ui/callbacks.py

# ui/callbacks.py

# -*- coding: utf-8 -*-
#
# PROJECT:      CognitiveEDA v5.6 - The QuantumLeap Intelligence Platform
#
# DESCRIPTION:  This module contains the core logic for all Gradio event handlers.
#               The main analysis pipeline now includes a strategic feature
#               engineering step before analysis.

import gradio as gr
import pandas as pd
import logging
from threading import Thread

import plotly.graph_objects as go
import plotly.express as px

# --- MODIFIED IMPORT ---
# Import both the analyzer class and the new feature engineering function
from core.analyzer import DataAnalyzer, engineer_features
from core.llm import GeminiNarrativeGenerator
from core.config import settings
from core.exceptions import DataProcessingError
from modules.clustering import perform_clustering


# --- Primary Analysis Chain ---

def run_initial_analysis(file_obj, progress=gr.Progress(track_tqdm=True)):
    """
    Phase 1: Now includes the strategic feature engineering step.
    Validates inputs, loads raw data, applies feature engineering, and then
    creates the core DataAnalyzer object on the transformed data.
    """
    if file_obj is None:
        raise gr.Error("No file uploaded. Please upload a CSV or Excel file.")

    progress(0, desc="Validating configuration...")
    if not settings.GOOGLE_API_KEY:
        logging.error("Analysis attempted without GOOGLE_API_KEY set.")
        raise gr.Error("CRITICAL: GOOGLE_API_KEY is not configured. Please add it as a secret.")

    try:
        progress(0.1, desc="Loading raw data...")
        df_raw = pd.read_csv(file_obj.name) if file_obj.name.endswith('.csv') else pd.read_excel(file_obj.name)
        if len(df_raw) > settings.MAX_UI_ROWS:
            df_raw = df_raw.sample(n=settings.MAX_UI_ROWS, random_state=42)
            logging.info(f"DataFrame sampled down to {settings.MAX_UI_ROWS} rows.")

        # --- INTEGRATION POINT ---
        # Apply the feature engineering function immediately after loading
        progress(0.5, desc="Applying strategic feature engineering...")
        df_engineered = engineer_features(df_raw)
        # -------------------------

        progress(0.8, desc="Instantiating analysis engine on engineered data...")
        # The analyzer now works with the transformed, high-value dataset
        analyzer = DataAnalyzer(df_engineered) 
        
        progress(1.0, desc="Analysis complete. Generating reports...")
        return analyzer
    except Exception as e:
        logging.error(f"A critical error occurred during initial analysis: {e}", exc_info=True)
        raise gr.Error(f"Analysis Failed! An unexpected error occurred: {str(e)}")


def generate_reports_and_visuals(analyzer, progress=gr.Progress(track_tqdm=True)):
    """
    Phase 2: Slower, multi-stage report and visual generation.
    Yields tuples of UI updates based on the *engineered* data.
    """
    if not isinstance(analyzer, DataAnalyzer):
        logging.warning("generate_reports_and_visuals called without a valid analyzer. Clearing UI.")
        yield (None,) * 14
        return

    progress(0, desc="Spawning AI report thread...")
    ai_report_queue = [""]
    def generate_ai_report_threaded(analyzer_instance):
        narrative_generator = GeminiNarrativeGenerator(api_key=settings.GOOGLE_API_KEY)
        ai_report_queue[0] = narrative_generator.generate_narrative(analyzer_instance)
    
    thread = Thread(target=generate_ai_report_threaded, args=(analyzer,))
    thread.start()

    progress(0.4, desc="Generating reports and visuals...")
    meta = analyzer.metadata
    missing_df, num_df, cat_df = analyzer.get_profiling_reports()
    fig_types, fig_missing, fig_corr = analyzer.get_overview_visuals()

    initial_updates = (
        gr.update(value="⏳ Generating AI report... Dashboard is ready."),
        gr.update(value=missing_df),
        gr.update(value=num_df),
        gr.update(value=cat_df),
        gr.update(value=fig_types),
        gr.update(value=fig_missing),
        gr.update(value=fig_corr),
        gr.update(choices=meta['numeric_cols'], value=meta['numeric_cols'][0] if meta['numeric_cols'] else None),
        gr.update(choices=meta['numeric_cols'], value=meta['numeric_cols'][0] if meta['numeric_cols'] else None),
        gr.update(choices=meta['numeric_cols'], value=meta['numeric_cols'][1] if len(meta['numeric_cols']) > 1 else None),
        gr.update(choices=meta['columns']),
        gr.update(visible=bool(meta['datetime_cols'])),
        gr.update(visible=bool(meta['text_cols'])),
        gr.update(visible=len(meta['numeric_cols']) > 1)
    )
    yield initial_updates

    thread.join()
    progress(1.0, desc="AI Report complete!")

    final_updates_list = list(initial_updates)
    final_updates_list[0] = gr.update(value=ai_report_queue[0])
    yield tuple(final_updates_list)


# --- Interactive Explorer & Module Callbacks ---

def create_histogram(analyzer, col):
    if not isinstance(analyzer, DataAnalyzer) or not col:
        return go.Figure()
    return px.histogram(analyzer.df, x=col, title=f"<b>Distribution of {col}</b>", marginal="box")

def create_scatterplot(analyzer, x_col, y_col, color_col):
    if not isinstance(analyzer, DataAnalyzer) or not x_col or not y_col:
        return go.Figure()
    df_sample = analyzer.df.sample(n=min(len(analyzer.df), 10000))
    return px.scatter(df_sample, x=x_col, y=y_col, color=color_col if color_col else None)

def update_clustering(analyzer, k):
    if not isinstance(analyzer, DataAnalyzer):
        return gr.update(), gr.update(), gr.update()
    fig_cluster, fig_elbow, summary = perform_clustering(analyzer.df, analyzer.metadata['numeric_cols'], k)
    return fig_cluster, fig_elbow, summary