""" Feedback UI integration for structured error category selection. Integrates with the existing verification interface to provide structured feedback capture. """ import gradio as gr from typing import Dict, List, Optional, Tuple, Any from datetime import datetime from config.prompt_management.feedback_system import FeedbackSystem from config.prompt_management.data_models import ( ErrorType, ErrorSubcategory, QuestionIssueType, ReferralProblemType, ScenarioType ) class FeedbackUIIntegration: """ UI integration for structured feedback capture. Provides Gradio components for: - Structured error category selection - Predefined subcategories from documentation - Pattern analysis display for reviewers - Integration with existing verification interface """ def __init__(self, feedback_system: Optional[FeedbackSystem] = None): """ Initialize the feedback UI integration. Args: feedback_system: Optional feedback system instance. If None, creates default. """ self.feedback_system = feedback_system or FeedbackSystem() # Define UI options based on data models self.error_type_options = [ ("Wrong Classification", "wrong_classification"), ("Severity Misjudgment", "severity_misjudgment"), ("Missed Indicators", "missed_indicators"), ("False Positive", "false_positive"), ("Context Misunderstanding", "context_misunderstanding"), ("Language Interpretation", "language_interpretation") ] self.subcategory_mapping = { "wrong_classification": [ ("GREEN → YELLOW", "green_to_yellow"), ("GREEN → RED", "green_to_red"), ("YELLOW → GREEN", "yellow_to_green"), ("YELLOW → RED", "yellow_to_red"), ("RED → GREEN", "red_to_green"), ("RED → YELLOW", "red_to_yellow") ], "severity_misjudgment": [ ("Underestimated Distress", "underestimated_distress"), ("Overestimated Distress", "overestimated_distress") ], "missed_indicators": [ ("Emotional Indicators", "emotional_indicators"), ("Spiritual Indicators", "spiritual_indicators"), ("Social Indicators", "social_indicators") ], "false_positive": [ ("Misinterpreted Statement", "misinterpreted_statement"), ("Cultural Misunderstanding", "cultural_misunderstanding") ], "context_misunderstanding": [ ("Ignored History", "ignored_history"), ("Missed Defensive Response", "missed_defensive_response") ], "language_interpretation": [ ("Literal Interpretation", "literal_interpretation"), ("Missed Subtext", "missed_subtext") ] } self.question_issue_options = [ ("Inappropriate Question", "inappropriate_question"), ("Insensitive Language", "insensitive_language"), ("Wrong Scenario Targeting", "wrong_scenario_targeting"), ("Unclear Question", "unclear_question"), ("Leading Question", "leading_question") ] self.referral_problem_options = [ ("Incomplete Summary", "incomplete_summary"), ("Missing Contact Info", "missing_contact_info"), ("Incorrect Urgency", "incorrect_urgency"), ("Poor Context Description", "poor_context_description") ] self.scenario_options = [ ("Loss of Interest", "loss_of_interest"), ("Loss of Loved One", "loss_of_loved_one"), ("No Support", "no_support"), ("Vague Stress", "vague_stress"), ("Sleep Issues", "sleep_issues"), ("Spiritual Practice Change", "spiritual_practice_change") ] def create_classification_error_interface(self) -> gr.Group: """ Create UI components for recording classification errors. Returns: gr.Group: Gradio group containing classification error interface """ with gr.Group() as classification_group: gr.Markdown("### Classification Error Feedback") with gr.Row(): error_type = gr.Dropdown( choices=[label for label, _ in self.error_type_options], label="Error Type", info="Select the type of classification error" ) subcategory = gr.Dropdown( choices=[], label="Subcategory", info="Specific subcategory (updates based on error type)" ) with gr.Row(): expected_category = gr.Dropdown( choices=["GREEN", "YELLOW", "RED"], label="Expected Category", info="What the classification should have been" ) actual_category = gr.Dropdown( choices=["GREEN", "YELLOW", "RED"], label="Actual Category", info="What the system classified it as" ) message_content = gr.Textbox( label="Patient Message", placeholder="Enter the patient message that was misclassified...", lines=3, info="The original patient message" ) reviewer_comments = gr.Textbox( label="Reviewer Comments", placeholder="Explain why this is an error and what should have happened...", lines=3, info="Detailed explanation of the error" ) confidence_level = gr.Slider( minimum=0.0, maximum=1.0, value=0.8, step=0.1, label="Confidence Level", info="How confident are you in this feedback?" ) submit_error = gr.Button("Record Classification Error", variant="primary") error_result = gr.Textbox(label="Result", interactive=False) # Update subcategory options when error type changes def update_subcategories(error_type_label): if not error_type_label: return gr.Dropdown(choices=[]) # Find the error type value error_type_value = None for label, value in self.error_type_options: if label == error_type_label: error_type_value = value break if error_type_value and error_type_value in self.subcategory_mapping: choices = [label for label, _ in self.subcategory_mapping[error_type_value]] return gr.Dropdown(choices=choices) else: return gr.Dropdown(choices=[]) error_type.change( fn=update_subcategories, inputs=[error_type], outputs=[subcategory] ) # Handle error submission def submit_classification_error(error_type_label, subcategory_label, expected, actual, message, comments, confidence): try: # Convert labels to values error_type_value = None for label, value in self.error_type_options: if label == error_type_label: error_type_value = value break if not error_type_value: return "Error: Invalid error type selected" subcategory_value = None if error_type_value in self.subcategory_mapping: for label, value in self.subcategory_mapping[error_type_value]: if label == subcategory_label: subcategory_value = value break if not subcategory_value: return "Error: Invalid subcategory selected" # Validate required fields if not all([expected, actual, message, comments]): return "Error: All fields are required" # Record the error error_id = self.feedback_system.record_classification_error( error_type=ErrorType(error_type_value), subcategory=ErrorSubcategory(subcategory_value), expected_category=expected, actual_category=actual, message_content=message, reviewer_comments=comments, confidence_level=confidence, session_id=f"ui_session_{datetime.now().strftime('%Y%m%d_%H%M%S')}", additional_context={"source": "ui_interface"} ) return f"✓ Classification error recorded successfully (ID: {error_id[:8]}...)" except Exception as e: return f"Error recording classification error: {str(e)}" submit_error.click( fn=submit_classification_error, inputs=[error_type, subcategory, expected_category, actual_category, message_content, reviewer_comments, confidence_level], outputs=[error_result] ) return classification_group def create_question_issue_interface(self) -> gr.Group: """ Create UI components for recording question issues. Returns: gr.Group: Gradio group containing question issue interface """ with gr.Group() as question_group: gr.Markdown("### Question Issue Feedback") with gr.Row(): issue_type = gr.Dropdown( choices=[label for label, _ in self.question_issue_options], label="Issue Type", info="Type of issue with the generated question" ) scenario_type = gr.Dropdown( choices=[label for label, _ in self.scenario_options], label="Scenario Type", info="The scenario the question was targeting" ) question_content = gr.Textbox( label="Problematic Question", placeholder="Enter the question that has issues...", lines=2, info="The generated question that needs improvement" ) reviewer_comments = gr.Textbox( label="Issue Description", placeholder="Explain what's wrong with this question...", lines=3, info="Detailed explanation of the issue" ) with gr.Row(): severity = gr.Dropdown( choices=["low", "medium", "high"], label="Severity", value="medium", info="How severe is this issue?" ) suggested_improvement = gr.Textbox( label="Suggested Improvement (Optional)", placeholder="Suggest a better question...", lines=2, info="Optional suggestion for how to improve the question" ) submit_question = gr.Button("Record Question Issue", variant="primary") question_result = gr.Textbox(label="Result", interactive=False) # Handle question issue submission def submit_question_issue(issue_type_label, scenario_label, question, comments, severity_val, improvement): try: # Convert labels to values issue_type_value = None for label, value in self.question_issue_options: if label == issue_type_label: issue_type_value = value break scenario_value = None for label, value in self.scenario_options: if label == scenario_label: scenario_value = value break if not all([issue_type_value, scenario_value, question, comments, severity_val]): return "Error: All required fields must be filled" # Record the issue issue_id = self.feedback_system.record_question_issue( issue_type=QuestionIssueType(issue_type_value), question_content=question, scenario_type=ScenarioType(scenario_value), reviewer_comments=comments, severity=severity_val, session_id=f"ui_session_{datetime.now().strftime('%Y%m%d_%H%M%S')}", suggested_improvement=improvement if improvement else None ) return f"✓ Question issue recorded successfully (ID: {issue_id[:8]}...)" except Exception as e: return f"Error recording question issue: {str(e)}" submit_question.click( fn=submit_question_issue, inputs=[issue_type, scenario_type, question_content, reviewer_comments, severity, suggested_improvement], outputs=[question_result] ) return question_group def create_pattern_analysis_display(self) -> gr.Group: """ Create UI components for displaying error pattern analysis. Returns: gr.Group: Gradio group containing pattern analysis display """ with gr.Group() as pattern_group: gr.Markdown("### Error Pattern Analysis") refresh_patterns = gr.Button("Refresh Pattern Analysis", variant="secondary") pattern_display = gr.Markdown( value="Click 'Refresh Pattern Analysis' to see current error patterns and improvement suggestions.", label="Pattern Analysis Results" ) # Handle pattern analysis refresh def refresh_pattern_analysis(): try: # Get feedback summary summary = self.feedback_system.get_feedback_summary() # Analyze patterns patterns = self.feedback_system.analyze_error_patterns(min_frequency=2) # Format results result = "## Current Feedback Summary\n\n" result += f"- **Total Errors:** {summary['total_errors']}\n" result += f"- **Total Question Issues:** {summary['total_question_issues']}\n" result += f"- **Total Referral Problems:** {summary['total_referral_problems']}\n" result += f"- **Average Confidence:** {summary['average_confidence']:.2f}\n" result += f"- **Recent Errors:** {summary['recent_errors']}\n\n" if patterns: result += "## Identified Error Patterns\n\n" for i, pattern in enumerate(patterns[:5], 1): # Top 5 patterns result += f"### {i}. {pattern.pattern_type.replace('_', ' ').title()}\n" result += f"- **Frequency:** {pattern.frequency}\n" result += f"- **Description:** {pattern.description}\n" result += f"- **Confidence:** {pattern.confidence_score:.2f}\n" result += "- **Suggested Improvements:**\n" for suggestion in pattern.suggested_improvements[:3]: # Top 3 suggestions result += f" - {suggestion}\n" result += "\n" else: result += "## No Significant Patterns Detected\n\n" result += "Not enough data to identify patterns (minimum 2 occurrences required).\n\n" # Add top improvement suggestions if summary['improvement_suggestions']: result += "## Top Improvement Suggestions\n\n" for i, suggestion in enumerate(summary['improvement_suggestions'][:5], 1): result += f"{i}. {suggestion}\n" return result except Exception as e: return f"Error analyzing patterns: {str(e)}" refresh_patterns.click( fn=refresh_pattern_analysis, outputs=[pattern_display] ) return pattern_group def create_complete_feedback_interface(self) -> gr.Tabs: """ Create the complete feedback interface with all components. Returns: gr.Tabs: Complete feedback interface with multiple tabs """ with gr.Tabs() as feedback_tabs: with gr.Tab("Classification Errors"): self.create_classification_error_interface() with gr.Tab("Question Issues"): self.create_question_issue_interface() with gr.Tab("Pattern Analysis"): self.create_pattern_analysis_display() return feedback_tabs def create_feedback_ui_demo(): """ Create a demo of the feedback UI integration. Returns: gr.Blocks: Gradio interface for testing feedback UI """ feedback_ui = FeedbackUIIntegration() with gr.Blocks(title="Structured Feedback System Demo") as demo: gr.Markdown("# Structured Feedback System") gr.Markdown("This interface allows reviewers to provide structured feedback on AI classifications, questions, and referrals.") feedback_ui.create_complete_feedback_interface() gr.Markdown("---") gr.Markdown("**Note:** This is a demonstration of the structured feedback capture system. In production, this would be integrated with the main verification interface.") return demo if __name__ == "__main__": # Run the demo demo = create_feedback_ui_demo() demo.launch(share=False, server_name="127.0.0.1", server_port=7861)