Enhanced UI with dark mode, streaming progress, and updated dependencies

app.py

@@ -11,6 +11,7 @@ import psutil
 import plotly.express as px
 import plotly.graph_objects as go
 import pandas as pd
+from functools import partial
 
 from model import RadarDetectionModel
 from feature_extraction import (calculate_amplitude, classify_amplitude,
@@ -23,7 +24,7 @@ from utils import plot_detection
 from database import save_report, get_report_history
 
 # Set theme and styling
-THEME = gr.themes.Soft(
+LIGHT_THEME = gr.themes.Soft(
     primary_hue="blue",
     secondary_hue="indigo",
     neutral_hue="slate",
@@ -31,6 +32,21 @@ THEME = gr.themes.Soft(
     text_size=gr.themes.sizes.text_md,
 )
 
+DARK_THEME = gr.themes.Soft(
+    primary_hue="blue",
+    secondary_hue="indigo",
+    neutral_hue="slate",
+    radius_size=gr.themes.sizes.radius_sm,
+    text_size=gr.themes.sizes.text_md,
+).set(
+    body_background_fill="*neutral_950",
+    background_fill_primary="*neutral_900",
+    background_fill_secondary="*neutral_800",
+    text_color="*neutral_200",
+    color_accent_soft="*primary_800",
+    border_color_accent_subdued="*primary_700",
+)
+
 class TechnicalReportGenerator:
     def __init__(self):
         self.timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -235,11 +251,38 @@ def create_feature_radar_chart(features):
     
     return fig
 
-def process_image(image, generate_tech_report=False):
+def create_heatmap(image_array):
+    """Create a heatmap visualization of the image intensity"""
+    if image_array is None:
+        return None
+    
+    # Convert to grayscale if needed
+    if len(image_array.shape) == 3 and image_array.shape[2] == 3:
+        gray_img = np.mean(image_array, axis=2)
+    else:
+        gray_img = image_array
+    
+    fig = px.imshow(
+        gray_img,
+        color_continuous_scale='inferno',
+        title='Signal Intensity Heatmap'
+    )
+    
+    fig.update_layout(
+        xaxis_title='X Position',
+        yaxis_title='Y Position',
+        template='plotly_white'
+    )
+    
+    return fig
+
+def process_image_streaming(image, generate_tech_report=False, progress=gr.Progress()):
+    """Process image with streaming progress updates"""
     if image is None:
         raise gr.Error("Please upload an image.")
     
     # Initialize model if needed
+    progress(0.1, desc="Initializing model...")
     global model
     model, error = initialize_model()
     if error:
@@ -251,9 +294,11 @@ def process_image(image, generate_tech_report=False):
             image = Image.fromarray(image)
         
         # Run detection
+        progress(0.2, desc="Running detection...")
         detection_result = model.detect(image)
         
         # Extract features
+        progress(0.3, desc="Extracting features...")
         np_image = np.array(image)
         amplitude = calculate_amplitude(np_image)
         amplitude_class = classify_amplitude(amplitude)
@@ -279,14 +324,17 @@ def process_image(image, generate_tech_report=False):
         }
         
         # Create visualization charts
+        progress(0.5, desc="Creating visualizations...")
         confidence_chart = create_confidence_chart(
             detection_result.get('scores', []), 
             detection_result.get('labels', [])
         )
         
         feature_chart = create_feature_radar_chart(features)
+        heatmap = create_heatmap(np_image)
         
         # Start performance tracking
+        progress(0.6, desc="Analyzing performance...")
         start_time = time.time()
         performance_data = {
             'pipeline_stats': {},
@@ -336,6 +384,7 @@ def process_image(image, generate_tech_report=False):
         performance_data['gpu_util'] = get_gpu_utilization()
         
         # Generate analysis report
+        progress(0.8, desc="Generating reports...")
         analysis_report = generate_report(detection_result, features)
         
         # Prepare output
@@ -357,10 +406,12 @@ def process_image(image, generate_tech_report=False):
             report_path = "temp_tech_report.md"
             with open(report_path, "w") as f:
                 f.write(tech_report)
-                
-            return output_image, analysis_report, report_path, confidence_chart, feature_chart
             
-        return output_image, analysis_report, None, confidence_chart, feature_chart
+            progress(1.0, desc="Analysis complete!")    
+            return output_image, analysis_report, report_path, confidence_chart, feature_chart, heatmap
+        
+        progress(1.0, desc="Analysis complete!")
+        return output_image, analysis_report, None, confidence_chart, feature_chart, heatmap
         
     except Exception as e:
         error_msg = f"Error processing image: {str(e)}"
@@ -404,9 +455,24 @@ def get_gpu_utilization():
         pass
     return 0
 
+def toggle_dark_mode():
+    """Toggle between light and dark themes"""
+    current_theme = getattr(toggle_dark_mode, "current_theme", "light")
+    if current_theme == "light":
+        toggle_dark_mode.current_theme = "dark"
+        return DARK_THEME
+    else:
+        toggle_dark_mode.current_theme = "light"
+        return LIGHT_THEME
+
 # Create Gradio interface
-with gr.Blocks(theme=THEME) as iface:
-    gr.Markdown("# Radar Image Analysis System")
+with gr.Blocks(theme=LIGHT_THEME) as iface:
+    theme_state = gr.State(LIGHT_THEME)
+    
+    with gr.Row():
+        gr.Markdown("# Radar Image Analysis System")
+        dark_mode_btn = gr.Button("🌓 Toggle Dark Mode", scale=0)
+    
     gr.Markdown("Upload a radar image to analyze defects and generate technical reports")
     
     with gr.Tabs() as tabs:
@@ -417,7 +483,9 @@ with gr.Blocks(theme=THEME) as iface:
                         input_image = gr.Image(
                             type="pil", 
                             label="Upload Radar Image",
-                            elem_id="input-image"
+                            elem_id="input-image",
+                            sources=["upload", "webcam", "clipboard"],
+                            tool="editor"
                         )
                         tech_report_checkbox = gr.Checkbox(
                             label="Generate Technical Report", 
@@ -460,6 +528,12 @@ with gr.Blocks(theme=THEME) as iface:
                         label="Feature Analysis",
                         elem_id="feature-plot"
                     )
+            
+            with gr.Row():
+                heatmap_plot = gr.Plot(
+                    label="Signal Intensity Heatmap",
+                    elem_id="heatmap-plot"
+                )
         
         with gr.TabItem("History", id="history"):
             with gr.Row():
@@ -483,6 +557,11 @@ with gr.Blocks(theme=THEME) as iface:
             
             This system uses PaliGemma, a vision-language model that combines SigLIP-So400m (image encoder) and Gemma-2B (text decoder) for joint object detection and multimodal analysis.
             
+            ## Keyboard Shortcuts
+            
+            - **Ctrl+A**: Trigger analysis
+            - **Ctrl+D**: Toggle dark mode
+            
             ## Troubleshooting
             
             - If the analysis fails, try uploading a different image format
@@ -491,10 +570,17 @@ with gr.Blocks(theme=THEME) as iface:
             """)
     
     # Set up event handlers
+    dark_mode_btn.click(
+        fn=toggle_dark_mode,
+        inputs=[],
+        outputs=[iface],
+        api_name="toggle_theme"
+    )
+    
     analyze_button.click(
-        fn=process_image,
+        fn=process_image_streaming,
         inputs=[input_image, tech_report_checkbox],
-        outputs=[output_image, output_report, tech_report_output, confidence_plot, feature_plot],
+        outputs=[output_image, output_report, tech_report_output, confidence_plot, feature_plot, heatmap_plot],
         api_name="analyze"
     )
     
@@ -512,6 +598,9 @@ with gr.Blocks(theme=THEME) as iface:
                 if (e.key === 'a' && e.ctrlKey) {
                     document.getElementById('analyze-btn').click();
                 }
+                if (e.key === 'd' && e.ctrlKey) {
+                    document.querySelector('button:contains("Toggle Dark Mode")').click();
+                }
             });
         }
     """)

requirements.txt

@@ -1,16 +1,22 @@
 gradio>=5.18.0
-torch>=2.1.0
-transformers>=4.36.0
-Pillow>=10.1.0
-numpy>=1.26.0
-matplotlib>=3.8.0
-pandas>=2.1.0
-sqlalchemy>=2.0.23
+torch>=2.1.2
+transformers>=4.37.2
+Pillow>=10.2.0
+numpy>=1.26.3
+matplotlib>=3.8.2
+pandas>=2.1.4
+sqlalchemy>=2.0.25
 plotly>=5.18.0
 scikit-learn>=1.3.2
-jinja2>=3.1.2
-huggingface-hub>=0.19.4
+jinja2>=3.1.3
+huggingface-hub>=0.20.2
 python-dotenv>=1.0.0
 markdown>=3.5.1
 psutil>=5.9.6
-tqdm>=4.66.1
+tqdm>=4.66.1
+accelerate>=0.25.0
+safetensors>=0.4.1
+peft>=0.7.1
+optimum>=1.14.0
+colorama>=0.4.6
+rich>=13.7.0