor unclear
+- **Hyperparameter Tuning**: Model-specific parameters but limited systematic optimization
+
+#### Step 3: AI Model Selection UI Integration Analysis
+
+**What**: Investigated how the UI integrates with the model registry and handles model selection  
+**How**: Traced code flow from UI components through model loading to inference  
+**Why**: UI-backend disconnection is causing major usability issues (Bug A)
+
+**Integration Flow:**
+
+1. **Sidebar Selection**: Uses `MODEL_CONFIG` from `config.py` (2 models only)
+2. **Model Loading**: `core_logic.py` expects specific weight file paths
+3. **Registry System**: `models/registry.py` has 6 models but isn't used by UI
+4. **Comparison Tab**: Uses registry correctly, causing inconsistency
+
+### Task 4 Findings
+
+**Model Architecture Strengths:**
+
+- **Diverse Options**: Good variety from lightweight to transformer-based models
+- **Performance Range**: Models span efficiency vs accuracy trade-offs
+- **Modality Support**: All models claim Raman/FTIR compatibility
+- **Modern Architectures**: Includes attention mechanisms and hybrid approaches
+
+**Critical Integration Issues:**
+
+1. **Bug A Root Cause - Configuration Split**:
+
+   - Sidebar uses legacy `config.py` with only 2 models
+   - Registry has 6 models but isn't connected to main UI
+   - Model weights expected in specific paths that may not exist
+
+2. **Model Loading Problems**:
+
+   - Weight files may be missing (`model_weights/` or `outputs/` directory)
+   - Error handling shows warnings but continues with random weights
+   - No dynamic availability checking
+
+3. **Inconsistent Performance Claims**:
+   - Registry shows 97.5% accuracy for enhanced_cnn
+   - Unclear if these are validated metrics or theoretical
+   - No real-time performance validation
+
+**Training and Validation Issues:**
+
+1. **Limited Validation Rigor**: Simple filename-based ground truth may be insufficient
+2. **No Cross-Modal Validation**: Models trained/tested on same modality data
+3. **Missing Baseline Comparisons**: No systematic comparison with traditional methods
+4. **Insufficient Hyperparameter Search**: Limited evidence of systematic optimization
+
+### Task 4 Recommendations
+
+**Immediate UI Integration Fixes:**
+
+1. **Connect Registry to Sidebar**: Replace `MODEL_CONFIG` with registry-based selection
+2. **Dynamic Model Availability**: Show only models with available weights
+3. **Unified Model Interface**: Consistent model loading across all UI components
+4. **Better Error Handling**: Clear feedback when models unavailable
+
+**Model Architecture Improvements:**
+
+1. **Modality-Specific Models**: Separate architectures optimized for Raman vs FTIR
+2. **Transfer Learning**: Pre-train on one modality, fine-tune on another
+3. **Multi-Modal Models**: Architectures that can handle both modalities simultaneously
+4. **Uncertainty Quantification**: Add confidence estimates to model outputs
+
+**Training and Validation Enhancements:**
+
+1. **Rigorous Cross-Validation**: Implement proper k-fold validation
+2. **External Validation**: Test on independent datasets
+3. **Hyperparameter Optimization**: Systematic search for optimal parameters
+4. **Baseline Comparisons**: Compare against traditional chemometric methods
+
+### Task 4 Reflection
+
+The model architecture diversity is impressive, but the UI integration is fundamentally broken due to configuration system evolution. The disconnect between registry (6 models) and UI (2 models) creates a poor user experience. Training validation appears adequate but could be more rigorous for scientific applications.
+
+### Transition to Next Task
+
+With model integration issues identified, we now investigate the specific UI bugs that impact user experience and functionality, providing detailed analysis of each reported issue.
+
+---
+
+## Task 5: UI Bug Investigation
+
+### Overview
+
+Detailed investigation of the four specific UI bugs reported: AI Model Selection limitations, modality validation issues, Model Comparison tab errors, and conflicting modality selectors.
+
+### Steps
+
+#### Step 1: Bug A Analysis - AI Model Selection Limitation
+
+**What**: Investigated why "Choose AI Model" selectbox shows only 2 models instead of 6  
+**How**: Traced code flow from UI rendering to model configuration  
+**Why**: This bug prevents users from accessing 4 out of 6 available models
+
+**Root Cause Analysis:**
+
+```python
+# In modules/ui_components.py line 197-199
+model_labels = [
+    f"{MODEL_CONFIG[name]['emoji']} {name}" for name in MODEL_CONFIG.keys()
+]
+```
+
+**Problem**: UI uses `MODEL_CONFIG` from `config.py` which only defines 2 models:
+
+- "Figure2CNN (Baseline)"
+- "ResNet1D (Advanced)"
+
+**Missing Models**: 4 models from registry not accessible:
+
+- enhanced_cnn (97.5% accuracy)
+- efficient_cnn (95.5% accuracy)
+- hybrid_net (96.8% accuracy)
+- resnet18vision (94.5% accuracy)
+
+#### Step 2: Bug B Analysis - Modality Validation Issues
+
+**What**: Analyzed why modality selector allows incorrect data processing  
+**How**: Examined data validation and routing logic between modality selection and preprocessing  
+**Why**: This causes incorrect spectroscopy analysis and invalid results
+
+**Issue Identification:**
+
+- **Modality Selection**: Sidebar allows user to choose Raman or FTIR
+- **Data Upload**: User uploads spectrum file (no automatic modality detection)
+- **Processing Gap**: No validation that uploaded data matches selected modality
+- **Result**: FTIR data processed with Raman parameters or vice versa
+
+**Validation Missing:**
+
+- No automatic spectroscopy type detection from data characteristics
+- No wavenumber range validation against modality expectations
+- No warning when data doesn't match selected modality
+
+#### Step 3: Bug C Analysis - Model Comparison Tab Errors
+
+**What**: Investigated specific errors in Model Comparison tab functionality  
+**How**: Analyzed error messages and async processing logic  
+**Why**: These errors prevent multi-model comparison functionality
+
+**Error Analysis:**
+
+1. **"Error loading model figure2: 'figure2'"**:
+
+   - Registry uses key "figure2" but UI expects "Figure2CNN (Baseline)"
+   - Model loading function expects config.py format, not registry format
+
+2. **"Error loading model resnet: 'resnet'"**:
+
+   - Same issue - key mismatch between registry and loading function
+
+3. **"Error during comparison: min() arg is an empty sequence"**:
+   - Occurs when no valid model results are available
+   - Async processing fails and leaves empty results list
+   - min() function called on empty list causes crash
+
+**Async Processing Issues:**
+
+- Models fail to load due to key mismatch
+- Error handling doesn't prevent downstream crashes
+- UI doesn't gracefully handle all-model-failure scenarios
+
+#### Step 4: Bug D Analysis - Conflicting Modality Selectors
+
+**What**: Identified UX issue with two modality selectors having different values  
+**How**: Examined state management between sidebar and main content areas  
+**Why**: This creates user confusion and inconsistent application behavior
+
+**Selector Locations:**
+
+1. **Sidebar**: `st.selectbox("Choose Modality", key="modality_select")`
+2. **Comparison Tab**: `st.selectbox("Select Modality", key="comparison_modality")`
+
+**State Management Issue:**
+
+```python
+# In comparison tab - line 1001
+st.session_state["modality_select"] = modality
+```
+
+- Comparison tab overwrites sidebar state
+- No synchronization mechanism
+- Users can have contradictory settings visible simultaneously
+
+### Task 5 Findings
+
+**Bug A - Model Selection (Critical):**
+
+- **Impact**: 66% of models inaccessible to users
+- **Cause**: Legacy configuration system override
+- **Severity**: High - Major functionality loss
+
+**Bug B - Modality Validation (High):**
+
+- **Impact**: Incorrect analysis results, misleading outputs
+- **Cause**: Missing data validation layer
+- **Severity**: High - Scientific accuracy compromised
+
+**Bug C - Comparison Errors (High):**
+
+- **Impact**: Multi-model comparison completely broken
+- **Cause**: Key mismatch between registry and loading systems
+- **Severity**: High - Core feature non-functional
+
+**Bug D - UI Inconsistency (Medium):**
+
+- **Impact**: User confusion, inconsistent behavior
+- **Cause**: Poor state management across components
+- **Severity**: Medium - UX degradation
+
+### Task 5 Recommendations
+
+**Bug A - Immediate Fix:**
+
+```python
+# Replace MODEL_CONFIG usage with registry
+from models.registry import choices, get_model_info
+
+# In render_sidebar():
+available_models = choices()
+model_labels = [f"{get_model_info(name).get('emoji', '')} {name}"
+                for name in available_models]
+```
+
+**Bug B - Data Validation:**
+
+```python
+def validate_modality_match(x_data, y_data, selected_modality):
+    """Validate that data characteristics match selected modality"""
+    wavenumber_range = max(x_data) - min(x_data)
+
+    if selected_modality == "raman" and not (200 <= min(x_data) <= 4000):
+        return False, "Data appears to be FTIR, not Raman"
+    elif selected_modality == "ftir" and not (400 <= min(x_data) <= 4000):
+        return False, "Data appears to be Raman, not FTIR"
+
+    return True, "Modality validated"
+```
+
+**Bug C - Model Loading Fix:**
+
+```python
+# Unify model loading to use registry keys consistently
+def load_model_from_registry(model_key):
+    """Load model using registry system"""
+    from models.registry import build, spec
+    model = build(model_key, 500)
+    return model
+```
+
+**Bug D - State Synchronization:**
+
+```python
+# Implement centralized modality state
+def sync_modality_state():
+    """Ensure all modality selectors show same value"""
+    if "comparison_modality" in st.session_state:
+        st.session_state["modality_select"] = st.session_state["comparison_modality"]
+```
+
+### Task 5 Reflection
+
+All four bugs stem from the evolution of the codebase where new systems (registry) were added without updating dependent components. The fixes are straightforward but require systematic updates across multiple files. The bugs range from critical functionality loss to user experience degradation.
+
+### Transition to Next Task
+
+With all bugs identified and root causes understood, we can now propose comprehensive improvements that address not only the immediate issues but also enhance the overall pipeline performance and usability.
+
+---
+
+## Task 6: Improvement Proposals
+
+### Overview
+
+Proposing comprehensive improvements for identified issues, prioritizing FTIR feature enhancements, Raman optimization, and UI bug fixes based on the analysis from Tasks 1-5.
+
+### Steps
+
+#### Step 1: Immediate Critical Fixes (High Priority)
+
+**What**: Address bugs that prevent core functionality  
+**How**: Systematic fixes for model selection, modality validation, and UI consistency  
+**Why**: These issues block users from accessing key features and compromise result accuracy
+
+**Priority 1: Model Selection Fix (Bug A)**
+
+```python
+# File: modules/ui_components.py
+# Replace lines 197-199 with:
+from models.registry import choices, get_model_info
+
+def render_sidebar():
+    # ... existing code ...
+
+    # Model selection using registry
+    st.markdown("##### AI Model Selection")
+    available_models = choices()
+
+    # Check model availability dynamically
+    available_with_weights = []
+    for model_key in available_models:
+        # Check if weights exist
+        model_info = get_model_info(model_key)
+        # Add availability check here
+        available_with_weights.append(model_key)
+
+    model_options = {name: get_model_info(name) for name in available_with_weights}
+    selected_model = st.selectbox(
+        "Choose AI Model",
+        list(model_options.keys()),
+        key="model_select",
+        format_func=lambda x: f"{model_options[x].get('description', x)}",
+        on_change=on_model_change,
+    )
+```
+
+**Priority 2: Modality Validation (Bug B)**
+
+```python
+# File: utils/preprocessing.py
+# Add validation function
+def validate_spectrum_modality(x_data, y_data, selected_modality):
+    """Validate spectrum characteristics match selected modality"""
+    x_min, x_max = min(x_data), max(x_data)
+
+    validation_rules = {
+        'raman': {
+            'min_wavenumber': 200,
+            'max_wavenumber': 4000,
+            'typical_peaks': 'sharp',
+            'baseline': 'stable'
+        },
+        'ftir': {
+            'min_wavenumber': 400,
+            'max_wavenumber': 4000,
+            'typical_peaks': 'broad',
+            'baseline': 'variable'
+        }
+    }
+
+    rules = validation_rules[selected_modality]
+    issues = []
+
+    if x_min < rules['min_wavenumber'] or x_max > rules['max_wavenumber']:
+        issues.append(f"Wavenumber range {x_min:.0f}-{x_max:.0f} cm⁻¹ unusual for {selected_modality.upper()}")
+
+    return len(issues) == 0, issues
+```
+
+#### Step 2: FTIR Performance Enhancement (High Priority)
+
+**What**: Implement FTIR-specific preprocessing and feature extraction improvements  
+**How**: Enable atmospheric corrections, add derivative spectroscopy, improve normalization  
+**Why**: FTIR currently underperforms due to inappropriate processing for its spectroscopic characteristics
+
+**Enhanced FTIR Preprocessing:**
+
+```python
+# File: utils/preprocessing.py
+# Modify MODALITY_PARAMS for FTIR
+MODALITY_PARAMS = {
+    "ftir": {
+        "baseline_degree": 3,  # More aggressive baseline correction
+        "smooth_window": 15,   # Wider smoothing for broad bands
+        "smooth_polyorder": 3,
+        "atmospheric_correction": True,  # Enable by default
+        "water_correction": True,       # Enable by default
+        "derivative_order": 1,          # Add first derivative
+        "normalize_method": "vector",   # L2 normalization better for FTIR
+        "region_weighting": True,       # Weight important chemical regions
+    }
+}
+
+def apply_ftir_enhancements(x, y):
+    """Enhanced FTIR preprocessing pipeline"""
+    # 1. Remove atmospheric interference
+    y_clean = remove_atmospheric_interference(y)
+
+    # 2. Advanced baseline correction (airPLS or rubber band)
+    y_baseline = advanced_baseline_correction(y_clean, method='airPLS')
+
+    # 3. First derivative for peak enhancement
+    y_deriv = np.gradient(y_baseline)
+
+    # 4. Region-of-interest weighting
+    y_weighted = apply_chemical_region_weighting(x, y_deriv)
+
+    # 5. Vector normalization
+    y_normalized = y_weighted / np.linalg.norm(y_weighted)
+
+    return y_normalized
+```
+
+**FTIR-Specific Model Architecture:**
+
+```python
+# File: models/ftir_cnn.py
+class FTIRSpecificCNN(nn.Module):
+    """CNN architecture optimized for FTIR characteristics"""
+
+    def __init__(self, input_length=500):
+        super().__init__()
+
+        # Multi-scale convolutions for broad absorption bands
+        self.multi_scale_conv = nn.ModuleList([
+            nn.Conv1d(1, 32, kernel_size=3, padding=1),   # Fine features
+            nn.Conv1d(1, 32, kernel_size=7, padding=3),   # Medium features
+            nn.Conv1d(1, 32, kernel_size=15, padding=7),  # Broad features
+        ])
+
+        # Attention mechanism for chemical region focus
+        self.attention = nn.MultiheadAttention(96, 8)
+
+        # Chemical group detection layers
+        self.chemical_layers = nn.Sequential(
+            nn.Conv1d(96, 64, kernel_size=5, padding=2),
+            nn.BatchNorm1d(64),
+            nn.ReLU(),
+            nn.Dropout(0.3)
+        )
+
+        # Classification head
+        self.classifier = nn.Sequential(
+            nn.AdaptiveAvgPool1d(1),
+            nn.Flatten(),
+            nn.Linear(64, 32),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+            nn.Linear(32, 2)
+        )
+
+    def forward(self, x):
+        # Multi-scale feature extraction
+        scale_features = []
+        for conv in self.multi_scale_conv:
+            scale_features.append(conv(x))
+
+        # Concatenate multi-scale features
+        features = torch.cat(scale_features, dim=1)
+
+        # Apply attention
+        features = features.permute(2, 0, 1)  # seq_len, batch, features
+        attended, _ = self.attention(features, features, features)
+        attended = attended.permute(1, 2, 0)  # batch, features, seq_len
+
+        # Chemical group detection
+        chemical_features = self.chemical_layers(attended)
+
+        # Classification
+        output = self.classifier(chemical_features)
+        return output
+```
+
+#### Step 3: Raman Optimization (Medium Priority)
+
+**What**: Enhance Raman preprocessing and add advanced denoising capabilities  
+**How**: Enable cosmic ray removal, adaptive smoothing, and weak signal enhancement  
+**Why**: Raman works adequately but has room for optimization, especially for weak signals
+
+**Raman Enhancements:**
+
+```python
+# File: utils/raman_enhancement.py
+def enhanced_raman_preprocessing(x, y):
+    """Enhanced Raman preprocessing with cosmic ray removal and adaptive denoising"""
+
+    # 1. Cosmic ray removal
+    y_clean = remove_cosmic_rays(y, threshold=3.0)
+
+    # 2. Adaptive smoothing based on signal-to-noise ratio
+    snr = calculate_snr(y_clean)
+    if snr < 10:
+        # Strong smoothing for noisy data
+        y_smooth = savgol_filter(y_clean, window_length=15, polyorder=2)
+    else:
+        # Light smoothing for clean data
+        y_smooth = savgol_filter(y_clean, window_length=7, polyorder=2)
+
+    # 3. Baseline correction optimized for Raman
+    y_baseline = polynomial_baseline_correction(y_smooth, degree=2)
+
+    # 4. Peak enhancement for weak signals
+    if snr < 5:
+        y_enhanced = enhance_weak_peaks(y_baseline)
+    else:
+        y_enhanced = y_baseline
+
+    return y_enhanced
+
+def remove_cosmic_rays(spectrum, threshold=3.0):
+    """Remove cosmic ray spikes from Raman spectrum"""
+    # Implementation of cosmic ray detection and removal
+    # Using derivative-based spike detection
+    pass
+```
+
+#### Step 4: UI/UX Improvements (Medium Priority)
+
+**What**: Fix remaining UI bugs and enhance user experience  
+**How**: Implement state synchronization, better error handling, and improved feedback  
+**Why**: Good UX is essential for user adoption and prevents analysis errors
+
+**State Synchronization Fix:**
+
+```python
+# File: modules/ui_components.py
+def synchronize_modality_state():
+    """Ensure consistent modality selection across all UI components"""
+    # Check if any modality selector changed
+    sidebar_modality = st.session_state.get("modality_select", "raman")
+    comparison_modality = st.session_state.get("comparison_modality", "raman")
+
+    # Sync states
+    if sidebar_modality != comparison_modality:
+        # Use most recent change
+        if "comparison_modality" in st.session_state:
+            st.session_state["modality_select"] = comparison_modality
+        else:
+            st.session_state["comparison_modality"] = sidebar_modality
+
+# Call this function at the start of each page render
+```
+
+**Enhanced Error Handling:**
+
+```python
+# File: core_logic.py
+def load_model_with_validation(model_name):
+    """Load model with comprehensive validation and user feedback"""
+    try:
+        from models.registry import build, spec, get_model_info
+
+        # Check if model exists in registry
+        if model_name not in choices():
+            st.error(f"❌ Model '{model_name}' not found in registry")
+            return None, False
+
+        # Get model info
+        model_info = get_model_info(model_name)
+
+        # Build model
+        model = build(model_name, 500)
+
+        # Check for weights
+        weight_path = f"model_weights/{model_name}_model.pth"
+        if os.path.exists(weight_path):
+            state_dict = torch.load(weight_path, map_location="cpu")
+            model.load_state_dict(state_dict)
+            st.success(f"✅ Model '{model_name}' loaded successfully")
+            return model, True
+        else:
+            st.warning(f"⚠️ Weights not found for '{model_name}'. Using random initialization.")
+            return model, False
+
+    except Exception as e:
+        st.error(f"❌ Error loading model '{model_name}': {str(e)}")
+        return None, False
+```
+
+#### Step 5: Advanced Improvements (Lower Priority)
+
+**What**: Implement advanced features for enhanced analysis capabilities  
+**How**: Add ensemble methods, uncertainty quantification, and automated quality assessment  
+**Why**: These improvements enhance the scientific rigor and usability of the platform
+
+**Ensemble Modeling:**
+
+```python
+# File: models/ensemble.py
+class SpectroscopyEnsemble:
+    """Ensemble of models for robust predictions"""
+
+    def __init__(self, model_names, modality):
+        self.models = {}
+        self.modality = modality
+
+        for name in model_names:
+            if is_model_compatible(name, modality):
+                self.models[name] = build(name, 500)
+
+    def predict_with_uncertainty(self, x):
+        """Predict with uncertainty quantification"""
+        predictions = []
+        confidences = []
+
+        for name, model in self.models.items():
+            pred, conf = model.predict_with_confidence(x)
+            predictions.append(pred)
+            confidences.append(conf)
+
+        # Ensemble prediction
+        ensemble_pred = np.mean(predictions, axis=0)
+        ensemble_std = np.std(predictions, axis=0)
+
+        return ensemble_pred, ensemble_std
+```
+
+### Task 6 Recommendations Summary
+
+**Immediate Actions (Week 1):**
+
+1. Fix model selection bug by connecting UI to registry
+2. Implement modality validation for uploaded data
+3. Resolve model comparison tab errors
+4. Synchronize modality selectors across UI
+
+**FTIR Enhancement (Week 2-3):**
+
+1. Enable atmospheric and water corrections by default
+2. Implement FTIR-specific preprocessing pipeline
+3. Add derivative spectroscopy capabilities
+4. Create FTIR-optimized model architecture
+
+**Raman Optimization (Week 3-4):**
+
+1. Implement cosmic ray removal
+2. Add adaptive preprocessing based on signal quality
+3. Enhance weak signal detection capabilities
+4. Optimize baseline correction parameters
+
+**Advanced Features (Month 2):**
+
+1. Implement ensemble modeling with uncertainty quantification
+2. Add automated data quality assessment
+3. Create modality-specific model architectures
+4. Develop comprehensive validation framework
+
+### Task 6 Reflection
+
+The proposed improvements address immediate functionality issues while building toward a more robust, scientifically rigorous platform. The modular architecture makes these improvements feasible to implement incrementally. Priority is given to fixes that restore core functionality, followed by scientific accuracy improvements, and finally advanced features for enhanced usability.
+
+### Final Recommendations
+
+The ML pipeline shows strong architectural foundations but suffers from evolution-related inconsistencies and inadequate domain-specific optimization. The proposed improvements will restore full functionality, significantly enhance FTIR performance, optimize Raman processing, and improve user experience. Implementation should proceed in priority order to quickly restore core functionality while building toward advanced capabilities.
+
+---
+
+## Overall Conclusions
+
+### Critical Issues Summary
+
+1. **UI-Backend Disconnect**: Model registry not connected to UI (Bug A)
+2. **FTIR Processing Inadequacy**: Generic preprocessing fails for FTIR characteristics
+3. **Missing Data Validation**: No modality-data matching verification (Bug B)
+4. **Inconsistent State Management**: Multiple modality selectors conflict (Bug D)
+5. **Broken Comparison Feature**: Model loading failures prevent comparisons (Bug C)
+
+### Success Factors
+
+1. **Strong Architecture**: Modular design supports improvements
+2. **Comprehensive Model Registry**: Good variety of architectures available
+3. **Solid Preprocessing Foundation**: Framework exists, needs optimization
+4. **Quality Tracking**: Performance monitoring infrastructure in place
+
+### Implementation Priority
+
+1. **Immediate**: Fix UI bugs to restore functionality
+2. **High**: Enhance FTIR processing for scientific accuracy
+3. **Medium**: Optimize Raman processing and improve UX
+4. **Future**: Add advanced features and ensemble methods
+
+The analysis reveals a platform with excellent potential held back by integration issues and inadequate domain-specific optimization. The proposed improvements will transform it into a robust, scientifically rigorous tool for polymer degradation analysis.