Create app_bk.py

interface/app_bk.py

@@ -0,0 +1,185 @@
+# 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]
+        # )
+        # Update the click handler
+        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()