Create ui/callbacks.py

ui/callbacks.py

@@ -0,0 +1,100 @@
+# ui/callbacks.py
+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 APIKeyMissingError, DataProcessingError
+from modules.clustering import perform_clustering
+# ... other module imports
+
+def register_callbacks(components):
+    """Binds event handlers to the UI components."""
+
+    # --- Main Analysis Trigger ---
+    def run_full_analysis(file_obj):
+        # 1. Input Validation
+        if file_obj is None:
+            raise gr.Error("No file uploaded. Please upload a CSV or Excel file.")
+        if not settings.GOOGLE_API_KEY:
+            raise APIKeyMissingError("CRITICAL: GOOGLE_API_KEY not found in .env file.")
+
+        try:
+            # 2. Data Loading & Pre-processing
+            logging.info(f"Processing uploaded file: {file_obj.name}")
+            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)
+            
+            # 3. Core Analysis
+            analyzer = DataAnalyzer(df)
+            meta = analyzer.metadata
+            missing_df, num_df, cat_df = analyzer.get_profiling_reports()
+            fig_types, fig_missing, fig_corr = analyzer.get_overview_visuals()
+
+            # 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. Prepare Initial UI Updates (Instantaneous)
+            updates = {
+                components["state_analyzer"]: analyzer,
+                components["ai_report_output"]: "⏳ Generating AI-powered report... This may take a moment.",
+                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),
+                # ... update dropdowns and visibility ...
+                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 updates
+
+            # 6. Final UI Update (When AI report is ready)
+            thread.join() # Wait for the AI thread to finish
+            updates[components["ai_report_output"]] = ai_report_queue[0]
+            yield updates
+
+        except (DataProcessingError, APIKeyMissingError) as e:
+            logging.error(f"User-facing 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 function
+    components["analyze_button"].click(
+        fn=run_full_analysis, 
+        inputs=[components["upload_button"]], 
+        outputs=list(components.values())
+    )
+
+    # --- Clustering Tab Callback ---
+    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"]]
+    )
+    
+    # ... Register other callbacks for histogram, scatter, etc. in a similar fashion ...