Update app/gradio_interface.py

app/gradio_interface.py

@@ -1,339 +1,339 @@
-# app/gradio_interface.py
-import os
-import gradio as gr
-import time
-import threading
-import tempfile
-import shutil
-from typing import Dict, List, Optional, Tuple, Union, Any
-import json
-import markdown
-import matplotlib.pyplot as plt
-import numpy as np
-from PIL import Image
-import io
-import base64
-
-class GradioInterface:
-    def __init__(self, orchestrator):
-        """Initialize the Gradio interface with the orchestrator."""
-        self.orchestrator = orchestrator
-        self.active_sessions = {}
-        self.processing_threads = {}
-        
-        # Create temporary directory for file uploads
-        self.temp_dir = tempfile.mkdtemp()
-        self.text_dir = os.path.join(self.temp_dir, "texts")
-        self.image_dir = os.path.join(self.temp_dir, "images")
-        os.makedirs(self.text_dir, exist_ok=True)
-        os.makedirs(self.image_dir, exist_ok=True)
-    
-    def create_interface(self):
-        """Create and return the Gradio interface."""
-        with gr.Blocks(title="Deep Dive Analysis with Sustainable AI", 
-                      theme=gr.themes.Soft(primary_hue="teal")) as interface:
-            
-            # Session management
-            session_id = gr.State("")
-            processing_status = gr.State("idle")
-            result_data = gr.State(None)
-            
-            gr.Markdown("# 🌿 Deep Dive Analysis with Sustainable AI")
-            gr.Markdown("Upload text files and images to analyze a topic in depth, with optimized AI processing.")
-            
-            with gr.Row():
-                with gr.Column(scale=2):
-                    # Input section
-                    with gr.Box():
-                        gr.Markdown("## 📝 Input")
-                        topic_input = gr.Textbox(label="Topic for Deep Dive", placeholder="Enter a topic to analyze...")
-                        
-                        with gr.Row():
-                            text_files = gr.File(label="Upload Text Files", file_count="multiple", file_types=[".txt", ".md", ".pdf", ".docx"])
-                            image_files = gr.File(label="Upload Images", file_count="multiple", file_types=["image"])
-                            
-                        analyze_btn = gr.Button("Start Analysis", variant="primary")
-                        status_msg = gr.Markdown("Ready to analyze.")
-                
-                with gr.Column(scale=1):
-                    # Sustainability metrics
-                    with gr.Box():
-                        gr.Markdown("## 📊 Sustainability Metrics")
-                        metrics_display = gr.Markdown("No metrics available yet.")
-                        metrics_chart = gr.Plot(label="Energy Usage")
-                        update_metrics_btn = gr.Button("Update Metrics")
-            
-            # Results section
-            with gr.Box():
-                gr.Markdown("## 📑 Analysis Results")
-                
-                with gr.Tabs() as tabs:
-                    with gr.TabItem("Executive Summary"):
-                        exec_summary = gr.Markdown("No results available yet.")
-                        confidence_indicator = gr.Markdown("")
-                        
-                    with gr.TabItem("Detailed Report"):
-                        detailed_report = gr.Markdown("No detailed report available yet.")
-                        
-                    with gr.TabItem("Text Analysis"):
-                        text_analysis = gr.Markdown("No text analysis available yet.")
-                        
-                    with gr.TabItem("Image Analysis"):
-                        with gr.Row():
-                            image_gallery = gr.Gallery(label="Analyzed Images")
-                        image_analysis = gr.Markdown("No image analysis available yet.")
-                        
-                    with gr.TabItem("Raw Data"):
-                        raw_json = gr.JSON(None)
-                        
-            # Define event handlers
-            def initialize_session():
-                """Initialize a new session."""
-                new_session = self.orchestrator.create_session()
-                return new_session, "idle", None
-                
-            def process_files(session, topic, text_files, image_files, status):
-                """Process uploaded files and start analysis."""
-                if not topic:
-                    return session, "error", "Please enter a topic for analysis.", None
-                    
-                if not text_files and not image_files:
-                    return session, "error", "Please upload at least one text file or image.", None
-                
-                # Save uploaded files to temp directories
-                text_file_paths = []
-                if text_files:
-                    for file in text_files:
-                        dest_path = os.path.join(self.text_dir, os.path.basename(file.name))
-                        shutil.copy(file.name, dest_path)
-                        text_file_paths.append(dest_path)
-                
-                image_file_paths = []
-                if image_files:
-                    for file in image_files:
-                        dest_path = os.path.join(self.image_dir, os.path.basename(file.name))
-                        shutil.copy(file.name, dest_path)
-                        image_file_paths.append(dest_path)
-                
-                # Start processing in a separate thread to avoid blocking the UI
-                def process_thread():
-                    try:
-                        # Use synchronized workflow for better control
-                        result = self.orchestrator.coordinate_workflow_with_synchronization(
-                            session, topic, text_file_paths, image_file_paths)
-                        
-                        # Store result for UI access
-                        self.active_sessions[session] = result
-                    except Exception as e:
-                        self.active_sessions[session] = {"error": str(e), "status": "error"}
-                
-                # Start processing thread
-                thread = threading.Thread(target=process_thread)
-                thread.daemon = True
-                thread.start()
-                self.processing_threads[session] = thread
-                
-                return session, "processing", "Analysis in progress... This may take a few minutes.", None
-            
-            def check_status(session, status):
-                """Check the status of the current processing job."""
-                if session and session in self.active_sessions:
-                    result = self.active_sessions[session]
-                    
-                    if isinstance(result, dict):
-                        if "error" in result:
-                            return "error", f"Error: {result['error']}", result
-                        elif result.get("status") == "completed":
-                            return "completed", "Analysis completed successfully!", result
-                
-                if status == "processing":
-                    return status, "Analysis in progress... This may take a few minutes.", None
-                
-                return status, "Ready to analyze.", None
-            
-            def update_results(result_data):
-                """Update the UI with results."""
-                if not result_data:
-                    return ("No results available yet.", 
-                            "", 
-                            "No detailed report available yet.",
-                            "No text analysis available yet.",
-                            [], 
-                            "No image analysis available yet.",
-                            None)
-                
-                # Extract results
-                exec_summary_text = "No executive summary available."
-                confidence_text = ""
-                detailed_report_text = "No detailed report available."
-                text_analysis_text = "No text analysis available."
-                image_list = []
-                image_analysis_text = "No image analysis available."
-                
-                # Process report data
-                if "report" in result_data:
-                    report = result_data["report"]
-                    
-                    # Executive summary
-                    if "executive_summary" in report:
-                        exec_summary_text = report["executive_summary"]
-                    
-                    # Confidence statement
-                    if "confidence_statement" in report:
-                        confidence_level = report.get("confidence_level", "unknown")
-                        confidence_text = f"**Confidence Level: {confidence_level.title()}**\n\n"
-                        confidence_text += report["confidence_statement"]
-                    
-                    # Detailed report
-                    if "detailed_report" in report:
-                        detailed_report_text = report["detailed_report"]
-                
-                # Process text analysis
-                if "results" in result_data and "text_analysis" in result_data["results"]:
-                    text_data = result_data["results"]["text_analysis"]
-                    
-                    if "document_analyses" in text_data:
-                        text_analysis_text = f"### Text Analysis Results\n\n"
-                        text_analysis_text += f"Found {text_data.get('relevant_documents', 0)} relevant documents out of {text_data.get('total_documents', 0)}.\n\n"
-                        
-                        for i, doc in enumerate(text_data["document_analyses"]):
-                            text_analysis_text += f"#### Document {i+1}: {doc.get('filename', 'Unknown')}\n\n"
-                            text_analysis_text += f"Relevance: {doc.get('relevance_score', 0):.2f}\n\n"
-                            text_analysis_text += f"{doc.get('summary', 'No summary available.')}\n\n"
-                
-                # Process image analysis
-                if "results" in result_data and "image_analysis" in result_data["results"]:
-                    img_data = result_data["results"]["image_analysis"]
-                    
-                    if "image_analyses" in img_data:
-                        image_analysis_text = f"### Image Analysis Results\n\n"
-                        image_analysis_text += f"Found {img_data.get('relevant_images', 0)} relevant images out of {img_data.get('total_images', 0)}.\n\n"
-                        
-                        # Get processed images for gallery
-                        if "processed_images" in img_data:
-                            for img_info in img_data["processed_images"]:
-                                if img_info.get("is_relevant", False):
-                                    try:
-                                        img_path = img_info.get("filepath", "")
-                                        if os.path.exists(img_path):
-                                            # Add to gallery
-                                            image_list.append((img_path, img_info.get("caption", "No caption")))
-                                    except Exception as e:
-                                        print(f"Error loading image: {e}")
-                        
-                        # Format analysis text
-                        for i, img in enumerate(img_data["image_analyses"]):
-                            image_analysis_text += f"#### Image {i+1}: {img.get('filename', 'Unknown')}\n\n"
-                            image_analysis_text += f"Caption: {img.get('caption', 'No caption available.')}\n\n"
-                            image_analysis_text += f"Relevance: {img.get('relevance_score', 0):.2f}\n\n"
-                            image_analysis_text += f"Model used: {img.get('model_used', 'unknown')}\n\n"
-                
-                return (exec_summary_text, 
-                        confidence_text, 
-                        detailed_report_text,
-                        text_analysis_text,
-                        image_list, 
-                        image_analysis_text,
-                        result_data)
-            
-            def update_metrics():
-                """Update sustainability metrics display."""
-                metrics = self.orchestrator.get_sustainability_metrics()
-                
-                if "error" in metrics:
-                    return "No metrics available: " + metrics["error"], None
-                
-                # Format metrics for display
-                metrics_text = "### Sustainability Metrics\n\n"
-                
-                # Energy usage
-                energy_usage = metrics.get("energy_usage", {}).get("total", 0)
-                metrics_text += f"**Total Energy Usage**: {energy_usage:.6f} Wh\n\n"
-                
-                # Carbon footprint
-                carbon = metrics.get("carbon_footprint_kg", 0)
-                metrics_text += f"**Carbon Footprint**: {carbon:.6f} kg CO₂\n\n"
-                
-                # Optimization gains
-                opt_gains = metrics.get("optimization_gains", {})
-                tokens_saved = opt_gains.get("tokens_saved", 0)
-                tokens_saved_pct = opt_gains.get("tokens_saved_pct", 0)
-                energy_saved = opt_gains.get("total_energy_saved", 0)
-                
-                metrics_text += f"**Tokens Saved**: {tokens_saved} ({tokens_saved_pct:.1f}%)\n\n"
-                metrics_text += f"**Energy Saved**: {energy_saved:.6f} Wh\n\n"
-                
-                # Environmental equivalents
-                env_equiv = metrics.get("environmental_equivalents", {})
-                if env_equiv:
-                    metrics_text += "### Environmental Impact\n\n"
-                    for impact, value in env_equiv.items():
-                        name = impact.replace("_", " ").title()
-                        metrics_text += f"**{name}**: {value:.2f}\n\n"
-                
-                # Create chart
-                fig, ax = plt.subplots(figsize=(6, 4))
-                
-                # Energy by model
-                energy_by_model = metrics.get("energy_usage", {}).get("by_model", {})
-                if energy_by_model:
-                    models = list(energy_by_model.keys())
-                    values = list(energy_by_model.values())
-                    
-                    # Shorten model names for display
-                    short_names = [m.split("/")[-1] if "/" in m else m for m in models]
-                    
-                    ax.bar(short_names, values)
-                    ax.set_ylabel("Energy (Wh)")
-                    ax.set_title("Energy Usage by Model")
-                    plt.xticks(rotation=45, ha="right")
-                    plt.tight_layout()
-                
-                return metrics_text, fig
-            
-            # Connect event handlers
-            session_id = gr.on_load(initialize_session)[0]
-            
-            analyze_btn.click(
-                process_files,
-                inputs=[session_id, topic_input, text_files, image_files, processing_status],
-                outputs=[session_id, processing_status, status_msg, result_data]
-            )
-            
-            # Periodic status check
-            gr.on(
-                "change",
-                lambda s, st: check_status(s, st),
-                inputs=[session_id, processing_status],
-                outputs=[processing_status, status_msg, result_data],
-                every=2  # Check every 2 seconds
-            )
-            
-            # Update results when result_data changes
-            result_data.change(
-                update_results,
-                inputs=[result_data],
-                outputs=[exec_summary, confidence_indicator, detailed_report, text_analysis, 
-                         image_gallery, image_analysis, raw_json]
-            )
-            
-            # Update metrics
-            update_metrics_btn.click(
-                update_metrics,
-                inputs=[],
-                outputs=[metrics_display, metrics_chart]
-            )
-            
-        return interface
-    
-    def launch(self, **kwargs):
-        """Launch the Gradio interface."""
-        interface = self.create_interface()
-        interface.launch(**kwargs)
-        
-    def cleanup(self):
-        """Clean up temporary files."""
-        try:
-            shutil.rmtree(self.temp_dir)
-        except Exception as e:
-            print(f"Error cleaning up temp files: {e}")
+# app/gradio_interface.py
+import os
+import gradio as gr
+import time
+import threading
+import tempfile
+import shutil
+from typing import Dict, List, Optional, Tuple, Union, Any
+import json
+import markdown
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import io
+import base64
+
+class GradioInterface:
+    def __init__(self, orchestrator):
+        """Initialize the Gradio interface with the orchestrator."""
+        self.orchestrator = orchestrator
+        self.active_sessions = {}
+        self.processing_threads = {}
+        
+        # Create temporary directory for file uploads
+        self.temp_dir = tempfile.mkdtemp()
+        self.text_dir = os.path.join(self.temp_dir, "texts")
+        self.image_dir = os.path.join(self.temp_dir, "images")
+        os.makedirs(self.text_dir, exist_ok=True)
+        os.makedirs(self.image_dir, exist_ok=True)
+    
+    def create_interface(self):
+        """Create and return the Gradio interface."""
+        with gr.Blocks(title="Deep Dive Analysis with Sustainable AI", 
+                      theme=gr.themes.Soft(primary_hue="teal")) as interface:
+            
+            # Session management
+            session_id = gr.State("")
+            processing_status = gr.State("idle")
+            result_data = gr.State(None)
+            
+            gr.Markdown("# 🌿 Deep Dive Analysis with Sustainable AI")
+            gr.Markdown("Upload text files and images to analyze a topic in depth, with optimized AI processing.")
+            
+            with gr.Row():
+                with gr.Column(scale=2):
+                    # Input section
+                    with gr.Blocks():
+                        gr.Markdown("## 📝 Input")
+                        topic_input = gr.Textbox(label="Topic for Deep Dive", placeholder="Enter a topic to analyze...")
+                        
+                        with gr.Row():
+                            text_files = gr.File(label="Upload Text Files", file_count="multiple", file_types=[".txt", ".md", ".pdf", ".docx"])
+                            image_files = gr.File(label="Upload Images", file_count="multiple", file_types=["image"])
+                            
+                        analyze_btn = gr.Button("Start Analysis", variant="primary")
+                        status_msg = gr.Markdown("Ready to analyze.")
+                
+                with gr.Column(scale=1):
+                    # Sustainability metrics
+                    with gr.Blocks():
+                        gr.Markdown("## 📊 Sustainability Metrics")
+                        metrics_display = gr.Markdown("No metrics available yet.")
+                        metrics_chart = gr.Plot(label="Energy Usage")
+                        update_metrics_btn = gr.Button("Update Metrics")
+            
+            # Results section
+            with gr.Blocks():
+                gr.Markdown("## 📑 Analysis Results")
+                
+                with gr.Tabs() as tabs:
+                    with gr.TabItem("Executive Summary"):
+                        exec_summary = gr.Markdown("No results available yet.")
+                        confidence_indicator = gr.Markdown("")
+                        
+                    with gr.TabItem("Detailed Report"):
+                        detailed_report = gr.Markdown("No detailed report available yet.")
+                        
+                    with gr.TabItem("Text Analysis"):
+                        text_analysis = gr.Markdown("No text analysis available yet.")
+                        
+                    with gr.TabItem("Image Analysis"):
+                        with gr.Row():
+                            image_gallery = gr.Gallery(label="Analyzed Images")
+                        image_analysis = gr.Markdown("No image analysis available yet.")
+                        
+                    with gr.TabItem("Raw Data"):
+                        raw_json = gr.JSON(None)
+                        
+            # Define event handlers
+            def initialize_session():
+                """Initialize a new session."""
+                new_session = self.orchestrator.create_session()
+                return new_session, "idle", None
+                
+            def process_files(session, topic, text_files, image_files, status):
+                """Process uploaded files and start analysis."""
+                if not topic:
+                    return session, "error", "Please enter a topic for analysis.", None
+                    
+                if not text_files and not image_files:
+                    return session, "error", "Please upload at least one text file or image.", None
+                
+                # Save uploaded files to temp directories
+                text_file_paths = []
+                if text_files:
+                    for file in text_files:
+                        dest_path = os.path.join(self.text_dir, os.path.basename(file.name))
+                        shutil.copy(file.name, dest_path)
+                        text_file_paths.append(dest_path)
+                
+                image_file_paths = []
+                if image_files:
+                    for file in image_files:
+                        dest_path = os.path.join(self.image_dir, os.path.basename(file.name))
+                        shutil.copy(file.name, dest_path)
+                        image_file_paths.append(dest_path)
+                
+                # Start processing in a separate thread to avoid blocking the UI
+                def process_thread():
+                    try:
+                        # Use synchronized workflow for better control
+                        result = self.orchestrator.coordinate_workflow_with_synchronization(
+                            session, topic, text_file_paths, image_file_paths)
+                        
+                        # Store result for UI access
+                        self.active_sessions[session] = result
+                    except Exception as e:
+                        self.active_sessions[session] = {"error": str(e), "status": "error"}
+                
+                # Start processing thread
+                thread = threading.Thread(target=process_thread)
+                thread.daemon = True
+                thread.start()
+                self.processing_threads[session] = thread
+                
+                return session, "processing", "Analysis in progress... This may take a few minutes.", None
+            
+            def check_status(session, status):
+                """Check the status of the current processing job."""
+                if session and session in self.active_sessions:
+                    result = self.active_sessions[session]
+                    
+                    if isinstance(result, dict):
+                        if "error" in result:
+                            return "error", f"Error: {result['error']}", result
+                        elif result.get("status") == "completed":
+                            return "completed", "Analysis completed successfully!", result
+                
+                if status == "processing":
+                    return status, "Analysis in progress... This may take a few minutes.", None
+                
+                return status, "Ready to analyze.", None
+            
+            def update_results(result_data):
+                """Update the UI with results."""
+                if not result_data:
+                    return ("No results available yet.", 
+                            "", 
+                            "No detailed report available yet.",
+                            "No text analysis available yet.",
+                            [], 
+                            "No image analysis available yet.",
+                            None)
+                
+                # Extract results
+                exec_summary_text = "No executive summary available."
+                confidence_text = ""
+                detailed_report_text = "No detailed report available."
+                text_analysis_text = "No text analysis available."
+                image_list = []
+                image_analysis_text = "No image analysis available."
+                
+                # Process report data
+                if "report" in result_data:
+                    report = result_data["report"]
+                    
+                    # Executive summary
+                    if "executive_summary" in report:
+                        exec_summary_text = report["executive_summary"]
+                    
+                    # Confidence statement
+                    if "confidence_statement" in report:
+                        confidence_level = report.get("confidence_level", "unknown")
+                        confidence_text = f"**Confidence Level: {confidence_level.title()}**\n\n"
+                        confidence_text += report["confidence_statement"]
+                    
+                    # Detailed report
+                    if "detailed_report" in report:
+                        detailed_report_text = report["detailed_report"]
+                
+                # Process text analysis
+                if "results" in result_data and "text_analysis" in result_data["results"]:
+                    text_data = result_data["results"]["text_analysis"]
+                    
+                    if "document_analyses" in text_data:
+                        text_analysis_text = f"### Text Analysis Results\n\n"
+                        text_analysis_text += f"Found {text_data.get('relevant_documents', 0)} relevant documents out of {text_data.get('total_documents', 0)}.\n\n"
+                        
+                        for i, doc in enumerate(text_data["document_analyses"]):
+                            text_analysis_text += f"#### Document {i+1}: {doc.get('filename', 'Unknown')}\n\n"
+                            text_analysis_text += f"Relevance: {doc.get('relevance_score', 0):.2f}\n\n"
+                            text_analysis_text += f"{doc.get('summary', 'No summary available.')}\n\n"
+                
+                # Process image analysis
+                if "results" in result_data and "image_analysis" in result_data["results"]:
+                    img_data = result_data["results"]["image_analysis"]
+                    
+                    if "image_analyses" in img_data:
+                        image_analysis_text = f"### Image Analysis Results\n\n"
+                        image_analysis_text += f"Found {img_data.get('relevant_images', 0)} relevant images out of {img_data.get('total_images', 0)}.\n\n"
+                        
+                        # Get processed images for gallery
+                        if "processed_images" in img_data:
+                            for img_info in img_data["processed_images"]:
+                                if img_info.get("is_relevant", False):
+                                    try:
+                                        img_path = img_info.get("filepath", "")
+                                        if os.path.exists(img_path):
+                                            # Add to gallery
+                                            image_list.append((img_path, img_info.get("caption", "No caption")))
+                                    except Exception as e:
+                                        print(f"Error loading image: {e}")
+                        
+                        # Format analysis text
+                        for i, img in enumerate(img_data["image_analyses"]):
+                            image_analysis_text += f"#### Image {i+1}: {img.get('filename', 'Unknown')}\n\n"
+                            image_analysis_text += f"Caption: {img.get('caption', 'No caption available.')}\n\n"
+                            image_analysis_text += f"Relevance: {img.get('relevance_score', 0):.2f}\n\n"
+                            image_analysis_text += f"Model used: {img.get('model_used', 'unknown')}\n\n"
+                
+                return (exec_summary_text, 
+                        confidence_text, 
+                        detailed_report_text,
+                        text_analysis_text,
+                        image_list, 
+                        image_analysis_text,
+                        result_data)
+            
+            def update_metrics():
+                """Update sustainability metrics display."""
+                metrics = self.orchestrator.get_sustainability_metrics()
+                
+                if "error" in metrics:
+                    return "No metrics available: " + metrics["error"], None
+                
+                # Format metrics for display
+                metrics_text = "### Sustainability Metrics\n\n"
+                
+                # Energy usage
+                energy_usage = metrics.get("energy_usage", {}).get("total", 0)
+                metrics_text += f"**Total Energy Usage**: {energy_usage:.6f} Wh\n\n"
+                
+                # Carbon footprint
+                carbon = metrics.get("carbon_footprint_kg", 0)
+                metrics_text += f"**Carbon Footprint**: {carbon:.6f} kg CO₂\n\n"
+                
+                # Optimization gains
+                opt_gains = metrics.get("optimization_gains", {})
+                tokens_saved = opt_gains.get("tokens_saved", 0)
+                tokens_saved_pct = opt_gains.get("tokens_saved_pct", 0)
+                energy_saved = opt_gains.get("total_energy_saved", 0)
+                
+                metrics_text += f"**Tokens Saved**: {tokens_saved} ({tokens_saved_pct:.1f}%)\n\n"
+                metrics_text += f"**Energy Saved**: {energy_saved:.6f} Wh\n\n"
+                
+                # Environmental equivalents
+                env_equiv = metrics.get("environmental_equivalents", {})
+                if env_equiv:
+                    metrics_text += "### Environmental Impact\n\n"
+                    for impact, value in env_equiv.items():
+                        name = impact.replace("_", " ").title()
+                        metrics_text += f"**{name}**: {value:.2f}\n\n"
+                
+                # Create chart
+                fig, ax = plt.subplots(figsize=(6, 4))
+                
+                # Energy by model
+                energy_by_model = metrics.get("energy_usage", {}).get("by_model", {})
+                if energy_by_model:
+                    models = list(energy_by_model.keys())
+                    values = list(energy_by_model.values())
+                    
+                    # Shorten model names for display
+                    short_names = [m.split("/")[-1] if "/" in m else m for m in models]
+                    
+                    ax.bar(short_names, values)
+                    ax.set_ylabel("Energy (Wh)")
+                    ax.set_title("Energy Usage by Model")
+                    plt.xticks(rotation=45, ha="right")
+                    plt.tight_layout()
+                
+                return metrics_text, fig
+            
+            # Connect event handlers
+            session_id = gr.on_load(initialize_session)[0]
+            
+            analyze_btn.click(
+                process_files,
+                inputs=[session_id, topic_input, text_files, image_files, processing_status],
+                outputs=[session_id, processing_status, status_msg, result_data]
+            )
+            
+            # Periodic status check
+            gr.on(
+                "change",
+                lambda s, st: check_status(s, st),
+                inputs=[session_id, processing_status],
+                outputs=[processing_status, status_msg, result_data],
+                every=2  # Check every 2 seconds
+            )
+            
+            # Update results when result_data changes
+            result_data.change(
+                update_results,
+                inputs=[result_data],
+                outputs=[exec_summary, confidence_indicator, detailed_report, text_analysis, 
+                         image_gallery, image_analysis, raw_json]
+            )
+            
+            # Update metrics
+            update_metrics_btn.click(
+                update_metrics,
+                inputs=[],
+                outputs=[metrics_display, metrics_chart]
+            )
+            
+        return interface
+    
+    def launch(self, **kwargs):
+        """Launch the Gradio interface."""
+        interface = self.create_interface()
+        interface.launch(**kwargs)
+        
+    def cleanup(self):
+        """Clean up temporary files."""
+        try:
+            shutil.rmtree(self.temp_dir)
+        except Exception as e:
+            print(f"Error cleaning up temp files: {e}")