interface/app.py

# interface/app.py
import gradio as gr
from typing import List, Dict
from .handlers import process_inputs
from .display import format_results
from .utils import validate_inputs
from config.settings import config
from .handlers import ProcessingHandler
from .utils import InputValidator

def create_interface():
    """Create the Gradio interface"""
    handler = ProcessingHandler()  
    
    # Create interface components
    with gr.Blocks(title="Image Inspection analysis") as app:
        gr.Markdown("# Image Inspection analysis System")
        
        with gr.Row():
            # Input components
            with gr.Column():
                query = gr.Textbox(
                    label="What would you like to analyze?",
                    placeholder="e.g., Check safety issues in pump systems",
                    lines=3
                )
                
                constraints = gr.Textbox(
                    label="Any specific constraints? (optional)",
                    placeholder="e.g., Exclude routine maintenance issues",
                    lines=2
                )
                
                top_k = gr.Slider(
                    minimum=1,
                    maximum=10,
                    value=5,
                    step=1,
                    label="Number of top results to show"
                )
                
                report_format = gr.Radio(
                    choices=["summary", "detailed"],
                    value="summary",
                    label="Report Format"
                )
                
                images = gr.File(
                    file_count="multiple",
                    label="Upload Images",
                    file_types=["image"]
                )

                submit_btn = gr.Button("Analyze", variant="primary")

            # Right column - Outputs
            with gr.Column():
                with gr.Tab("Results"):
                    analysis_status = gr.Markdown("Ready for analysis...")
                    
                    results_box = gr.Markdown(
                        visible=False,
                        label="Analysis Results"
                    )
                    
                    selected_images = gr.Gallery(
                        label="Selected Relevant Images",
                        visible=False,
                        columns=2,
                        height=400
                    )
                    
                    confidence_scores = gr.Json(
                        label="Confidence Scores",
                        visible=False
                    )
                
                with gr.Tab("Processing Details"):
                    processing_status = gr.JSON(
                        label="Processing Steps",
                        visible=False
                    )
                    
                with gr.Tab("Errors"):
                    error_box = gr.Markdown(
                        visible=False
                    )

        # Second: Helper functions for UI updates
        def update_ui_on_error(error_msg):
            return {
                results_box: gr.update(visible=True, value=error_msg),
                selected_images: gr.update(visible=False),
                confidence_scores: gr.update(visible=False),
                processing_status: gr.update(visible=True, value={'status': 'error'})
            }
        
        def update_ui_on_success(results):
            return {
                results_box: gr.update(visible=True, value=results['content']),
                selected_images: gr.update(visible=True, value=results['images']),
                confidence_scores: gr.update(visible=True, value=results['scores']),
                processing_status: gr.update(visible=True, value={'status': 'success'})
            }
        
        def validate_and_process(query, constraints, images, top_k, report_format):
            # Validate inputs
            is_valid, error_message = InputValidator.validate_inputs(
                query, constraints, images
            )
            
            if not is_valid:
                return update_ui_on_error(error_message)
            
            # If valid, proceed with processing
            return handler.process_inputs(
                query, constraints, images, top_k, report_format
            )
        
        # Single submit button with combined functionality
        #submit_btn = gr.Button("Analyze", variant="primary")
        
        # # Connect submit button to both clear and process
        # submit_btn.click(
        #     fn=lambda: [
        #         gr.Markdown.update(visible=True, value="Validating inputs......"),
        #         gr.Markdown.update(visible=False),
        #         gr.Gallery.update(visible=False),
        #         gr.Json.update(visible=False),
        #         gr.JSON.update(visible=False)
        #     ],
        #     inputs=None,
        #     outputs=[analysis_status, results_box, selected_images, 
        #             confidence_scores, processing_status]
        # ).then(  # Chain the processing after clearing
        #     fn=validate_and_process,
        #     inputs=[query, constraints, images, top_k, report_format],
        #     outputs=[results_box, selected_images, confidence_scores, processing_status],
        #     #show_progress=True
        # ).then(
        #     # Third: Update UI based on results
        #     fn=lambda results: update_ui_on_success(results) if results['status'] == 'success' 
        #                     else update_ui_on_error(results['error']),
        #     inputs=[processing_status],
        #     outputs=[results_box, selected_images, confidence_scores, processing_status]
        # )


        progress_message = gr.Markdown("Ready to analyze...")

        
        # Update the click handler

        submit_btn.click(
            # First update UI to show processing state
            fn=lambda: (
                gr.update(visible=True, value="⏳ Processing your request..."),  # progress message
                gr.update(visible=False),  # results
                gr.update(visible=False),  # images
                gr.update(visible=False),  # scores
                gr.update(visible=True, value={"status": "started", "step": "initializing"})  # status
            ),
            inputs=None,
            outputs=[
                progress_message,
                results_box,
                selected_images,
                confidence_scores,
                processing_status
            ]
        ).then(
            # Process inputs
            fn=handler.process_inputs,
            inputs=[
                query,
                constraints,
                images,
                top_k,
                report_format
            ],
            outputs=[
                results_box,
                selected_images,
                confidence_scores,
                processing_status
            ],
            show_progress="full"  # Show gradio's built-in progress
        ).then(
            # Update UI after processing
            fn=lambda: gr.update(visible=True, value="✅ Analysis complete!"),
            inputs=None,
            outputs=progress_message
        )

        # Add error handling feedback
        progress_message.change(
            fn=lambda x: gr.update(visible=True) if "Error" in str(x) else gr.update(visible=False),
            inputs=[progress_message],
            outputs=[progress_message]
        )
        # submit_btn.click(
        #     # First clear/reset outputs
        #     fn=lambda: (
        #         "Processing...",  # for results_box
        #         None,            # for selected_images
        #         None,            # for confidence_scores
        #         {"status": "processing"}  # for processing_status
        #     ),
        #     inputs=None,
        #     outputs=[
        #         results_box,
        #         selected_images,
        #         confidence_scores,
        #         processing_status
        #     ]
        # ).then(  # Then process inputs
        #     fn=handler.process_inputs,
        #     inputs=[
        #         query,
        #         constraints,
        #         images,
        #         top_k,
        #         report_format
        #     ],
        #     outputs=[
        #         results_box,
        #         selected_images,
        #         confidence_scores,
        #         processing_status
        #     ]
        # )

    return app

# Launch the interface
if __name__ == "__main__":
    interface = create_interface()
    interface.launch()