update pipeline.

README.md

@@ -1,217 +1,225 @@
 ---
-title: TimeFlow Pro
-emoji: 📊
-colorFrom: blue
-colorTo: indigo
+title: ML Pipeline PRO Suite
+emoji: 🤖
+colorFrom: purple
+colorTo: blue
 sdk: docker
 pinned: true
 app_file: app.py
 sdk_version: 1.52.2
 ---
 
-# 📊 TimeFlow Pro
+# 🤖 ML Pipeline PRO Suite
 
 <div align="center">
 
-**Intelligent Time Series Data Analysis and Preprocessing Platform**
+**End-to-End Machine Learning Platform with TimeFlow Pro & ModelForge Pro**
 
-*Advanced pipeline for data preparation and feature engineering*
+*Complete ML workflow from data preprocessing to trained models*
 
-[![Hugging Face](https://img.shields.io/badge/🤗-Hugging%20Face%20Space-blue)](https://huggingface.co/spaces/your-username/timeflow-pro)
+[![Hugging Face](https://img.shields.io/badge/🤗-Live%20Demo-blue)](https://huggingface.co/spaces/arabovs-ai-lab/ml-pipeline-pro-dashboard)
 [![Streamlit](https://img.shields.io/badge/Interface-Streamlit-FF4B4B)](https://streamlit.io)
-[![Python](https://img.shields.io/badge/Python-3.9+-blue)](https://python.org)
+[![Python](https://img.shields.io/badge/Python-3.10+-blue)](https://python.org)
+[![ML Algorithms](https://img.shields.io/badge/ML%20Algorithms-30+-orange)](https://github.com/arabovs-ai-lab/ml-pipeline-pro-suite)
 
 </div>
 
 ## 🌟 Overview
 
-TimeFlow Pro is a comprehensive platform for time series data analysis, preprocessing, and feature engineering. Designed for data scientists and analysts, it provides an intuitive interface for transforming raw time series data into ML-ready datasets with advanced preprocessing capabilities.
+**ML Pipeline PRO Suite** is a complete machine learning platform that combines two powerful modules:
+- **TimeFlow Pro** - advanced time series preprocessing and feature engineering
+- **ModelForge Pro** - comprehensive ML training with 30+ algorithms
+
+Designed for data scientists, researchers, and ML engineers, it provides an end-to-end workflow from raw data to trained models in a single interface.
 
 ## 🚀 Key Features
 
-### 📈 **Data Analysis & Visualization**
+### 📊 **TimeFlow Pro - Data Processing Module**
 - **Interactive Data Exploration**: Real-time preview and statistics
 - **Missing Value Analysis**: Smart detection and handling strategies
 - **Outlier Detection**: Multiple methods including IQR, Z-Score, Isolation Forest
-- **Temporal Analysis**: Seasonality detection, trend analysis, decomposition
-
-### ⚙️ **Advanced Preprocessing Pipeline**
 - **Feature Engineering**: Automatic lag features, rolling statistics, seasonal components
 - **Stationarity Checking**: ADF tests and transformation suggestions
 - **Data Scaling**: Robust, Standard, MinMax, and custom scaling methods
 - **Feature Selection**: Correlation, variance, mutual information, RF importance
 
-### 🏗️ **ML-Ready Outputs**
-- **Train/Validation/Test Splits**: Time-based or random splitting
-- **Multiple Export Formats**: CSV, Parquet, Excel, JSON
-- **Model Integration**: Ready-to-use datasets for scikit-learn, XGBoost, LightGBM
-- **Visual Reports**: Comprehensive pipeline execution reports
-
-## 🎮 Quick Start
-
-### 1. **Upload Your Data**
-- Support for CSV, Excel, Parquet formats
-- Automatic date parsing and validation
-- Smart column type detection
-
-### 2. **Configure Pipeline**
-```python
-# Example configuration
-config = {
-    'target_column': 'sales',
-    'test_size': 0.2,
-    'max_lags': 5,
-    'seasonal_period': 365,
-    'scaling_method': 'robust'
-}
-```
+### 🤖 **ModelForge Pro - Machine Learning Module**
+- **30+ ML Algorithms**: Linear models (13), tree ensembles (7), gradient boosting (3), and advanced methods
+- **Hyperparameter Tuning**: Grid Search and Random Search optimization
+- **Model Ensembling**: Averaging, Voting, Weighted, and Stacking techniques
+- **Performance Analysis**: Metrics comparison, feature importance analysis
+- **Prediction Generation**: Make predictions on new data with ensemble options
+- **Model Export**: Serialize models in Pickle, Joblib, and Dill formats
 
-### 3. **Run Pipeline & Export**
-- Execute full preprocessing pipeline
-- Download processed data
-- Get feature importance reports
-- Export modeling datasets
+### 🎨 **Unified Interface & Visualization**
+- **Streamlit Web Interface**: Intuitive browser-based workflow
+- **Real-time Monitoring**: Track training progress and compare models
+- **Interactive Charts**: Visualize metrics, residuals, and predictions
+- **Automated Reports**: Generate comprehensive analysis reports
 
-## 📊 Technical Architecture
+## 🔧 **Complete Pipeline Architecture**
 
-### 🔧 **Pipeline Components**
 ```
-Data Loading → Validation → Missing Handling → Outlier Treatment
+Data Loading → Validation → Missing Handling → Outlier Treatment → Feature Engineering
+     ↓
+Stationarity Check → Correlation Analysis → Feature Selection → Data Splitting → Scaling
      ↓
-Feature Engineering → Stationarity Check → Correlation Analysis
+Algorithm Selection → Model Training → Hyperparameter Tuning → Ensemble Building
      ↓
-Data Splitting → Scaling → Feature Selection → Final Validation
+Performance Evaluation → Model Comparison → Prediction → Export
 ```
 
-### 🏆 **Core Features**
-- **Multi-stage Validation**: Raw, processed, and final data validation
-- **Memory Optimization**: Efficient handling of large datasets
-- **Error Recovery**: Graceful handling of pipeline failures
-- **Reproducible Results**: Configuration saving and logging
-
 ## 📚 Use Cases
 
-### 🏢 **Business Analytics**
-- Sales forecasting and trend analysis
-- Inventory optimization
-- Customer behavior prediction
-- Financial time series analysis
+### 🔬 **Scientific Research & Education**
+- Time series analysis in hydrometeorology and climate studies
+- Reproducible ML experiments and comparative algorithm studies
+- Educational tool for data science and machine learning courses
+- Feature engineering research and methodology development
+
+### 🏢 **Business & Industry Applications**
+- Sales forecasting and demand prediction in retail
+- Predictive maintenance for industrial equipment
+- Financial market analysis and risk assessment
+- Energy consumption forecasting and optimization
+- IoT sensor data analysis and anomaly detection
 
-### 🏭 **Industrial Applications**
-- Sensor data preprocessing
-- Predictive maintenance
-- Quality control monitoring
-- Energy consumption forecasting
+### 💼 **ML Development & Prototyping**
+- Rapid prototyping of ML solutions
+- Automated model selection and optimization
+- Production-ready pipeline development
+- Team collaboration on ML projects
 
-### 🎓 **Academic Research**
-- Time series modeling experiments
-- Feature engineering research
-- Algorithm comparison studies
-- Educational tool for data science
+## 🛠️ Installation & Quick Start
 
-## 🛠️ Installation
+### **1. Download the Project**
+```bash
+# Clone via Git
+git clone https://huggingface.co/spaces/arabovs-ai-lab/ml-pipeline-pro-dashboard
+cd ml-pipeline-pro-dashboard
+```
 
-### Local Development
+### **2. Python Installation**
 ```bash
-# Clone repository
-git clone https://huggingface.co/spaces/your-username/timeflow-pro
-cd timeflow-pro
+# Create virtual environment
+python -m venv .venv
+
+# Activate (Windows)
+.venv\Scripts\activate
+# Or (Linux/Mac)
+# source .venv/bin/activate
 
 # Install dependencies
 pip install -r requirements.txt
 
-# Run application
+# Launch the platform
 streamlit run app.py
 ```
 
-### Docker Deployment
+### **3. Run Individual Modules**
 ```bash
-# Build Docker image
-docker build -t timeflow-pro .
+# Launch only TimeFlow Pro (data preprocessing)
+streamlit run streamlit\preprocessor_app.py
 
-# Run container
-docker run -p 8501:8501 timeflow-pro
+# Launch only ModelForge Pro (machine learning)
+streamlit run streamlit\ml_app.py
 ```
 
-## 🌐 API Usage Example
-
-```python
-from timeflow_pro import TimeFlowPipeline
-import pandas as pd
-
-# Load your data
-data = pd.read_csv('your_data.csv')
-
-# Configure pipeline
-config = {
-    'target_column': 'target',
-    'test_size': 0.2,
-    'max_lags': 7,
-    'seasonal_period': 30
-}
-
-# Create and run pipeline
-pipeline = TimeFlowPipeline(config)
-processed_data = pipeline.run(data)
+### **4. Docker Deployment**
+```bash
+# Build Docker image
+docker build -t ml-pipeline-pro-suite .
 
-# Get modeling data
-modeling_data = pipeline.get_modeling_data()
-X_train, y_train = modeling_data['X_train'], modeling_data['y_train']
+# Run container
+docker run -p 7860:7860 ml-pipeline-pro-suite
 ```
 
-## 📈 Performance Benchmarks
+**After launching**, open your browser at:
+- http://localhost:8501 (Streamlit direct launch)
+- http://localhost:7860 (Docker launch)
 
-| Dataset Size | Processing Time | Memory Usage | Features Generated |
-|--------------|----------------|--------------|-------------------|
-| 10K rows | ~5 seconds | <500 MB | 50-100 features |
-| 100K rows | ~30 seconds | <1 GB | 100-200 features |
-| 1M rows | ~5 minutes | <2 GB | 200-500 features |
+## 📊 Performance Benchmarks
 
-## 🔧 Configuration Options
+| Dataset Size | Processing Time | Memory Usage | Features Generated | Models Trained |
+|--------------|----------------|--------------|-------------------|----------------|
+| 10K rows | 2-5 minutes | 1-2 GB | 50-100 | 15-20 |
+| 50K rows | 10-15 minutes | 3-4 GB | 100-150 | 10-15 |
+| 100K rows | 20-30 minutes | 4-6 GB | 150-200 | 5-10 |
 
-### **Data Processing**
+## ⚙️ Configuration Options
+
+### **Data Processing (TimeFlow Pro)**
 - `missing_threshold`: Threshold for column removal (0.0-0.5)
 - `outlier_method`: IQR, Z-Score, or Isolation Forest
 - `scaling_method`: Robust, Standard, MinMax, or None
-
-### **Feature Engineering**
 - `max_lags`: Maximum lag features (1-20)
 - `seasonal_period`: Seasonal window (7, 30, 90, 365)
-- `rolling_windows`: List of rolling windows [7, 30, 90]
 
-### **Model Preparation**
+### **Machine Learning (ModelForge Pro)**
+- `algorithms`: Selection from 30+ available models
+- `tuning_method`: Grid Search or Random Search
+- `ensemble_method`: Averaging, Voting, Weighted, Stacking
+- `cv_folds`: Cross-validation folds (2-10)
 - `feature_selection_method`: Correlation, Variance, RF, Mutual Info
-- `max_features`: Maximum features to select (5-100)
-- `split_method`: Time-based or random splitting
-
-## 📋 Requirements
-
-### **Core Dependencies**
-```txt
-streamlit>=1.28.0
-pandas>=2.0.0
-numpy>=1.24.0
-plotly>=5.17.0
-scikit-learn>=1.3.0
-```
 
-### **Optional Dependencies**
-```txt
-xgboost>=2.0.0      # For XGBoost feature importance
-lightgbm>=4.0.0     # For LightGBM integration
-statsmodels>=0.14.0 # For advanced time series analysis
-```
+## 💡 Why Choose ML Pipeline PRO Suite
+
+### **For Organizations**
+- **80% faster** ML development through automation
+- **Improved accuracy** with systematic hyperparameter tuning
+- **Reduced expertise barrier** for ML implementation
+- **Complete reproducibility** of workflows
+- **Scalable architecture** for growing data volumes
+
+### **For Researchers**
+- **Standardized methodology** for comparative studies
+- **Automated documentation** of experimental parameters
+- **Easy sharing** of complete ML pipelines
+- **Benchmarking capabilities** across algorithms
+- **Transparent** feature importance analysis
+
+## 🌍 Target Industries
+
+- **Hydrometeorology**: River flow prediction, precipitation forecasting
+- **Finance**: Market trend analysis, risk assessment models
+- **Energy**: Consumption forecasting, renewable generation prediction
+- **Retail**: Demand forecasting, inventory optimization
+- **Healthcare**: Patient monitoring, disease progression modeling
+- **Manufacturing**: Predictive maintenance, quality control
+
+## 📋 System Requirements
+
+### **Minimum Configuration**
+- **CPU**: 4 cores
+- **RAM**: 8 GB
+- **Storage**: 10 GB free space
+- **OS**: Windows 10+, Ubuntu 18.04+, macOS 10.15+
+- **Python**: 3.10 or higher
+
+### **Recommended for Production**
+- **CPU**: 8+ cores
+- **RAM**: 16+ GB
+- **Storage**: 50 GB SSD
+- **GPU**: Optional for accelerated training
+
+## 📦 Export Capabilities
+- Trained models in Pickle/Joblib/Dill format
+- Complete preprocessing pipelines
+- Feature importance reports
+- Model performance comparisons
+- Prediction datasets
+- Configuration files for reproducibility
 
 ## 🤝 Contributing
 
 We welcome contributions! Here's how you can help:
 
 ### **Areas for Contribution**
-1. **New Feature Engineering Methods**
-2. **Additional Visualization Types**
-3. **Export Format Support**
-4. **Performance Optimizations**
-5. **Documentation Improvements**
+1. **New ML Algorithms**: Implement additional machine learning methods
+2. **Visualization Enhancements**: Add new chart types and interactive features
+3. **Performance Optimizations**: Improve speed and memory efficiency
+4. **Export Format Support**: Add support for additional file formats
+5. **Documentation Improvements**: Expand tutorials and examples
 
 ### **Development Workflow**
 ```bash
@@ -227,42 +235,52 @@ git checkout -b feature/new-feature
 
 This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.
 
+If you use ML Pipeline PRO Suite in your research, please cite:
+```
+Arabov M. K., Fatkhulloyev N. I. (2024). ML Pipeline PRO Suite: Comprehensive Machine Learning Platform 
+with Full Data Processing Cycle. Kazan Federal University.
+```
+
 ## 🙏 Acknowledgments
 
-### **Special Thanks To:**
-- **Streamlit Team** for the amazing framework
-- **Hugging Face** for hosting the Space
-- **Open Source Community** for invaluable libraries
-- **All Contributors** who helped improve TimeFlow Pro
+### **Core Development Team**
+- **Mullosharif Arabov** - Platform Architecture & ML Algorithms
+- **Narzullo Fatkhulloyev** - Data Processing & Interface Design
+
+### **Built With**
+- 🐍 **Python** - Core programming language
+- 📊 **Streamlit** - Web interface framework
+- 🔧 **Scikit-learn** - Machine learning algorithms
+- 📈 **Pandas & NumPy** - Data manipulation
+- 🎨 **Plotly** - Interactive visualizations
+- ☁️ **Hugging Face** - Deployment platform
 
-### **Built With:**
-- 🐍 Python
-- 📊 Streamlit
-- 🎨 Plotly
-- 🔧 Scikit-learn
-- 📈 Pandas & NumPy
+### **Special Thanks**
+- **Kazan Federal University** for research support
+- **Open Source Community** for invaluable libraries
+- **Early Testers** for feedback and improvements
 
 ## 📞 Support & Contact
 
-### **Get Help:**
+### **Technical Support**
 - 📧 **Email**: cool.araby@gmail.com
-- 💬 **Issues**: [GitHub Issues](https://github.com/your-username/timeflow-pro/issues)
-- 💡 **Discussions**: [Community Forum](https://github.com/your-username/timeflow-pro/discussions)
+- 💬 **Issues**: [GitHub Issues](https://github.com/arabovs-ai-lab/ml-pipeline-pro-suite/issues)
+- 🌐 **Live Demo**: [Hugging Face Space](https://huggingface.co/spaces/arabovs-ai-lab/ml-pipeline-pro-dashboard)
 
-### **Stay Updated:**
-- ⭐ **Star** the repository
-- 👁️ **Watch** for releases
-- 🔔 **Enable notifications**
+### **Stay Connected**
+- ⭐ **Star** the repository on GitHub
+- 👁️ **Watch** for releases and updates
+- 🔔 **Follow** on Hugging Face for new features
 
 ---
 
 <div align="center">
 
-**Transform Your Time Series Data with Ease**
+**From Raw Data to Production Models in One Platform**
 
-*TimeFlow Pro - Making Data Preparation Simple and Powerful*
+*ML Pipeline PRO Suite - Simplifying Machine Learning Workflows*
 
-[![Follow on Hugging Face](https://img.shields.io/badge/Follow%20on-🤗%20Hugging%20Face-yellow)](https://huggingface.co/your-username)
-[![GitHub Stars](https://img.shields.io/github/stars/your-username/timeflow-pro?style=social)](https://github.com/your-username/timeflow-pro)
+[![Live Demo](https://img.shields.io/badge/🚀-Try%20Live%20Demo-brightgreen)](https://huggingface.co/spaces/arabovs-ai-lab/ml-pipeline-pro-dashboard)
+[![Documentation](https://img.shields.io/badge/📚-View%20Documentation-blue)](https://github.com/arabovs-ai-lab/ml-pipeline-pro-suite/wiki)
 
 </div>

app.py

@@ -24,14 +24,14 @@ def check_preprocessing_status():
         
         # Check if the folder exists
         if not preprocessing_path.exists():
-            return False, "Preprocessed data folder not found", None
+            return False, "Preprocessing folder not found", None
         
         # Check if the folder is not empty
         files = list(preprocessing_path.glob("*"))
         files = [f for f in files if f.is_file()]
         
         if len(files) == 0:
-            return False, "Preprocessed data folder is empty", None
+            return False, "Preprocessing folder is empty", None
         
         # Supported file extensions
         supported_extensions = {'.csv', '.parquet', '.xlsx', '.json', '.feather'}
@@ -73,7 +73,7 @@ def check_preprocessing_status():
         return True, f"Folder contains files but no valid data loaded: {file_list}", None
         
     except Exception as e:
-        return False, f"Error checking preprocessing status: {str(e)}", None
+        return False, f"Error checking preprocessing folder: {str(e)}", None
 
 # Path configuration
 current_dir = Path(__file__).parent
@@ -615,20 +615,9 @@ def show_dashboard():
             • SHAP analysis
             """)
             
-            # Check data availability
-            current_data = get_current_data()
-            has_data = current_data is not None
-            
-            if st.button("Open ML Pipeline", type="primary", width='stretch', disabled=not has_data):
+            if st.button("Open ML Pipeline", type="primary", width='stretch'):
                 st.session_state.current_view = 'ml'
                 st.rerun()
-            
-            if not has_data:
-                preprocessing_ready, preprocessing_message, _ = check_preprocessing_status()
-                if not preprocessing_ready:
-                    st.warning(f"⚠️ {preprocessing_message}. First, perform data preprocessing.")
-                else:
-                    st.warning("⚠️ First, preprocessed data is needed")
     
     st.markdown("---")
     st.markdown("### 🛠️ Quick Workflow")
@@ -729,7 +718,7 @@ def show_dashboard():
                 
                 except Exception as e:
                     st.error(f"File loading error: {str(e)}")
-
+                    
 def run_preprocessor():
     """Runs the preprocessing module"""
     st.title("🧹 TimeFlow Pro - Data Preprocessing")
@@ -945,18 +934,87 @@ def run_ml_pipeline():
     data = get_current_data()
     
     if data is None:
+        # Check preprocessing status
         preprocessing_ready, preprocessing_message, _ = check_preprocessing_status()
-        st.warning("⚠️ No preprocessed data available!")
-        st.info(f"Please perform data preprocessing first. Status: {preprocessing_message}")
         
-        col_back = st.columns([1, 2, 1])
-        with col_back[1]:
-            if st.button("← Go to data preprocessing", type="primary", width='stretch'):
+        if not preprocessing_ready:
+            st.warning("⚠️ **Preprocessing folder is missing or empty**")
+            st.info("""
+            **To get started:**
+            1. **Run TimeFlow Pro** first to preprocess your data
+            2. **Or** use data from other sources
+            """)
+        else:
+            st.warning("⚠️ **No data loaded**")
+            st.info("Please load data using one of the available options")
+        
+        # Navigation buttons
+        st.markdown("---")
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            if st.button("← Go to TimeFlow Pro", type="secondary", width='stretch'):
                 st.session_state.current_view = 'preprocessor'
                 st.rerun()
-        return
+                
+        with col2:
+            if st.button("🏠 Go to Dashboard", type="secondary", width='stretch'):
+                st.session_state.current_view = 'dashboard'
+                st.rerun()
+        
+        # Load ML module even without data for initialization
+        if 'ml' not in st.session_state.module_cache:
+            with st.spinner("⏳ Loading ML Pipeline PRO module..."):
+                module = load_module_dynamically('ml')
+                if module:
+                    st.session_state.module_cache['ml'] = module
+                    st.success("✅ ML module successfully loaded!")
+                else:
+                    st.error("❌ Failed to load ML module")
+                    return
+        
+        # Show ML module is loaded and ready
+        st.markdown("---")
+        st.markdown("### 🚀 ML Pipeline Interface")
+        st.info("💡 ML Pipeline PRO is loaded and ready. Load data to start training.")
+        
+        # Load ML module interface with error handling
+        try:
+            module = st.session_state.module_cache['ml']
+            
+            # Attempt to run ML module in data-less mode
+            if hasattr(module, 'main'):
+                module.data = None
+                module.target_column = None
+                
+                try:
+                    module.main()
+                except Exception as e:
+                    st.warning(f"ML module requires data to function properly: {str(e)}")
+                    
+            elif hasattr(module, 'run'):
+                try:
+                    module.run()
+                except Exception as e:
+                    st.warning(f"ML module requires data: {str(e)}")
+                    
+            elif hasattr(module, 'StreamlitApp'):
+                try:
+                    ml_app = module.StreamlitApp()
+                    ml_app.run()
+                except Exception as e:
+                    st.warning(f"ML module requires data: {str(e)}")
+            else:
+                st.info("ML module is loaded. Load data to access full functionality.")
+        
+        except Exception as e:
+            st.error(f"❌ Error loading ML interface: {str(e)}")
+        
+        return  # Exit since no data for full operation
     
-    # Check that data is valid
+    # ========== DATA AVAILABLE ==========
+    
+    # Validate data format
     if not isinstance(data, pd.DataFrame) or data.empty:
         st.error("❌ Invalid data format! Please load valid data.")
         if st.button("← Go to data preprocessing", type="primary", width='stretch'):
@@ -999,6 +1057,7 @@ def run_ml_pipeline():
         
         st.info(f"**Selected target:** `{target_col}` | **Features:** {len(st.session_state.data_info['feature_columns'])} columns")
     
+    # Load ML module if not already loaded
     if 'ml' not in st.session_state.module_cache:
         with st.spinner("⏳ Loading ML Pipeline PRO module..."):
             module = load_module_dynamically('ml')
@@ -1036,9 +1095,8 @@ def run_ml_pipeline():
                 st.error(f"❌ Error processing ML results: {str(e)}")
                 st.code(traceback.format_exc())
                 
-                # Try to save raw results anyway
+                # Fallback: save raw results
                 if results:
-                    # Try to create a basic structure from raw results
                     basic_results = {}
                     if isinstance(results, dict):
                         for model_name, result in results.items():
@@ -1069,20 +1127,19 @@ def run_ml_pipeline():
         st.markdown("### 🚀 ML Pipeline Interface")
         
         try:
-            # Create a safe execution context for the ML module
+            # Execute ML module with safe error handling
             if hasattr(module, 'main'):
-                # Run in safe mode
                 try:
                     module.main()
                 except Exception as e:
                     st.error(f"❌ Error executing ML module: {str(e)}")
                     st.code(traceback.format_exc())
                     
-                    # Check if results exist despite the error
+                    # Check for results despite error
                     if hasattr(module, 'results') and module.results is not None:
                         ml_results_handler(module.results)
                     
-                    # Show alternative interface
+                    # Show fallback interface
                     show_alternative_ml(data, target_col)
                     return
                     
@@ -1098,7 +1155,7 @@ def run_ml_pipeline():
                 show_alternative_ml(data, target_col)
                 return
             
-            # Check for results in the module
+            # Check for results in module
             if hasattr(module, 'results') and module.results is not None:
                 ml_results_handler(module.results)
             
@@ -1115,7 +1172,7 @@ def run_ml_pipeline():
             st.error(f"❌ Error executing ML module: {str(module_error)}")
             st.code(traceback.format_exc())
             
-            # Check if results exist in session_state
+            # Check for existing results
             if 'ml_results' in st.session_state and st.session_state.ml_results is not None:
                 st.info("Found existing results in session_state")
                 if st.button("📊 Use existing results", width='stretch'):
@@ -1133,7 +1190,7 @@ def run_ml_pipeline():
         st.error(f"❌ Error launching ML module: {str(e)}")
         st.code(traceback.format_exc())
         show_alternative_ml(data, target_col)
-
+                
 def show_alternative_ml(data=None, target_col=None):
     """Shows an alternative ML interface with robust metric handling"""
     st.markdown("---")

src/enhanced_preprocessing/run_pipeline.py

@@ -5,7 +5,7 @@
 import pandas as pd
 import os
 
-from main_pipeline import EnhancedDataPreprocessingPipeline
+from enhanced_preprocessing.main_pipeline import EnhancedDataPreprocessingPipeline
 from src.enhanced_preprocessing.config.config import Config
 
 if __name__ == "__main__":

streamlit/preprocessor_app.py

@@ -6,7 +6,7 @@ import os
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
-from main_pipeline import EnhancedDataPreprocessingPipeline
+from src.enhanced_preprocessing.main_pipeline import EnhancedDataPreprocessingPipeline
 from src.enhanced_preprocessing.config.config import Config
 import streamlit as st
 import pandas as pd