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# ui/callbacks.py | |
# -*- coding: utf-8 -*- | |
# | |
# PROJECT: CognitiveEDA v5.0 - The QuantumLeap Intelligence Platform | |
# | |
# DESCRIPTION: The "Controller" of the application. This module contains all | |
# the Gradio event handlers (callbacks) that connect the UI (view) | |
# to the core analysis engine (model). | |
import gradio as gr | |
import pandas as pd | |
import logging | |
from threading import Thread | |
from core.analyzer import DataAnalyzer | |
from core.llm import GeminiNarrativeGenerator | |
from core.config import settings | |
from core.exceptions import DataProcessingError | |
from modules.clustering import perform_clustering | |
from modules.text import generate_word_cloud | |
from modules.timeseries import analyze_time_series | |
# NOTE: The UI layout file would need to be updated to pass all the individual component | |
# references, which is done in the create_main_layout function. This callback assumes | |
# a dictionary of components is passed to it. | |
def register_callbacks(components): | |
""" | |
Binds all event handlers (callbacks) to the UI components. | |
Args: | |
components (dict): A dictionary mapping component names to their | |
Gradio component objects. | |
""" | |
# --- Main Analysis Trigger --- | |
def run_full_analysis(file_obj, progress=gr.Progress(track_tqdm=True)): | |
""" | |
The primary orchestration function triggered by the user. It loads data, | |
runs the standard analysis, and spawns a thread for the AI report. | |
""" | |
# 1. Input Validation (File) | |
if file_obj is None: | |
raise gr.Error("No file uploaded. Please upload a CSV or Excel file.") | |
# 2. Runtime Configuration Validation (API Key) | |
# This is the critical fix. We check for the key here, at the point of use, | |
# rather than letting it crash the app at startup. | |
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. " | |
"The AI Strategy Report cannot be generated. Please add the key to your " | |
".env file (for local development) or as a platform secret (for deployed apps) and restart." | |
) | |
try: | |
# 3. Data Loading & Core Analysis | |
progress(0.1, desc="Loading and parsing data...") | |
df = pd.read_csv(file_obj.name) if file_obj.name.endswith('.csv') else pd.read_excel(file_obj.name) | |
if len(df) > settings.MAX_UI_ROWS: | |
df = df.sample(n=settings.MAX_UI_ROWS, random_state=42) | |
progress(0.3, desc="Instantiating analysis engine...") | |
analyzer = DataAnalyzer(df) | |
meta = analyzer.metadata | |
# 4. Asynchronous AI Narrative Generation | |
ai_report_queue = [""] # Use a mutable list to pass string by reference | |
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() | |
# 5. Generate Standard Reports and Visuals (runs immediately) | |
progress(0.5, desc="Generating data profiles...") | |
missing_df, num_df, cat_df = analyzer.get_profiling_reports() | |
progress(0.7, desc="Creating overview visualizations...") | |
fig_types, fig_missing, fig_corr = analyzer.get_overview_visuals() | |
# 6. Prepare and yield initial UI updates | |
progress(0.9, desc="Building initial dashboard...") | |
initial_updates = { | |
components["state_analyzer"]: analyzer, | |
components["ai_report_output"]: gr.update(value="⏳ Generating AI-powered report in the background... The main dashboard is ready now."), | |
components["profile_missing_df"]: gr.update(value=missing_df), | |
components["profile_numeric_df"]: gr.update(value=num_df), | |
components["profile_categorical_df"]: gr.update(value=cat_df), | |
components["plot_types"]: gr.update(value=fig_types), | |
components["plot_missing"]: gr.update(value=fig_missing), | |
components["plot_correlation"]: gr.update(value=fig_corr), | |
components["dd_hist_col"]: gr.update(choices=meta['numeric_cols'], value=meta['numeric_cols'][0] if meta['numeric_cols'] else None), | |
components["dd_scatter_x"]: gr.update(choices=meta['numeric_cols'], value=meta['numeric_cols'][0] if meta['numeric_cols'] else None), | |
components["dd_scatter_y"]: gr.update(choices=meta['numeric_cols'], value=meta['numeric_cols'][1] if len(meta['numeric_cols']) > 1 else None), | |
components["dd_scatter_color"]: gr.update(choices=meta['columns']), | |
components["tab_timeseries"]: gr.update(visible=bool(meta['datetime_cols'])), | |
components["tab_text"]: gr.update(visible=bool(meta['text_cols'])), | |
components["tab_cluster"]: gr.update(visible=len(meta['numeric_cols']) > 1), | |
} | |
yield initial_updates | |
# 7. Wait for AI thread and yield final update | |
thread.join() | |
progress(1.0, desc="AI Report complete!") | |
final_updates = initial_updates.copy() | |
final_updates[components["ai_report_output"]] = ai_report_queue[0] | |
yield final_updates | |
except DataProcessingError as e: | |
logging.error(f"User-facing data processing error: {e}", exc_info=True) | |
raise gr.Error(str(e)) | |
except Exception as e: | |
logging.error(f"A critical unhandled error occurred: {e}", exc_info=True) | |
raise gr.Error(f"Analysis Failed! An unexpected error occurred: {str(e)}") | |
# Bind the main analysis function | |
# Note: `outputs` must be a list of all components being updated. | |
output_component_list = list(components.values()) | |
components["analyze_button"].click( | |
fn=run_full_analysis, | |
inputs=[components["upload_button"]], | |
outputs=output_component_list | |
) | |
# --- Other Interactive Callbacks --- | |
def update_clustering(analyzer, k): | |
if not analyzer: 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 | |
components["num_clusters"].change( | |
fn=update_clustering, | |
inputs=[components["state_analyzer"], components["num_clusters"]], | |
outputs=[components["plot_cluster"], components["plot_elbow"], components["md_cluster_summary"]] | |
) | |
# (Imagine other callbacks for scatter, histogram, etc., are registered here) |