Initial commit of AutoML project

.gitignore

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+# Ignore Python cache
+__pycache__/
+*.pyc
+
+# Ignore virtual environment
+venv/
+
+# Ignore temporary data files
+.cache/

.gradio/certificate.pem

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AI_SQL_Assistant.log

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+INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
+INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
+INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
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+INFO:webbrowser:Groq client initialized successfully
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+INFO:httpx:HTTP Request: GET https://api.gradio.app/pkg-version "HTTP/1.1 200 OK"

app.py

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+import matplotlib
+matplotlib.use('Agg')
+from flask import Flask, request, jsonify, send_from_directory
+import pandas as pd
+from rag.memory import store_dataset, get_dataset, store_model_results, get_model_results
+from rag.rag_query import query_dataset_with_groq
+from models.supervised import train_model as train_supervised
+from models.unsupervised import train_unsupervised
+from visuals.charts import (
+    plot_histogram, plot_bar, plot_scatter, plot_box, plot_pie, plot_heatmap,
+    plot_confusion_matrix, plot_roc_curve, plot_feature_importance,
+    plot_elbow_curve, plot_cluster_plot, plot_dendrogram, plot_tsne
+)
+import os
+import logging
+import json
+import re
+import matplotlib.pyplot as plt
+import io
+import base64
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+app = Flask(__name__, static_folder='frontend')
+dataset_name = "active_dataset"
+
+@app.route('/')
+def index():
+    """Serve the main HTML page."""
+    return send_from_directory(app.static_folder, 'index.html')
+
+@app.route('/<path:path>')
+def static_proxy(path):
+    """Serve static files from the frontend directory."""
+    return send_from_directory(app.static_folder, path)
+
+@app.route('/api/upload', methods=['POST'])
+def upload_csv():
+    """Handle CSV file uploads, store the dataset, and return a success message."""
+    if 'file' not in request.files:
+        logging.warning("No file part in upload request.")
+        return jsonify({"error": "No file part"}), 400
+    file = request.files['file']
+    if file.filename == '':
+        logging.warning("No selected file in upload request.")
+        return jsonify({"error": "No selected file"}), 400
+    try:
+        df = pd.read_csv(file)
+        store_dataset(dataset_name, df)
+        logging.info(f"Uploaded {df.shape[0]} rows and {df.shape[1]} columns.")
+        return jsonify({"message": f"Uploaded {df.shape[0]} rows and {df.shape[1]} columns."})
+    except Exception as e:
+        logging.error(f"Error uploading file: {e}")
+        return jsonify({"error": str(e)}), 500
+
+@app.route('/api/columns', methods=['GET'])
+def get_columns():
+    """Return a list of column names from the currently loaded dataset."""
+    df = get_dataset(dataset_name)
+    if df is not None:
+        return jsonify({"columns": list(df.columns)})
+    logging.info("No dataset loaded when requesting columns.")
+    return jsonify({"columns": []})
+
+@app.route('/api/learning_type', methods=['GET'])
+def get_learning_type():
+    """Determine and return the learning type (supervised/unsupervised) and target column using LLM intelligence."""
+    df = get_dataset(dataset_name)
+    if df is None:
+        logging.warning("No dataset uploaded when requesting learning type.")
+        return jsonify({"error": "No dataset uploaded yet."}), 400
+
+    prompt = (
+        "You are an expert data scientist. Your task is to analyze a dataset and determine its learning type (supervised or unsupervised). "
+        "If it's a supervised learning problem, you MUST identify the single target column that the other columns would predict. "
+        "A target column is typically a label, outcome, or value that is being predicted (e.g., 'price', 'churn', 'diagnosis', 'category', 'sales'). "
+        "If no such clear target column exists, it's an unsupervised problem. "
+        "Respond ONLY with a JSON object, and nothing else. Do NOT include any introductory/concluding remarks, explanations, or markdown outside the JSON. "
+        "The JSON must strictly follow this format: "
+        "{\"learning_type\": \"Supervised\", \"target_column\": \"your_target_column_name\"} "
+        "OR "
+        "{\"learning_type\": \"Unsupervised\", \"target_column\": null}. "
+        "\n\n"
+        f"COLUMNS AND DATA TYPES:\n{dtypes_str}\n\n"
+        f"DATA SAMPLE:\n{df.head().to_string()}"
+    )
+
+    try:
+        response_text = query_dataset_with_groq(dataset_name, prompt).strip()
+        logging.info(f"Raw LLM response for learning type: {response_text}")
+
+        # Attempt to parse the JSON response
+        try:
+            data = json.loads(response_text)
+            learning_type = data.get("learning_type", "Unsupervised")
+            target_column = data.get("target_column")
+            logging.info(f"Parsed LLM response - learning_type: {learning_type}, target_column: {target_column}")
+
+            # Validate the target column if learning_type is Supervised
+            if learning_type == "Supervised":
+                if target_column is None or target_column not in df.columns:
+                    logging.warning(f"LLM suggested supervised learning but target column '{target_column}' is invalid or not found. Defaulting to Unsupervised.")
+                    learning_type = "Unsupervised"
+                    target_column = None
+            else:
+                # If LLM says unsupervised, ensure target_column is null
+                target_column = None
+
+        except json.JSONDecodeError:
+            logging.error(f"LLM response is not a valid JSON: {response_text}. Attempting regex fallback.")
+            # Fallback: Try to extract using regex if JSON parsing fails (less reliable)
+            match = re.search(r'"learning_type"\s*:\s*"(Supervised|Unsupervised)"(?:,\s*"target_column"\s*:\s*"?([a-zA-Z0-9_]+)?"?)?', response_text)
+            if match:
+                learning_type = match.group(1)
+                target_column = match.group(2) if match.group(2) else None
+                logging.info(f"Regex fallback parsed - learning_type: {learning_type}, target_column: {target_column}")
+
+                if learning_type == "Supervised" and (target_column is None or target_column not in df.columns):
+                    logging.warning(f"Regex fallback: Invalid target column '{target_column}' for supervised. Defaulting to Unsupervised.")
+                    learning_type = "Unsupervised"
+                    target_column = None
+                elif learning_type == "Unsupervised":
+                    target_column = None
+            else:
+                logging.error("Could not parse LLM response for learning type using regex fallback. Defaulting to Unsupervised.")
+                learning_type = "Unsupervised"
+                target_column = None
+
+        return jsonify({"learning_type": learning_type, "target_column": target_column})
+
+    except Exception as e:
+        logging.error(f"An unexpected error occurred while determining learning type: {str(e)}", exc_info=True)
+        # Fallback to a default in case of any error during Groq call or initial processing
+        return jsonify({"learning_type": "Unsupervised", "target_column": None})
+
+@app.route('/api/train', methods=['POST'])
+def train_model_api():
+    """Handle model training requests for both supervised and unsupervised learning."""
+    data = request.json
+    model_name = data.get('model_name')
+    target_col = data.get('target_col')
+    learning_type = data.get('learning_type')
+
+    df = get_dataset(dataset_name)
+    if df is None:
+        logging.warning("No dataset uploaded when requesting model training.")
+        return jsonify({"error": "No dataset uploaded yet."}), 400
+
+    if learning_type == "Supervised":
+        if not target_col or target_col == 'None':
+            logging.warning("No target column provided for supervised training.")
+            return jsonify({"error": "Please select a target column for supervised learning."}), 400
+        
+        model, metrics, y_test, y_pred, y_pred_proba, X_test = train_supervised(df, target_col, model_name)
+        if model:
+            store_model_results(dataset_name, model, y_test, y_pred, y_pred_proba, X_test)
+            logging.info(f"{model_name} trained successfully for supervised learning.")
+            return jsonify({"message": f"{model_name} trained successfully.", "metrics": metrics})
+        else:
+            logging.error(f"Failed to train {model_name} for supervised learning. Reason: {metrics}")
+            return jsonify({"error": f"Failed to train {model_name}. Reason: {metrics}"}), 500
+    else: # Unsupervised
+        model, result = train_unsupervised(df, model_name)
+        if model:
+            logging.info(f"{model_name} trained successfully for unsupervised learning.")
+            return jsonify({"message": f"{model_name} trained successfully.", "result": result.tolist() if hasattr(result, 'tolist') else result})
+        else:
+            logging.error(f"Failed to train {model_name} for unsupervised learning. Reason: {result}")
+            return jsonify({"error": f"Failed to train {model_name}. Reason: {result}"}), 500
+
+@app.route('/api/plot', methods=['POST'])
+def generate_plot_api():
+    """Generate and return a plot based on the requested type and columns."""
+    data = request.json
+    plot_type = data.get('plot_type')
+    col1 = data.get('col1')
+    col2 = data.get('col2')
+
+    df = get_dataset(dataset_name)
+    if df is None:
+        logging.warning("No dataset loaded when requesting plot generation.")
+        return jsonify({"error": "No data loaded."}), 400
+
+    plot_functions = {
+        "Histogram": plot_histogram,
+        "Bar": plot_bar,
+        "Scatter": plot_scatter,
+        "Box": plot_box,
+        "Pie": plot_pie,
+        "Heatmap": plot_heatmap,
+        "Elbow Curve": plot_elbow_curve,
+        "Cluster Plot": plot_cluster_plot,
+        "Dendrogram": plot_dendrogram,
+        "t-SNE": plot_tsne,
+        "Confusion Matrix": plot_confusion_matrix,
+        "ROC Curve": plot_roc_curve,
+        "Feature Importance Plot": plot_feature_importance
+    }
+
+    if plot_type not in plot_functions:
+        logging.warning(f"Unsupported plot type requested: {plot_type}")
+        return jsonify({"error": "Plot not supported."}), 400
+
+    fig, err = None, None
+    try:
+        if plot_type == "Scatter":
+            fig, err = plot_functions[plot_type](df, col1, col2, data.get('color_col'))
+        elif plot_type == "Box":
+            fig, err = plot_functions[plot_type](df, col1, col2)
+        elif plot_type == "Heatmap":
+            fig, err = plot_functions[plot_type](df)
+        elif plot_type == "Elbow Curve":
+            from utils.data_cleaner import prepare_data
+            X_prepared, _ = prepare_data(df)
+            fig, err = plot_functions[plot_type](X_prepared)
+        elif plot_type == "Cluster Plot":
+            from utils.data_cleaner import prepare_data
+            from sklearn.cluster import KMeans
+            X_prepared, _ = prepare_data(df)
+            if X_prepared.empty:
+                return jsonify({"error": "Data is empty after cleaning for Cluster Plot."}), 400
+            # Perform KMeans clustering (e.g., with 3 clusters)
+            n_clusters = 3 # Default number of clusters
+            if len(X_prepared) < n_clusters:
+                n_clusters = len(X_prepared) # Adjust n_clusters if data points are fewer
+            if n_clusters == 0:
+                return jsonify({"error": "Not enough data points to form clusters."}), 400
+            kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
+            kmeans.fit(X_prepared)
+            labels = kmeans.labels_
+            fig, err = plot_functions[plot_type](X_prepared, labels=labels)
+        elif plot_type == "Dendrogram":
+            from utils.data_cleaner import prepare_data
+            X_prepared, _ = prepare_data(df)
+            fig, err = plot_functions[plot_type](X_prepared)
+        elif plot_type == "t-SNE":
+            from utils.data_cleaner import prepare_data
+            X_prepared, _ = prepare_data(df)
+            fig, err = plot_functions[plot_type](X_prepared)
+        elif plot_type in ["Confusion Matrix", "ROC Curve", "Feature Importance Plot"]:
+            model_results = get_model_results(dataset_name)
+            if not model_results:
+                logging.warning(f"No trained model found for {plot_type} plot.")
+                return jsonify({"error": "No trained model found. Please train a supervised model first."}), 400
+            
+            model = model_results['model']
+            y_test = model_results['y_test']
+            y_pred = model_results['y_pred']
+            y_pred_proba = model_results['y_pred_proba']
+            X_test = model_results['X_test']
+
+            if plot_type == "Confusion Matrix":
+                # Need to get class names. For simplicity, using unique values from y_test.
+                class_names = [str(c) for c in sorted(pd.Series(y_test).unique())]
+                fig, err = plot_functions[plot_type](y_test, y_pred, class_names)
+            elif plot_type == "ROC Curve":
+                if y_pred_proba is None:
+                    logging.warning("ROC Curve requested but model does not provide probability predictions.")
+                    return jsonify({"error": "ROC Curve requires probability predictions, which this model does not provide."}), 400
+                fig, err = plot_functions[plot_type](y_test, y_pred_proba)
+            elif plot_type == "Feature Importance Plot":
+                if not hasattr(model, 'feature_importances_'):
+                    logging.warning("Feature Importance Plot requested but model does not have feature importances.")
+                    return jsonify({"error": "Model does not have feature importances to plot."}), 400
+                # Feature names are from X_test columns
+                feature_names = X_test.columns.tolist()
+                fig, err = plot_functions[plot_type](model, feature_names)
+        else:
+            # Default case for plots that only need one column (e.g., Histogram, Bar, Pie)
+            fig, err = plot_functions[plot_type](df, col1)
+
+        if err:
+            logging.error(f"Plot generation error for {plot_type}: {err}")
+            return jsonify({"error": err}), 400
+        
+        # Save plot to a BytesIO object and encode to base64
+        buf = io.BytesIO()
+        fig.savefig(buf, format='png', bbox_inches='tight')
+        plt.close(fig)
+        buf.seek(0)
+        img_str = base64.b64encode(buf.read()).decode('utf-8')
+        return jsonify({'image': img_str})
+    except Exception as e:
+        logging.error(f"An unexpected error occurred during plot generation for {plot_type}: {e}", exc_info=True)
+        return jsonify({"error": f"An internal server error occurred: {str(e)}"}), 500
+
+@app.route('/api/plot_options', methods=['GET'])
+def plot_options():
+    """Return a list of available plot options based on the dataset's learning type."""
+    df = get_dataset(dataset_name)
+    if df is None:
+        logging.warning("No dataset uploaded when requesting plot options.")
+        return jsonify({"error": "No dataset uploaded yet."}), 400
+
+    # Get learning type from the dedicated endpoint
+    learning_type_response = get_learning_type()
+    learning_type_data = learning_type_response.get_json()
+    learning_type = learning_type_data.get('learning_type', 'Unsupervised')
+
+    if learning_type == "Supervised":
+        plots = ["Histogram", "Bar", "Scatter", "Box", "Pie", "Heatmap", "Confusion Matrix", "ROC Curve", "Feature Importance Plot"]
+    else:
+        plots = ["Histogram", "Bar", "Scatter", "Box", "Pie", "Heatmap", "Cluster Plot", "Elbow Curve", "Dendrogram", "t-SNE"]
+
+    # Ensure Scatter Plot is always available if there are at least two numeric columns
+    numeric_cols = df.select_dtypes(include=np.number).columns
+    if len(numeric_cols) >= 2 and "Scatter" not in plots:
+        plots.insert(2, "Scatter") # Insert at a reasonable position
+    
+    return jsonify({"plots": plots})
+
+@app.route('/api/ask', methods=['POST'])
+def ask_question_api():
+    """Handle user questions to the AI about the dataset."""
+    data = request.json
+    user_query = data.get('user_query')
+    if not user_query:
+        logging.warning("Empty user query received for AI assistant.")
+        return jsonify({"error": "Please ask a question."}), 400
+    
+    answer = query_dataset_with_groq(dataset_name, user_query)
+    return jsonify({"answer": answer})
+
+if __name__ == '__main__':
+    logging.info("Starting Flask application...")
+    app.run(debug=True, port=5001)

catboost_info/catboost_training.json

@@ -0,0 +1,1004 @@
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config.py

@@ -0,0 +1,6 @@
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")

frontend/index.html

@@ -0,0 +1,91 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AutoML Companion</title>
+    <link rel="stylesheet" href="/style.css">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+</head>
+<body>
+    <div class="app-layout">
+        <aside class="sidebar">
+            <div class="sidebar-header">
+                <h2>AutoML</h2>
+                <button id="sidebar-toggle"><i class="fas fa-angle-left"></i></button>
+            </div>
+            <nav class="sidebar-nav">
+                <a href="#" class="nav-link active" data-page="load-dataset"><i class="fas fa-upload"></i><span class="nav-text">Load Dataset</span></a>
+                <a href="#" class="nav-link" data-page="train"><i class="fas fa-cogs"></i><span class="nav-text">Model Trainer</span></a>
+                <a href="#" class="nav-link" data-page="visualize"><i class="fas fa-chart-bar"></i><span class="nav-text">Visualize</span></a>
+                <a href="#" class="nav-link" data-page="ask"><i class="fas fa-question-circle"></i><span class="nav-text">Ask AI</span></a>
+            </nav>
+        </aside>
+        <main class="main-content">
+            <div class="container">
+                <div id="load-dataset" class="page active">
+                    <div class="card">
+                        <h2><i class="fas fa-upload"></i> Load Dataset</h2>
+                        <p>Upload a CSV file to begin your analysis.</p>
+                        <div class="file-upload-wrapper">
+                            <input type="file" id="csv-upload" accept=".csv">
+                            <label for="csv-upload">Choose a file</label>
+                        </div>
+                        <div id="upload-status"></div>
+                        <label for="column-list">Available Columns:</label>
+                        <select id="column-list" multiple></select>
+                    </div>
+                </div>
+                <div id="train" class="page">
+                    <div class="card">
+                        <h2><i class="fas fa-cogs"></i> Model Trainer</h2>
+                        <p>Choose a learning type and model to train on your data.</p>
+                        <select id="learning-type" disabled>
+                            <option value="Supervised">Supervised</option>
+                            <option value="Unsupervised">Unsupervised</option>
+                        </select>
+                        <select id="model-dropdown"></select>
+                        <select id="target-column-dropdown"></select>
+                        <button id="train-model">Train Model</button>
+                        <div id="train-output"></div>
+                    </div>
+                </div>
+                <div id="visualize" class="page">
+                    <div class="card">
+                        <h2><i class="fas fa-chart-bar"></i> Visualize</h2>
+                        <p>Select a plot type and columns to generate a visualization.</p>
+                        <select id="plot-type">
+                            <option value="">Select Plot Type</option>
+                        </select>
+                        <label for="plot-col1">Column 1 (Numeric for Box Plot)</label>
+                        <select id="plot-col1"></select>
+                        <label for="plot-col2">Column 2 (Categorical for Box Plot)</label>
+                        <select id="plot-col2"></select>
+                        <div id="scatter-color-container" style="display: none;">
+                            <label for="scatter-color">Color by (Optional)</label>
+                            <select id="scatter-color"></select>
+                        </div>
+                        <button id="generate-plot">Generate Plot</button>
+                        <div id="plot-output">
+                            <div class="loader"></div>
+                            <img id="plot-img" src="" alt="">
+                        </div>
+                        <div id="plot-error"></div>
+                        <pre><code id="plot-code"></code></pre>
+                    </div>
+                </div>
+                <div id="ask" class="page">
+                    <div class="card">
+                        <h2><i class="fas fa-question-circle"></i> Ask AI</h2>
+                        <p>Ask a question about your dataset in plain English.</p>
+                        <input type="text" id="ai-question" placeholder="e.g., 'What is the correlation between X and Y?'">
+                        <button id="ask-ai">Ask</button>
+                        <div id="ai-answer"></div>
+                    </div>
+                </div>
+            </div>
+        </main>
+    </div>
+    <script src="/main.js"></script>
+</body>
+</html>

frontend/main.js

@@ -0,0 +1,298 @@
+document.addEventListener('DOMContentLoaded', () => {
+    const navLinks = document.querySelectorAll('.nav-link');
+    const pages = document.querySelectorAll('.page');
+    const sidebar = document.querySelector('.sidebar');
+    const mainContent = document.querySelector('.main-content');
+    const sidebarToggle = document.getElementById('sidebar-toggle');
+    const loader = document.querySelector('.loader');
+
+    function animateNavText() {
+        const navTexts = document.querySelectorAll('.nav-text');
+        navTexts.forEach(text => {
+            text.style.animation = 'none';
+            text.offsetHeight; // Trigger reflow
+            text.style.animation = '';
+        });
+    }
+
+    navLinks.forEach(link => {
+        link.addEventListener('click', (e) => {
+            e.preventDefault();
+            const pageId = link.dataset.page;
+
+            pages.forEach(page => {
+                page.classList.remove('active');
+            });
+
+            navLinks.forEach(navLink => {
+                navLink.classList.remove('active');
+            });
+
+            document.getElementById(pageId).classList.add('active');
+            link.classList.add('active');
+        });
+    });
+
+    sidebarToggle.addEventListener('click', () => {
+        sidebar.classList.toggle('collapsed');
+        mainContent.classList.toggle('collapsed');
+        if (!sidebar.classList.contains('collapsed')) {
+            animateNavText();
+        }
+    });
+
+    function formatAIResponse(text) {
+        text = text.replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>');
+        text = text.replace(/^\d+\.\s+(.*)/gm, '<li>$1</li>');
+        text = text.replace(/(<li>.*<\/li>)/s, '<ol>$1<\/ol>');
+        text = text.replace(/^\*\s+(.*)/gm, '<li>$1</li>');
+        text = text.replace(/(<li>.*<\/li>)/s, '<ul>$1<\/ul>');
+        return text;
+    }
+
+    const csvUpload = document.getElementById('csv-upload');
+    const uploadStatus = document.getElementById('upload-status');
+    const columnList = document.getElementById('column-list');
+    const plotType = document.getElementById('plot-type');
+    const plotCol1 = document.getElementById('plot-col1');
+    const plotCol2 = document.getElementById('plot-col2');
+    const scatterColorContainer = document.getElementById('scatter-color-container');
+    const scatterColor = document.getElementById('scatter-color');
+    const generatePlot = document.getElementById('generate-plot');
+    const plotImg = document.getElementById('plot-img');
+    const plotError = document.getElementById('plot-error');
+    const learningType = document.getElementById('learning-type');
+    const modelDropdown = document.getElementById('model-dropdown');
+    const targetColumnDropdown = document.getElementById('target-column-dropdown');
+    const trainModel = document.getElementById('train-model');
+    const trainOutput = document.getElementById('train-output');
+    const aiQuestion = document.getElementById('ai-question');
+    const askAi = document.getElementById('ask-ai');
+    const aiAnswer = document.getElementById('ai-answer');
+
+    csvUpload.addEventListener('change', async (event) => {
+        const file = event.target.files[0];
+        if (!file) return;
+
+        uploadStatus.textContent = 'Uploading...';
+        const formData = new FormData();
+        formData.append('file', file);
+
+        try {
+            const response = await fetch('/api/upload', {
+                method: 'POST',
+                body: formData
+            });
+            const result = await response.json();
+            uploadStatus.textContent = result.message || result.error;
+            if (response.ok) {
+                updateColumnSelectors();
+                setLearningType();
+                updatePlotOptions();
+            }
+        } catch (error) {
+            uploadStatus.textContent = `Error: ${error.message}`;
+        }
+    });
+
+    async function updateColumnSelectors() {
+        try {
+            const response = await fetch('/api/columns');
+            const result = await response.json();
+            const columns = result.columns || [];
+
+            [columnList, plotCol1, plotCol2, targetColumnDropdown, scatterColor].forEach(selector => {
+                selector.innerHTML = '';
+                const defaultOption = document.createElement('option');
+                defaultOption.value = 'None';
+                defaultOption.textContent = 'None';
+                selector.appendChild(defaultOption);
+
+                columns.forEach(col => {
+                    const option = document.createElement('option');
+                    option.value = col;
+                    option.textContent = col;
+                    selector.appendChild(option);
+                });
+            });
+        } catch (error) {
+            console.error('Error updating column selectors:', error);
+        }
+    }
+
+    plotType.addEventListener('change', () => {
+        if (plotType.value === 'Scatter') {
+            scatterColorContainer.style.display = 'block';
+        } else {
+            scatterColorContainer.style.display = 'none';
+        }
+    });
+
+    generatePlot.addEventListener('click', async () => {
+        if (!plotType.value) {
+            plotError.textContent = 'Please select a plot type.';
+            return;
+        }
+
+        loader.style.display = 'block';
+        plotImg.src = '';
+        plotError.textContent = '';
+
+        const body = {
+            plot_type: plotType.value,
+            col1: plotCol1.value,
+            col2: plotCol2.value,
+            color_col: scatterColor.value
+        };
+
+        try {
+            const response = await fetch('/api/plot', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify(body)
+            });
+            const result = await response.json();
+            if (result.image) {
+                plotImg.src = `data:image/png;base64,${result.image}`;
+            } else {
+                plotError.textContent = result.error;
+            }
+        } catch (error) {
+            plotError.textContent = `Error: ${error.message}`;
+        } finally {
+            loader.style.display = 'none';
+        }
+    });
+
+    function formatMetrics(metrics) {
+        let formatted = '\n';
+        for (const [key, value] of Object.entries(metrics)) {
+            formatted += `<strong>${key}:</strong> ${JSON.stringify(value, null, 2)}\n`;
+        }
+        return formatted;
+    }
+
+    async function setLearningType() {
+        try {
+            const response = await fetch('/api/learning_type');
+            const result = await response.json();
+            if (result.learning_type) {
+                learningType.disabled = false;
+                learningType.value = result.learning_type;
+                learningType.dispatchEvent(new Event('change'));
+                learningType.disabled = true;
+                if (result.learning_type === 'Supervised' && result.target_column) {
+                    targetColumnDropdown.value = result.target_column;
+                }
+            }
+        } catch (error) {
+            console.error('Error setting learning type:', error);
+        }
+    }
+
+    async function updatePlotOptions() {
+        try {
+            const response = await fetch('/api/plot_options');
+            const result = await response.json();
+            const plots = result.plots || [];
+
+            plotType.innerHTML = '';
+            const defaultOption = document.createElement('option');
+            defaultOption.value = '';
+            defaultOption.textContent = 'Select Plot Type';
+            plotType.appendChild(defaultOption);
+
+            plots.forEach(plotName => {
+                const option = document.createElement('option');
+                option.value = plotName;
+                option.textContent = plotName;
+                plotType.appendChild(option);
+            });
+        } catch (error) {
+            console.error('Error updating plot options:', error);
+        }
+    }
+
+    trainModel.addEventListener('click', async () => {
+        trainOutput.textContent = 'Training in progress...';
+        loader.style.display = 'block';
+        const body = {
+            learning_type: learningType.value,
+            model_name: modelDropdown.value,
+            target_col: targetColumnDropdown.value
+        };
+
+        try {
+            const response = await fetch('/api/train', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify(body)
+            });
+            const result = await response.json();
+            let output = result.message || result.error;
+            if (result.metrics) {
+                output += formatMetrics(result.metrics);
+            }
+            if (result.result) {
+                output += `\n<strong>Result:</strong> ${JSON.stringify(result.result, null, 2)}`;
+            }
+            trainOutput.innerHTML = output;
+        } catch (error) {
+            trainOutput.textContent = `Error: ${error.message}`;
+        } finally {
+            loader.style.display = 'none';
+        }
+    });
+
+    askAi.addEventListener('click', async () => {
+        aiAnswer.textContent = 'Thinking...';
+        loader.style.display = 'block';
+        const body = {
+            user_query: aiQuestion.value
+        };
+
+        try {
+            const response = await fetch('/api/ask', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify(body)
+            });
+            const result = await response.json();
+            aiAnswer.innerHTML = formatAIResponse(result.answer || result.error);
+        } catch (error) {
+            aiAnswer.textContent = `Error: ${error.message}`;
+        } finally {
+            loader.style.display = 'none';
+        }
+    });
+
+    learningType.addEventListener('change', () => {
+        const supervisedModels = ["Logistic Regression", "Naive Bayes", "Decision Tree", "Random Forest", "SVM", "KNN", "XGBoost", "CatBoost", "Linear Regression"];
+        const unsupervisedModels = ["KMeans", "DBSCAN", "PCA"];
+        const models = learningType.value === 'Supervised' ? supervisedModels : unsupervisedModels;
+        
+        modelDropdown.innerHTML = '';
+        models.forEach(model => {
+            const option = document.createElement('option');
+            option.value = model;
+            option.textContent = model;
+            modelDropdown.appendChild(option);
+        });
+    });
+
+    learningType.dispatchEvent(new Event('change'));
+
+    // Add click-to-copy functionality to output boxes
+    [trainOutput, aiAnswer, uploadStatus, plotError].forEach(el => {
+        el.addEventListener('click', () => {
+            const textToCopy = el.textContent;
+            navigator.clipboard.writeText(textToCopy).then(() => {
+                const originalText = el.textContent;
+                el.textContent = 'Copied!';
+                setTimeout(() => {
+                    el.textContent = originalText;
+                }, 1000);
+            });
+        });
+    });
+});

frontend/style.css

@@ -0,0 +1,313 @@
+:root {
+    --primary-color: #6A5ACD;
+    --secondary-color: #20B2AA;
+    --sidebar-bg: #2c3e50;
+    --text-color-dark: #34495e;
+    --text-color-light: #ecf0f1;
+    --hover-color: #7B68EE;
+}
+
+body {
+    font-family: 'Poppins', -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
+    margin: 0;
+    padding: 0;
+    -webkit-font-smoothing: antialiased;
+    -moz-osx-font-smoothing: grayscale;
+}
+
+.app-layout {
+    display: flex;
+    height: 100vh;
+    overflow: hidden;
+}
+
+.sidebar {
+    width: 260px;
+    background-color: var(--sidebar-bg);
+    height: 100%;
+    position: fixed;
+    top: 0;
+    left: 0;
+    padding: 1.5rem;
+    transition: width 0.3s ease;
+    z-index: 10;
+    box-shadow: 2px 0 10px rgba(0,0,0,0.1);
+}
+
+.sidebar.collapsed {
+    width: 80px;
+}
+
+.sidebar-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 2rem;
+}
+
+.sidebar-header h2 {
+    margin: 0;
+    font-weight: 700;
+    font-size: 1.8rem;
+    color: var(--text-color-light);
+    transition: opacity 0.3s ease;
+}
+
+.sidebar.collapsed .sidebar-header h2 {
+    opacity: 0;
+}
+
+#sidebar-toggle {
+    background: transparent;
+    border: none;
+    color: var(--text-color-light);
+    font-size: 1.2rem;
+    cursor: pointer;
+    transition: transform 0.3s ease;
+}
+
+#sidebar-toggle:hover {
+    color: var(--hover-color);
+}
+
+.sidebar.collapsed #sidebar-toggle {
+    transform: rotate(180deg);
+}
+
+.sidebar-nav {
+    display: flex;
+    flex-direction: column;
+    gap: 1rem;
+}
+
+.sidebar-nav .nav-link {
+    display: flex;
+    align-items: center;
+    gap: 1rem;
+    padding: 0.8rem 1rem;
+    border-radius: 8px;
+    color: var(--text-color-light);
+    font-weight: 500;
+    text-decoration: none;
+    transition: background-color 0.2s, transform 0.2s;
+}
+
+.sidebar-nav .nav-link:hover {
+    background-color: rgba(255, 255, 255, 0.1);
+    transform: translateX(5px);
+}
+
+.sidebar-nav .nav-link.active {
+    background-color: var(--primary-color);
+    font-weight: 700;
+    box-shadow: 0 4px 15px rgba(106, 90, 205, 0.3);
+}
+
+.sidebar-nav .nav-link .nav-text {
+    opacity: 1;
+    transition: opacity 0.3s ease;
+}
+
+.sidebar.collapsed .nav-link .nav-text {
+    opacity: 0;
+    width: 0;
+    overflow: hidden;
+}
+
+.sidebar.collapsed .nav-link {
+    justify-content: center;
+}
+
+.main-content {
+    margin-left: 260px;
+    width: calc(100% - 260px);
+    height: 100%;
+    transition: margin-left 0.3s ease, width 0.3s ease;
+    background-image: url('BGround.png');
+    background-size: cover;
+    background-position: center;
+    position: relative;
+    overflow-y: auto;
+}
+
+.main-content.collapsed {
+    margin-left: 80px;
+    width: calc(100% - 80px);
+}
+
+.container {
+    width: 100%;
+    min-height: 100%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    padding: 2.5rem;
+    box-sizing: border-box;
+}
+
+.page {
+    display: none;
+    width: 100%;
+    max-width: 900px;
+}
+
+.page.active {
+    display: block;
+    animation: fadeIn 0.5s;
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(10px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+.card {
+    background: rgba(0, 0, 0, 0.3);
+    border-radius: 16px;
+    box-shadow: 0 4px 30px rgba(0, 0, 0, 0.2);
+    backdrop-filter: blur(10px);
+    -webkit-backdrop-filter: blur(10px);
+    border: 1px solid rgba(255, 255, 255, 0.2);
+    padding: 2.5rem;
+    color: var(--text-color-light);
+    margin-bottom: 2rem;
+}
+
+.card h2 {
+    color: var(--text-color-light);
+    font-weight: 700;
+    margin-bottom: 1.5rem;
+    text-align: center;
+}
+
+.card p, .card label {
+    color: var(--text-color-light);
+    font-weight: 500;
+    margin-bottom: 0.5rem;
+    display: block;
+}
+
+.file-upload-wrapper {
+    position: relative;
+    display: inline-block;
+    width: 100%;
+    margin-bottom: 1rem;
+}
+
+#csv-upload {
+    display: none;
+}
+
+.file-upload-wrapper label {
+    display: block;
+    padding: 1rem;
+    background: rgba(255, 255, 255, 0.2);
+    border: 2px dashed rgba(255, 255, 255, 0.5);
+    border-radius: 8px;
+    text-align: center;
+    cursor: pointer;
+    transition: background-color 0.2s, border-color 0.2s;
+}
+
+.file-upload-wrapper label:hover {
+    background: rgba(255, 255, 255, 0.3);
+    border-color: var(--primary-color);
+}
+
+select, input[type="text"], button {
+    width: 100%;
+    padding: 1rem;
+    margin-bottom: 1rem;
+    border-radius: 8px;
+    background: rgba(255, 255, 255, 0.1);
+    border: 1px solid rgba(255, 255, 255, 0.2);
+    color: var(--text-color-light);
+    font-size: 1rem;
+    box-sizing: border-box;
+}
+
+select option {
+    background: #fff;
+    color: var(--text-color-dark);
+}
+
+select:focus, input:focus {
+    background: rgba(255, 255, 255, 0.2);
+    box-shadow: 0 0 0 3px rgba(106, 90, 205, 0.3);
+    outline: none;
+}
+
+button {
+    background-color: var(--primary-color);
+    cursor: pointer;
+    transition: background-color 0.2s, transform 0.2s;
+}
+
+button:hover {
+    background-color: var(--hover-color);
+    transform: translateY(-3px);
+    box-shadow: 0 6px 20px rgba(106, 90, 205, 0.4);
+}
+
+#plot-output {
+    background: rgba(0, 0, 0, 0.2);
+    border: 1px solid rgba(255, 255, 255, 0.2);
+    border-radius: 8px;
+    padding: 1rem;
+    margin-top: 1rem;
+    min-height: 200px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+#plot-img {
+    max-width: 100%;
+    max-height: 100%;
+    border-radius: 8px;
+}
+
+#train-output, #ai-answer, #upload-status, #plot-error {
+    background-color: rgba(0, 0, 0, 0.2);
+    border: 1px solid rgba(255, 255, 255, 0.2);
+    color: var(--text-color-light);
+    padding: 1rem;
+    border-radius: 8px;
+    margin-top: 1rem;
+    min-height: 50px;
+    white-space: pre-wrap;
+    word-wrap: break-word;
+    cursor: pointer;
+}
+
+.loader {
+    border: 4px solid #f3f3f3;
+    border-top: 4px solid var(--primary-color);
+    border-radius: 50%;
+    width: 40px;
+    height: 40px;
+    animation: spin 1s linear infinite;
+    display: none;
+}
+
+@keyframes spin {
+    0% { transform: rotate(0deg); }
+    100% { transform: rotate(360deg); }
+}
+
+.nav-text {
+    animation: fadeInText 0.5s forwards;
+}
+
+@keyframes fadeInText {
+    from {
+        opacity: 0;
+    }
+    to {
+        opacity: 1;
+    }
+}
+
+.sidebar.collapsed .nav-text {
+    animation: none;
+}

models/supervised.py

@@ -0,0 +1,104 @@
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.naive_bayes import GaussianNB
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.svm import SVC, SVR
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier, XGBRegressor
+from catboost import CatBoostClassifier
+from utils.metrics import classification_metrics, regression_metrics
+from utils.data_cleaner import prepare_data
+import pandas as pd
+import logging
+
+# Configure logging for this module
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+def train_model(df, target_column, model_name):
+    """Trains a supervised machine learning model based on the specified model name.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame containing features and target.
+        target_column (str): The name of the target column.
+        model_name (str): The name of the model to train (e.g., "Logistic Regression", "Random Forest").
+
+    Returns:
+        tuple: A tuple containing:
+            - model: The trained model object.
+            - metrics (dict): A dictionary of evaluation metrics.
+            - y_test (pd.Series): Actual target values from the test set.
+            - y_pred (np.array): Predicted target values for the test set.
+            - y_pred_proba (np.array, optional): Predicted probabilities for classification tasks.
+            - X_test (pd.DataFrame): Feature values from the test set.
+            - error (str, optional): An error message if training fails.
+    """
+    try:
+        # Prepare data: clean, encode, scale, and split into features (X) and target (y)
+        X, y, label_encoders, is_classification = prepare_data(df, target_column)
+
+        # Split data into training and testing sets
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        model = None
+        # Initialize the selected model
+        if model_name == "Logistic Regression":
+            if not is_classification:
+                return None, "Logistic Regression is for classification tasks.", None, None, None, None
+            model = LogisticRegression(max_iter=1000) # Increased max_iter for convergence
+        elif model_name == "Naive Bayes":
+            if not is_classification:
+                return None, "Naive Bayes is for classification tasks.", None, None, None, None
+            model = GaussianNB()
+        elif model_name == "Decision Tree":
+            model = DecisionTreeClassifier(random_state=42)
+        elif model_name == "Random Forest":
+            if is_classification:
+                model = RandomForestClassifier(random_state=42)
+            else:
+                model = RandomForestRegressor(random_state=42)
+        elif model_name == "SVM":
+            if is_classification:
+                model = SVC(probability=True, random_state=42) # probability=True for ROC curve
+            else:
+                model = SVR()
+        elif model_name == "KNN":
+            if not is_classification:
+                return None, "KNN is for classification tasks.", None, None, None, None
+            model = KNeighborsClassifier()
+        elif model_name == "XGBoost":
+            if is_classification:
+                model = XGBClassifier(eval_metric='mlogloss', use_label_encoder=False, random_state=42)
+            else:
+                model = XGBRegressor(random_state=42)
+        elif model_name == "CatBoost":
+            if not is_classification:
+                return None, "CatBoost is for classification tasks.", None, None, None, None
+            model = CatBoostClassifier(verbose=0, random_state=42) # verbose=0 to suppress output
+        elif model_name == "Linear Regression":
+            if is_classification:
+                return None, "Linear Regression is for regression tasks.", None, None, None, None
+            model = LinearRegression()
+        else:
+            return None, "Model not found.", None, None, None, None
+
+        # Train the model
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_test)
+        y_pred_proba = None
+
+        # Get prediction probabilities for classification models (needed for ROC curve)
+        if is_classification and hasattr(model, 'predict_proba'):
+            y_pred_proba = model.predict_proba(X_test)
+
+        # Calculate evaluation metrics
+        if is_classification:
+            metrics = classification_metrics(y_test, y_pred)
+        else:
+            metrics = regression_metrics(y_test, y_pred)
+
+        logging.info(f"Successfully trained {model_name} model.")
+        return model, metrics, y_test, y_pred, y_pred_proba, X_test
+    except Exception as e:
+        logging.error(f"An error occurred during model training for {model_name}: {e}", exc_info=True)
+        return None, f"An error occurred during model training: {e}", None, None, None, None

models/unsupervised.py

@@ -0,0 +1,64 @@
+from sklearn.cluster import KMeans, DBSCAN
+from sklearn.decomposition import PCA
+from utils.data_cleaner import prepare_data
+import pandas as pd
+import logging
+
+# Configure logging for this module
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+def train_unsupervised(df, model_name, n_clusters=3, eps=0.5, min_samples=5, n_components=2):
+    """Trains an unsupervised machine learning model based on the specified model name.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame for unsupervised learning.
+        model_name (str): The name of the unsupervised model to train (e.g., "KMeans", "DBSCAN", "PCA").
+        n_clusters (int, optional): Number of clusters for KMeans. Defaults to 3.
+        eps (float, optional): The maximum distance between two samples for one to be considered as in the neighborhood of the other for DBSCAN. Defaults to 0.5.
+        min_samples (int, optional): The number of samples (or total weight) in a neighborhood for a point to be considered as a core point for DBSCAN. Defaults to 5.
+        n_components (int, optional): Number of components to keep for PCA. Defaults to 2.
+
+    Returns:
+        tuple: A tuple containing:
+            - fitted_model: The trained unsupervised model object.
+            - result: The clustering labels or transformed data.
+            - error (str, optional): An error message if training fails.
+    """
+    try:
+        # Prepare data for unsupervised learning (cleaning and scaling)
+        df_prepared, _ = prepare_data(df)
+        
+        if df_prepared.empty:
+            logging.warning("Prepared DataFrame is empty for unsupervised training.")
+            return None, "Prepared data is empty."
+
+        model = None
+        # Initialize the selected unsupervised model
+        if model_name == "KMeans":
+            model = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
+        elif model_name == "DBSCAN":
+            model = DBSCAN(eps=eps, min_samples=min_samples)
+        elif model_name == "PCA":
+            model = PCA(n_components=n_components)
+        else:
+            logging.warning(f"Unsupervised model not supported: {model_name}")
+            return None, "Model not supported."
+
+        # Fit the model to the prepared data
+        fitted_model = model.fit(df_prepared)
+
+        result = None
+        # Extract results based on the model type
+        if hasattr(fitted_model, 'labels_'):
+            result = fitted_model.labels_
+            logging.info(f"KMeans/DBSCAN trained. Clusters/labels generated.")
+        elif hasattr(fitted_model, 'components_'):
+            result = fitted_model.transform(df_prepared)
+            logging.info(f"PCA trained. Data transformed to {n_components} components.")
+        else:
+            logging.info(f"Unsupervised model {model_name} trained, but no specific labels or components found.")
+
+        return fitted_model, result
+    except Exception as e:
+        logging.error(f"An error occurred during unsupervised model training for {model_name}: {e}", exc_info=True)
+        return None, f"An error occurred during model training: {e}"

models/xgboost_model.py

@@ -0,0 +1,37 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from xgboost import XGBClassifier, XGBRegressor
+from utils.metrics import classification_metrics, regression_metrics
+
+def encode_dataframe(df):
+    label_encoders = {}
+    for col in df.select_dtypes(include='object'):
+        le = LabelEncoder()
+        df[col] = le.fit_transform(df[col])
+        label_encoders[col] = le
+    return df, label_encoders
+
+def train_xgboost(df, target_column, task='classification'):
+    df = df.dropna()
+    df, encoders = encode_dataframe(df)
+
+    X = df.drop(columns=[target_column])
+    y = df[target_column]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+    if task == 'classification':
+        model = XGBClassifier()
+    else:
+        model = XGBRegressor()
+
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+
+    if task == 'classification':
+        metrics = classification_metrics(y_test, y_pred)
+    else:
+        metrics = regression_metrics(y_test, y_pred)
+
+    return model, metrics

rag/memory.py

@@ -0,0 +1,104 @@
+import pandas as pd
+import os
+import pickle
+import logging
+
+# Configure logging for this module
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+CACHE_DIR = ".cache"
+# Create the cache directory if it doesn't exist
+if not os.path.exists(CACHE_DIR):
+    os.makedirs(CACHE_DIR)
+    logging.info(f"Created cache directory: {CACHE_DIR}")
+
+def get_dataset_path(name):
+    """Constructs the file path for a dataset pickle file."""
+    return os.path.join(CACHE_DIR, f"{name}.pkl")
+
+def get_model_results_path(name):
+    """Constructs the file path for model results pickle file."""
+    return os.path.join(CACHE_DIR, f"{name}_model_results.pkl")
+
+def store_dataset(name, dataframe):
+    """Saves a DataFrame to a pickle file in the cache directory.
+
+    Args:
+        name (str): The name to associate with the dataset (used for filename).
+        dataframe (pd.DataFrame): The DataFrame to be stored.
+    """
+    path = get_dataset_path(name)
+    try:
+        dataframe.to_pickle(path)
+        logging.info(f"Dataset '{name}' stored successfully at {path}")
+    except Exception as e:
+        logging.error(f"Error storing dataset '{name}' to {path}: {e}", exc_info=True)
+    
+def get_dataset(name):
+    """Loads a DataFrame from a pickle file in the cache directory.
+
+    Args:
+        name (str): The name of the dataset to retrieve.
+
+    Returns:
+        pd.DataFrame or None: The loaded DataFrame if found, otherwise None.
+    """
+    path = get_dataset_path(name)
+    if os.path.exists(path):
+        try:
+            df = pd.read_pickle(path)
+            logging.info(f"Dataset '{name}' loaded successfully from {path}")
+            return df
+        except Exception as e:
+            logging.error(f"Error loading dataset '{name}' from {path}: {e}", exc_info=True)
+            return None
+    logging.info(f"Dataset '{name}' not found at {path}")
+    return None
+
+def store_model_results(name, model, y_test, y_pred, y_pred_proba, X_test):
+    """Saves trained model, test data, predictions, and probabilities to a pickle file.
+
+    Args:
+        name (str): The name to associate with the model results.
+        model: The trained model object.
+        y_test (pd.Series): Actual target values from the test set.
+        y_pred (np.array): Predicted target values for the test set.
+        y_pred_proba (np.array, optional): Predicted probabilities for classification tasks.
+        X_test (pd.DataFrame): Feature values from the test set.
+    """
+    path = get_model_results_path(name)
+    results = {
+        "model": model,
+        "y_test": y_test,
+        "y_pred": y_pred,
+        "y_pred_proba": y_pred_proba,
+        "X_test": X_test
+    }
+    try:
+        with open(path, 'wb') as f:
+            pickle.dump(results, f)
+        logging.info(f"Model results for '{name}' stored successfully at {path}")
+    except Exception as e:
+        logging.error(f"Error storing model results for '{name}' to {path}: {e}", exc_info=True)
+
+def get_model_results(name):
+    """Loads trained model, test data, predictions, and probabilities from a pickle file.
+
+    Args:
+        name (str): The name of the model results to retrieve.
+
+    Returns:
+        dict or None: A dictionary containing model results if found, otherwise None.
+    """
+    path = get_model_results_path(name)
+    if os.path.exists(path):
+        try:
+            with open(path, 'rb') as f:
+                results = pickle.load(f)
+            logging.info(f"Model results for '{name}' loaded successfully from {path}")
+            return results
+        except Exception as e:
+            logging.error(f"Error loading model results for '{name}' from {path}: {e}", exc_info=True)
+            return None
+    logging.info(f"Model results for '{name}' not found at {path}")
+    return None

rag/rag_query.py

@@ -0,0 +1,72 @@
+"""Module for querying the Groq API with dataset context."""
+
+from groq import Groq, APIStatusError
+from config import GROQ_API_KEY
+from rag.memory import get_dataset
+import pandas as pd
+import logging
+
+# Configure logging for this module
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Initialize Groq client with API key from config
+client = Groq(api_key=GROQ_API_KEY)
+
+def query_dataset_with_groq(dataset_name, user_query):
+    """Queries the Groq API with a user question, providing dataset context.
+
+    Args:
+        dataset_name (str): The name of the dataset to retrieve from memory.
+        user_query (str): The user's question about the dataset.
+
+    Returns:
+        str: The AI's answer to the question, or an error message if the query fails.
+    """
+    logging.info(f"Attempting to query Groq with user question: {user_query}")
+    df = get_dataset(dataset_name)
+    if df is None:
+        logging.error(f"Dataset '{dataset_name}' not found in memory for Groq query.")
+        return "No dataset found with that name. Please upload a dataset first."
+
+    # Prepare context for the LLM, including dataset overview, summary statistics, and a sample
+    context = f"""
+You are an expert Data Analyst. You have been provided with a dataset.
+
+**Dataset Overview:**
+- **Shape:** {df.shape[0]} rows and {df.shape[1]} columns.
+- **Columns and Data Types:**\n{df.dtypes.to_string()}
+
+**Summary Statistics:**\n{df.describe(include='all').to_string()}
+
+**First 5 Rows:**\n{df.head(5).to_string(index=False)}
+
+**User Question:** {user_query}
+
+Answer the user's question clearly and accurately based *only* on the provided dataset information.
+"""
+
+    try:
+        logging.info("Sending request to Groq API for chat completion.")
+        response = client.chat.completions.create(
+            model="llama3-70b-8192", # Using a powerful model for better understanding
+            messages=[
+                {"role": "system", "content": "You are a helpful data science assistant. Provide concise and accurate answers."},
+                {"role": "user", "content": context}
+            ],
+            temperature=0.1, # Low temperature for factual and less creative responses
+            max_tokens=1024, # Limit response length
+            top_p=1,
+            stop=None,
+        )
+        ai_response_content = response.choices[0].message.content
+        logging.info("Successfully received response from Groq API.")
+        return ai_response_content
+    except APIStatusError as e:
+        logging.error(f"Groq API error occurred: Status Code {e.status_code}, Response: {e.response}", exc_info=True)
+        if e.status_code == 503:
+            return "The AI service is currently unavailable due to high demand or maintenance. Please try again later."
+        else:
+            return f"An error occurred with the AI service (Status: {e.status_code}). Please check the logs for more details."
+    except Exception as e:
+        logging.error(f"An unexpected error occurred while querying the AI: {e}", exc_info=True)
+        return f"An unexpected error occurred while processing your request: {e}"

requirements.txt

@@ -0,0 +1,16 @@
+pandas
+numpy
+scikit-learn
+matplotlib
+seaborn
+plotly
+xgboost
+catboost
+langgraph
+python-dotenv
+faiss-cpu
+tiktoken
+groq
+flask
+scipy
+gunicorn

utils/data_cleaner.py

@@ -0,0 +1,101 @@
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from sklearn.impute import SimpleImputer
+import logging
+
+# Configure logging for this module
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+def clean_data(df):
+    """Cleans the input DataFrame by imputing missing values.
+
+    - Numerical columns: Imputes missing values with the mean.
+    - Categorical columns: Imputes missing values with the most frequent value.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame to clean.
+
+    Returns:
+        pd.DataFrame: The DataFrame with missing values imputed.
+    """
+    logging.info("Starting data cleaning process.")
+    # Impute missing values for numerical columns
+    numerical_cols = df.select_dtypes(include=['number']).columns
+    if not numerical_cols.empty:
+        logging.info(f"Imputing missing numerical values for columns: {list(numerical_cols)}")
+        imputer_numerical = SimpleImputer(strategy='mean')
+        df[numerical_cols] = imputer_numerical.fit_transform(df[numerical_cols])
+
+    # Impute missing values for categorical columns
+    categorical_cols = df.select_dtypes(include=['object', 'category']).columns
+    if not categorical_cols.empty:
+        logging.info(f"Imputing missing categorical values for columns: {list(categorical_cols)}")
+        imputer_categorical = SimpleImputer(strategy='most_frequent')
+        df[categorical_cols] = imputer_categorical.fit_transform(df[categorical_cols])
+    
+    logging.info("Data cleaning process completed.")
+    return df
+
+def prepare_data(df, target_column=None):
+    """Prepares the DataFrame for machine learning by cleaning, encoding, and scaling.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame.
+        target_column (str, optional): The name of the target column. If provided,
+                                       data is prepared for supervised learning (X, y split).
+                                       Otherwise, for unsupervised learning (all features).
+
+    Returns:
+        tuple: If target_column is provided:
+                   (X (pd.DataFrame), y (pd.Series), label_encoders (dict), is_classification (bool))
+               If target_column is None:
+                   (df_prepared (pd.DataFrame), label_encoders (dict))
+    """
+    logging.info(f"Starting data preparation process. Target column: {target_column}")
+    df = clean_data(df.copy()) # Ensure we work on a copy to avoid modifying original df
+    label_encoders = {}
+    is_classification = False
+
+    # Encode categorical features (excluding the target column if it's categorical)
+    for col in df.select_dtypes(include=['object', 'category']).columns:
+        if col != target_column:
+            logging.info(f"Encoding categorical feature: {col}")
+            le = LabelEncoder()
+            df[col] = le.fit_transform(df[col])
+            label_encoders[col] = le
+
+    if target_column:
+        # Supervised learning preparation
+        logging.info(f"Preparing data for supervised learning with target: {target_column}")
+        # Determine if it's a classification or regression task based on target column properties
+        if df[target_column].dtype == 'object' or df[target_column].nunique() <= 10: # Heuristic for classification
+            is_classification = True
+            logging.info(f"Target column '{target_column}' identified as classification.")
+            le = LabelEncoder()
+            df[target_column] = le.fit_transform(df[target_column])
+            label_encoders[target_column] = le
+        else:
+            logging.info(f"Target column '{target_column}' identified as regression.")
+        
+        X = df.drop(columns=[target_column])
+        y = df[target_column]
+        
+        # Scale numerical features in X
+        numerical_cols = X.select_dtypes(include=['number']).columns
+        if not numerical_cols.empty:
+            logging.info(f"Scaling numerical features in X: {list(numerical_cols)}")
+            scaler = StandardScaler()
+            X[numerical_cols] = scaler.fit_transform(X[numerical_cols])
+            
+        logging.info("Data preparation for supervised learning completed.")
+        return X, y, label_encoders, is_classification
+    else:
+        # Unsupervised learning preparation (scale all numerical features)
+        logging.info("Preparing data for unsupervised learning.")
+        numerical_cols = df.select_dtypes(include=['number']).columns
+        if not numerical_cols.empty:
+            logging.info(f"Scaling numerical features for unsupervised learning: {list(numerical_cols)}")
+            scaler = StandardScaler()
+            df[numerical_cols] = scaler.fit_transform(df[numerical_cols])
+        logging.info("Data preparation for unsupervised learning completed.")
+        return df.copy(), label_encoders

utils/export.py

@@ -0,0 +1,12 @@
+import pickle
+import os
+
+def save_model(model, path='model.pkl'):
+    with open(path, 'wb') as f:
+        pickle.dump(model, f)
+
+def load_model(path='model.pkl'):
+    if os.path.exists(path):
+        with open(path, 'rb') as f:
+            return pickle.load(f)
+    return None

utils/metrics.py

@@ -0,0 +1,56 @@
+"""Module for calculating and returning various machine learning evaluation metrics."""
+
+from sklearn.metrics import accuracy_score, r2_score, confusion_matrix, mean_squared_error, precision_score, recall_score, f1_score
+import numpy as np
+import logging
+
+# Configure logging for this module
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+def classification_metrics(y_true, y_pred):
+    """Calculates common classification metrics.
+
+    Args:
+        y_true (array-like): True labels.
+        y_pred (array-like): Predicted labels.
+
+    Returns:
+        dict: A dictionary containing accuracy, precision, recall, F1-score, and confusion matrix.
+    """
+    logging.info("Calculating classification metrics.")
+    try:
+        metrics = {
+            "Accuracy": accuracy_score(y_true, y_pred),
+            "Precision": precision_score(y_true, y_pred, average='weighted', zero_division=0),
+            "Recall": recall_score(y_true, y_pred, average='weighted', zero_division=0),
+            "F1 Score": f1_score(y_true, y_pred, average='weighted', zero_division=0),
+            "Confusion Matrix": confusion_matrix(y_true, y_pred).tolist()
+        }
+        logging.info("Classification metrics calculated successfully.")
+        return metrics
+    except Exception as e:
+        logging.error(f"Error calculating classification metrics: {e}", exc_info=True)
+        return {"error": f"Failed to calculate classification metrics: {e}"}
+
+def regression_metrics(y_true, y_pred):
+    """Calculates common regression metrics.
+
+    Args:
+        y_true (array-like): True target values.
+        y_pred (array-like): Predicted target values.
+
+    Returns:
+        dict: A dictionary containing R2 score, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
+    """
+    logging.info("Calculating regression metrics.")
+    try:
+        metrics = {
+            "R2 Score": r2_score(y_true, y_pred),
+            "MSE": mean_squared_error(y_true, y_pred),
+            "RMSE": np.sqrt(mean_squared_error(y_true, y_pred))
+        }
+        logging.info("Regression metrics calculated successfully.")
+        return metrics
+    except Exception as e:
+        logging.error(f"Error calculating regression metrics: {e}", exc_info=True)
+        return {"error": f"Failed to calculate regression metrics: {e}"}

visuals/charts.py

@@ -0,0 +1,579 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+from scipy import stats
+import pandas as pd
+from sklearn.metrics import confusion_matrix, roc_curve, auc
+from sklearn.cluster import KMeans
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+from scipy.cluster.hierarchy import dendrogram, linkage
+import logging
+
+# Configure logging for this module
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Consistent theme settings for plots
+FIG_SIZE = (10, 6)
+TITLE_FONT_SIZE = 14
+LABEL_FONT_SIZE = 12
+LEGEND_FONT_SIZE = 10
+PRIMARY_COLOR = "#4C72B0"  # A nice blue
+SECONDARY_COLOR = "#55A868" # A nice green
+
+def plot_histogram(df, col):
+    """Generates a histogram for a given numeric column.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame.
+        col (str): The name of the numeric column to plot.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info(f"Generating histogram for column: {col}")
+    if col not in df.columns:
+        logging.error(f"Column '{col}' not found for histogram.")
+        return None, f"Column '{col}' not found."
+    if not pd.api.types.is_numeric_dtype(df[col]):
+        logging.error(f"Column '{col}' is not numeric for histogram.")
+        return None, "Histogram is only for numeric columns."
+    
+    plt.figure(figsize=FIG_SIZE)
+    sns.set_style("whitegrid")
+    
+    # Calculate optimal bin width using Freedman-Diaconis rule
+    try:
+        iqr = df[col].quantile(0.75) - df[col].quantile(0.25)
+        if iqr > 0:
+            bin_width = 2 * iqr / (len(df[col]) ** (1/3))
+            bins = int((df[col].max() - df[col].min()) / bin_width) if bin_width > 0 else 25
+        else:
+            bins = 25 # Default if IQR is zero
+    except Exception as e:
+        logging.warning(f"Could not calculate optimal bins for {col}: {e}. Using default 25 bins.")
+        bins = 25
+    
+    ax = sns.histplot(df[col], kde=True, bins=bins, color=PRIMARY_COLOR, edgecolor='black', line_kws={'linewidth': 2, 'linestyle': '--'})
+    
+    # Add mean and median lines
+    mean_val = df[col].mean()
+    median_val = df[col].median()
+    ax.axvline(mean_val, color='red', linestyle='--', linewidth=2, label=f'Mean: {mean_val:.2f}')
+    ax.axvline(median_val, color='green', linestyle='-', linewidth=2, label=f'Median: {median_val:.2f}')
+    
+    skewness = df[col].skew()
+    plt.title(f'Distribution of {col} (Skewness: {skewness:.2f})', fontsize=TITLE_FONT_SIZE, weight='bold')
+    plt.xlabel(col, fontsize=LABEL_FONT_SIZE)
+    plt.ylabel('Density', fontsize=LABEL_FONT_SIZE)
+    plt.legend(fontsize=LEGEND_FONT_SIZE)
+    plt.tight_layout()
+    logging.info(f"Histogram for {col} generated successfully.")
+    return plt.gcf(), None
+
+def plot_bar(df, col):
+    """Generates a bar plot for a given categorical or discrete numeric column.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame.
+        col (str): The name of the column to plot.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info(f"Generating bar plot for column: {col}")
+    if col not in df.columns:
+        logging.error(f"Column '{col}' not found for bar plot.")
+        return None, f"Column '{col}' not found."
+    
+    plt.figure(figsize=FIG_SIZE)
+    sns.set_style("whitegrid")
+    
+    counts = df[col].value_counts()
+    # Handle too many categories by showing top N and grouping others
+    if len(counts) > 15:
+        logging.info(f"Column {col} has too many unique values ({len(counts)}). Showing top 14 and grouping others.")
+        top_14 = counts.nlargest(14)
+        other_sum = counts.nsmallest(len(counts) - 14).sum()
+        top_14['Other'] = other_sum
+        counts = top_14
+
+    ax = sns.barplot(y=counts.index.astype(str), x=counts.values, palette="viridis", orient='h')
+    
+    # Add count labels to bars
+    for i, v in enumerate(counts.values):
+        ax.text(v + 1, i, str(v), color='black', va='center', fontsize=10)
+        
+    plt.title(f'Frequency of {col}', fontsize=TITLE_FONT_SIZE, weight='bold')
+    plt.xlabel('Count', fontsize=LABEL_FONT_SIZE)
+    plt.ylabel(col, fontsize=LABEL_FONT_SIZE)
+    plt.tight_layout()
+    logging.info(f"Bar plot for {col} generated successfully.")
+    return plt.gcf(), None
+
+def plot_scatter(df, col1, col2, color_col=None):
+    """Generates a scatter plot between two numeric columns, with optional coloring.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame.
+        col1 (str): The name of the first numeric column (x-axis).
+        col2 (str): The name of the second numeric column (y-axis).
+        color_col (str, optional): The name of a column to use for coloring points.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info(f"Generating scatter plot for {col1} vs {col2}, colored by {color_col or 'None'}")
+    if col1 not in df.columns or col2 not in df.columns:
+        logging.error(f"One or both columns ({col1}, {col2}) not found for scatter plot.")
+        return None, "One or both columns not found."
+    if not pd.api.types.is_numeric_dtype(df[col1]) or not pd.api.types.is_numeric_dtype(df[col2]):
+        logging.error(f"Columns {col1} or {col2} are not numeric for scatter plot.")
+        return None, "Scatter plots are only available for numeric columns."
+    if color_col and color_col != 'None' and color_col not in df.columns:
+        logging.error(f"Color column '{color_col}' not found for scatter plot.")
+        return None, f"Color column '{color_col}' not found."
+    
+    try:
+        plt.figure(figsize=FIG_SIZE)
+        sns.set_style("whitegrid")
+        hue = color_col if color_col and color_col != 'None' else None
+        
+        plot_df = df.dropna(subset=[col1, col2]) # Drop NaNs for plotting
+
+        sns.scatterplot(data=plot_df, x=col1, y=col2, hue=hue, palette="coolwarm", s=50, alpha=0.6)
+        
+        # Add a linear regression trend line if both columns are numeric
+        if pd.api.types.is_numeric_dtype(df[col1]) and pd.api.types.is_numeric_dtype(df[col2]):
+            # Ensure there's enough data for linear regression
+            if len(plot_df) > 1:
+                m, b, r_value, _, _ = stats.linregress(plot_df[col1], plot_df[col2])
+                x_line = np.array([plot_df[col1].min(), plot_df[col1].max()])
+                y_line = m * x_line + b
+                plt.plot(x_line, y_line, color='red', linestyle='--', label=f'Trend Line (R² = {r_value**2:.2f})')
+                plt.legend(fontsize=LEGEND_FONT_SIZE)
+            else:
+                logging.warning("Not enough data points for linear regression trend line.")
+
+        plt.title(f'{col1} vs. {col2}', fontsize=TITLE_FONT_SIZE, weight='bold')
+        plt.xlabel(col1, fontsize=LABEL_FONT_SIZE)
+        plt.ylabel(col2, fontsize=LABEL_FONT_SIZE)
+        plt.tight_layout()
+        logging.info(f"Scatter plot for {col1} vs {col2} generated successfully.")
+        return plt.gcf(), None
+    except Exception as e:
+        logging.error(f"An error occurred during scatter plot generation: {e}", exc_info=True)
+        return None, f"An error occurred during plot generation: {e}"
+
+def plot_box(df, continuous_var, group_var):
+    """Generates a box plot to show the distribution of a continuous variable across categories of a grouping variable.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame.
+        continuous_var (str): The name of the continuous numeric column.
+        group_var (str): The name of the categorical or discrete column for grouping.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info(f"Generating box plot for {continuous_var} by {group_var}")
+    if continuous_var not in df.columns or group_var not in df.columns:
+        logging.error(f"One or both columns ({continuous_var}, {group_var}) not found for box plot.")
+        return None, "One or both columns not found."
+    if not pd.api.types.is_numeric_dtype(df[continuous_var]):
+        logging.error(f"Column '{continuous_var}' is not numeric for box plot.")
+        return None, "Box plots require a numeric column for the x-axis."
+    
+    plt.figure(figsize=FIG_SIZE)
+    sns.set_style("whitegrid")
+    
+    # Order categories by median of the continuous variable
+    order = df.groupby(group_var)[continuous_var].median().sort_values(ascending=False).index
+    sns.boxplot(data=df, x=continuous_var, y=group_var, palette="Set2", order=order, orient='h')
+    
+    plt.title(f'{continuous_var} by {group_var}', fontsize=TITLE_FONT_SIZE, weight='bold')
+    plt.xlabel(continuous_var, fontsize=LABEL_FONT_SIZE)
+    plt.ylabel(group_var, fontsize=LABEL_FONT_SIZE)
+    plt.tight_layout()
+    logging.info(f"Box plot for {continuous_var} by {group_var} generated successfully.")
+    return plt.gcf(), None
+
+def plot_pie(df, col):
+    """Generates a pie chart for a given categorical column.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame.
+        col (str): The name of the categorical column to plot.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info(f"Generating pie chart for column: {col}")
+    if col not in df.columns:
+        logging.error(f"Column '{col}' not found for pie chart.")
+        return None, f"Column '{col}' not found."
+    
+    counts = df[col].value_counts()
+    # Handle too many categories by showing top N and grouping others
+    if len(counts) > 7:
+        logging.info(f"Column {col} has too many unique values ({len(counts)}). Showing top 6 and grouping others.")
+        top_6 = counts.nlargest(6)
+        other_sum = counts.nsmallest(len(counts) - 6).sum()
+        top_6['Other'] = other_sum
+        counts = top_6
+
+    plt.figure(figsize=(8, 8)) # Pie charts often look better square
+    
+    explode = [0.03] * len(counts) # Slightly separate slices for better visual
+    colors = sns.color_palette('pastel')[0:len(counts)]
+    
+    plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90, explode=explode, colors=colors, pctdistance=0.85)
+    centre_circle = plt.Circle((0,0),0.70,fc='white') # Donut chart effect
+    fig = plt.gcf()
+    fig.gca().add_artist(centre_circle)
+
+    plt.title(f'Distribution of {col}', fontsize=TITLE_FONT_SIZE, weight='bold')
+    plt.tight_layout()
+    logging.info(f"Pie chart for {col} generated successfully.")
+    return plt.gcf(), None
+
+def plot_heatmap(df):
+    """Generates a correlation heatmap for all numeric columns in the DataFrame.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info("Generating correlation heatmap.")
+    numeric_df = df.select_dtypes(include=np.number)
+    if numeric_df.shape[1] < 2:
+        logging.error("Not enough numeric columns for a heatmap.")
+        return None, "Not enough numeric columns for a heatmap."
+    
+    plt.figure(figsize=(12, 10))
+    corr = numeric_df.corr()
+    
+    # Generate a mask for the upper triangle
+    mask = np.triu(np.ones_like(corr, dtype=bool))
+    
+    sns.heatmap(corr, mask=mask, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5, vmin=-1, vmax=1, annot_kws={"size": 8})
+    plt.title('Correlation Heatmap', fontsize=TITLE_FONT_SIZE, weight='bold')
+    plt.xticks(rotation=45, ha='right', fontsize=LABEL_FONT_SIZE)
+    plt.yticks(rotation=0, fontsize=LABEL_FONT_SIZE)
+    plt.tight_layout()
+    logging.info("Correlation heatmap generated successfully.")
+    return plt.gcf(), None
+
+def plot_confusion_matrix(y_true, y_pred, class_names):
+    """Generates a confusion matrix plot.
+
+    Args:
+        y_true (array-like): True labels.
+        y_pred (array-like): Predicted labels.
+        class_names (list): List of class names for labels.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info("Generating confusion matrix.")
+    try:
+        cm = confusion_matrix(y_true, y_pred)
+        plt.figure(figsize=(8, 6))
+        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)
+        plt.title('Confusion Matrix', fontsize=TITLE_FONT_SIZE, weight='bold')
+        plt.ylabel('Actual', fontsize=LABEL_FONT_SIZE)
+        plt.xlabel('Predicted', fontsize=LABEL_FONT_SIZE)
+        plt.tight_layout()
+        logging.info("Confusion matrix generated successfully.")
+        return plt.gcf(), None
+    except Exception as e:
+        logging.error(f"Error generating confusion matrix: {e}", exc_info=True)
+        return None, f"Error generating confusion matrix: {e}"
+
+def plot_roc_curve(y_true, y_pred_proba, class_names=None):
+    """Generates a Receiver Operating Characteristic (ROC) curve.
+
+    Args:
+        y_true (array-like): True binary labels.
+        y_pred_proba (array-like): Target scores, probabilities of the positive class.
+        class_names (list, optional): List of class names. Not directly used in plot but good for context.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info("Generating ROC curve.")
+    try:
+        # Handle multi-class or binary probability predictions
+        if y_pred_proba.ndim == 1: # Binary classification, single probability array
+            fpr, tpr, _ = roc_curve(y_true, y_pred_proba)
+        elif y_pred_proba.shape[1] == 2: # Binary classification, two columns of probabilities
+            fpr, tpr, _ = roc_curve(y_true, y_pred_proba[:, 1]) # Assume second column is positive class
+        else: # Multi-class, need to binarize or choose a class
+            # For simplicity, if multi-class, we'll plot ROC for the first class vs. rest
+            # A more robust solution would allow selecting a class or plotting all.
+            logging.warning("Multi-class ROC curve requested. Plotting for first class vs. rest.")
+            # Binarize y_true for the first class
+            y_true_bin = (y_true == sorted(np.unique(y_true))[0]).astype(int)
+            fpr, tpr, _ = roc_curve(y_true_bin, y_pred_proba[:, 0])
+
+        roc_auc = auc(fpr, tpr)
+        plt.figure(figsize=FIG_SIZE)
+        sns.set_style("whitegrid")
+        plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
+        plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+        plt.xlim([0.0, 1.0])
+        plt.ylim([0.0, 1.05])
+        plt.xlabel('False Positive Rate', fontsize=LABEL_FONT_SIZE)
+        plt.ylabel('True Positive Rate', fontsize=LABEL_FONT_SIZE)
+        plt.title('Receiver Operating Characteristic', fontsize=TITLE_FONT_SIZE, weight='bold')
+        plt.legend(loc="lower right", fontsize=LEGEND_FONT_SIZE)
+        plt.tight_layout()
+        logging.info("ROC curve generated successfully.")
+        return plt.gcf(), None
+    except Exception as e:
+        logging.error(f"Error generating ROC curve: {e}", exc_info=True)
+        return None, f"Error generating ROC curve: {e}"
+
+def plot_feature_importance(model, feature_names):
+    """Generates a feature importance bar plot for tree-based models.
+
+    Args:
+        model: A trained model with a 'feature_importances_' attribute.
+        feature_names (list): List of feature names corresponding to the importances.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info("Generating feature importance plot.")
+    if not hasattr(model, 'feature_importances_'):
+        logging.error("Model does not have feature importances attribute.")
+        return None, "Model does not have feature importances."
+    
+    try:
+        importances = model.feature_importances_
+        # Sort features by importance in descending order
+        indices = np.argsort(importances)[::-1]
+        
+        plt.figure(figsize=FIG_SIZE)
+        sns.set_style("whitegrid")
+        
+        # Plot top N features for clarity
+        num_features_to_plot = min(len(feature_names), 20) # Plot top 20 features or fewer if less available
+        
+        plt.title("Feature Importances", fontsize=TITLE_FONT_SIZE, weight='bold')
+        sns.barplot(x=importances[indices[:num_features_to_plot]], y=[feature_names[i] for i in indices[:num_features_to_plot]], palette="viridis")
+        plt.xlabel("Relative Importance", fontsize=LABEL_FONT_SIZE)
+        plt.ylabel("Feature Name", fontsize=LABEL_FONT_SIZE)
+        plt.tight_layout()
+        logging.info("Feature importance plot generated successfully.")
+        return plt.gcf(), None
+    except Exception as e:
+        logging.error(f"Error generating feature importance plot: {e}", exc_info=True)
+        return None, f"Error generating feature importance plot: {e}"
+
+def plot_elbow_curve(X, max_k=10):
+    """Generates an elbow curve to help determine the optimal number of clusters (k) for KMeans.
+
+    Args:
+        X (pd.DataFrame or np.array): The input data for clustering.
+        max_k (int, optional): The maximum number of clusters to test. Defaults to 10.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info(f"Generating elbow curve for max_k={max_k}")
+    inertias = []
+    if not isinstance(X, pd.DataFrame):
+        X = pd.DataFrame(X) # Ensure X is a DataFrame for .dropna()
+    X_cleaned = X.dropna() # Handle NaNs for KMeans
+    
+    if X_cleaned.empty:
+        logging.error("Data is empty after cleaning for Elbow Curve.")
+        return None, "Data is empty after cleaning for Elbow Curve."
+
+    # Ensure max_k is not greater than the number of samples
+    if max_k > len(X_cleaned):
+        logging.warning(f"max_k ({max_k}) is greater than number of samples ({len(X_cleaned)}). Adjusting max_k.")
+        max_k = len(X_cleaned)
+    
+    if max_k < 1:
+        return None, "max_k must be at least 1."
+
+    try:
+        for k in range(1, max_k + 1):
+            kmeans = KMeans(n_clusters=k, random_state=42, n_init=10) # n_init to suppress warning
+            kmeans.fit(X_cleaned)
+            inertias.append(kmeans.inertia_)
+        
+        plt.figure(figsize=FIG_SIZE)
+        sns.set_style("whitegrid")
+        plt.plot(range(1, max_k + 1), inertias, marker='o', linestyle='-', color=PRIMARY_COLOR)
+        plt.xlabel('Number of clusters (k)', fontsize=LABEL_FONT_SIZE)
+        plt.ylabel('Inertia', fontsize=LABEL_FONT_SIZE)
+        plt.title('Elbow Method For Optimal k', fontsize=TITLE_FONT_SIZE, weight='bold')
+        plt.xticks(np.arange(1, max_k + 1, 1)) # Ensure integer ticks
+        plt.tight_layout()
+        logging.info("Elbow curve generated successfully.")
+        return plt.gcf(), None
+    except Exception as e:
+        logging.error(f"Error generating elbow curve: {e}", exc_info=True)
+        return None, f"Error generating elbow curve: {e}"
+
+def plot_cluster_plot(X, labels, title="Cluster Plot"):
+    """Generates a 2D scatter plot of clusters, optionally after dimensionality reduction.
+
+    Args:
+        X (pd.DataFrame or np.array): The input data.
+        labels (array-like, optional): Cluster labels for coloring points. If None, points are not colored.
+        title (str, optional): Title of the plot. Defaults to "Cluster Plot".
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info(f"Generating cluster plot with title: {title}")
+    if not isinstance(X, pd.DataFrame):
+        X = pd.DataFrame(X)
+    
+    # Handle NaNs before dimensionality reduction
+    X_cleaned = X.dropna()
+    if X_cleaned.empty:
+        logging.error("Data is empty after cleaning for Cluster Plot.")
+        return None, "Data is empty after cleaning for Cluster Plot."
+
+    plot_df = X_cleaned.copy()
+    xlabel = 'Feature 1'
+    ylabel = 'Feature 2'
+
+    # Reduce dimensions to 2 if data has more than 2 features
+    if X_cleaned.shape[1] > 2:
+        try:
+            logging.info("Applying PCA for dimensionality reduction to 2 components.")
+            pca = PCA(n_components=2)
+            X_reduced = pca.fit_transform(X_cleaned)
+            plot_df = pd.DataFrame(X_reduced, columns=['PC1', 'PC2'])
+            xlabel = 'Principal Component 1'
+            ylabel = 'Principal Component 2'
+        except Exception as e:
+            logging.error(f"Could not reduce dimensions for cluster plot using PCA: {e}", exc_info=True)
+            return None, f"Could not reduce dimensions for cluster plot: {e}"
+    elif X_cleaned.shape[1] == 1:
+        logging.error("Data must have at least 2 dimensions for a 2D cluster plot.")
+        return None, "Data must have at least 2 dimensions for a 2D cluster plot."
+
+    plt.figure(figsize=FIG_SIZE)
+    sns.set_style("whitegrid")
+    
+    if labels is not None:
+        # Align labels with cleaned data if necessary
+        if isinstance(labels, pd.Series):
+            labels_aligned = labels.loc[X_cleaned.index] if labels.index.equals(X.index) else labels # Simple alignment
+        else:
+            labels_aligned = labels # Assume already aligned or numpy array
+        sns.scatterplot(x=plot_df.iloc[:, 0], y=plot_df.iloc[:, 1], hue=labels_aligned, palette='viridis', s=50, alpha=0.7)
+        plt.legend(title='Cluster', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=LEGEND_FONT_SIZE)
+    else:
+        sns.scatterplot(x=plot_df.iloc[:, 0], y=plot_df.iloc[:, 1], s=50, alpha=0.7, color=PRIMARY_COLOR)
+        
+    plt.title(title, fontsize=TITLE_FONT_SIZE, weight='bold')
+    plt.xlabel(xlabel, fontsize=LABEL_FONT_SIZE)
+    plt.ylabel(ylabel, fontsize=LABEL_FONT_SIZE)
+    plt.tight_layout()
+    logging.info("Cluster plot generated successfully.")
+    return plt.gcf(), None
+
+def plot_dendrogram(X):
+    """Generates a dendrogram for hierarchical clustering.
+
+    Args:
+        X (pd.DataFrame or np.array): The input data for clustering.
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info("Generating dendrogram.")
+    if not isinstance(X, pd.DataFrame):
+        X = pd.DataFrame(X)
+    X_cleaned = X.dropna() # Handle NaNs
+    
+    if X_cleaned.empty:
+        logging.error("Data is empty after cleaning for Dendrogram.")
+        return None, "Data is empty after cleaning for Dendrogram."
+
+    # Limit the number of samples for dendrogram for performance and readability
+    if X_cleaned.shape[0] > 1000: 
+        logging.warning(f"Dendrogram data size ({X_cleaned.shape[0]}) is large. Sampling 1000 points.")
+        X_cleaned = X_cleaned.sample(n=1000, random_state=42)
+
+    try:
+        linked = linkage(X_cleaned, 'ward') # Ward method minimizes variance within clusters
+        plt.figure(figsize=(12, 8))
+        dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True, leaf_rotation=90, leaf_font_size=8)
+        plt.title('Hierarchical Clustering Dendrogram', fontsize=TITLE_FONT_SIZE, weight='bold')
+        plt.xlabel('Sample Index or Cluster Size', fontsize=LABEL_FONT_SIZE)
+        plt.ylabel('Distance', fontsize=LABEL_FONT_SIZE)
+        plt.tight_layout()
+        logging.info("Dendrogram generated successfully.")
+        return plt.gcf(), None
+    except Exception as e:
+        logging.error(f"Error generating dendrogram: {e}", exc_info=True)
+        return None, f"Error generating dendrogram: {e}"
+
+def plot_tsne(X, labels=None):
+    """Generates a t-SNE plot for dimensionality reduction and visualization of high-dimensional data.
+
+    Args:
+        X (pd.DataFrame or np.array): The input high-dimensional data.
+        labels (array-like, optional): Labels for coloring points (e.g., cluster assignments).
+
+    Returns:
+        tuple: A matplotlib Figure object and an error message (None if successful).
+    """
+    logging.info("Generating t-SNE plot.")
+    if not isinstance(X, pd.DataFrame):
+        X = pd.DataFrame(X)
+    X_cleaned = X.dropna() # Handle NaNs
+    
+    if X_cleaned.empty:
+        logging.error("Data is empty after cleaning for t-SNE.")
+        return None, "Data is empty after cleaning for t-SNE."
+
+    # t-SNE can be computationally expensive on large datasets, consider sampling
+    if X_cleaned.shape[0] > 2000:
+        logging.warning(f"t-SNE data size ({X_cleaned.shape[0]}) is large. Sampling 2000 points.")
+        X_cleaned = X_cleaned.sample(n=2000, random_state=42)
+        if labels is not None:
+            # Align labels with sampled data
+            if isinstance(labels, pd.Series):
+                labels = labels.loc[X_cleaned.index]
+            else: # If numpy array, convert to series for easy indexing
+                labels = pd.Series(labels).loc[X_cleaned.index]
+
+    try:
+        # Perplexity should be less than the number of samples
+        perplexity_val = min(30, len(X_cleaned) - 1) if len(X_cleaned) > 1 else 1
+        if perplexity_val < 1:
+            return None, "Not enough samples for t-SNE (need at least 2)."
+
+        tsne = TSNE(n_components=2, random_state=42, perplexity=perplexity_val)
+        X_tsne = tsne.fit_transform(X_cleaned)
+        
+        plt.figure(figsize=FIG_SIZE)
+        sns.set_style("whitegrid")
+        
+        if labels is not None:
+            sns.scatterplot(x=X_tsne[:, 0], y=X_tsne[:, 1], hue=labels, palette='viridis', s=50, alpha=0.7)
+            plt.legend(title='Cluster/Label', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=LEGEND_FONT_SIZE)
+        else:
+            sns.scatterplot(x=X_tsne[:, 0], y=X_tsne[:, 1], s=50, alpha=0.7, color=PRIMARY_COLOR)
+            
+        plt.title('t-SNE Plot', fontsize=TITLE_FONT_SIZE, weight='bold')
+        plt.xlabel('t-SNE Component 1', fontsize=LABEL_FONT_SIZE)
+        plt.ylabel('t-SNE Component 2', fontsize=LABEL_FONT_SIZE)
+        plt.tight_layout()
+        logging.info("t-SNE plot generated successfully.")
+        return plt.gcf(), None
+    except Exception as e:
+        logging.error(f"Error generating t-SNE plot: {e}", exc_info=True)
+        return None, f"Error generating t-SNE plot: {e}"