Update app.py

app.py

@@ -5,6 +5,7 @@ from collections import deque
 import time
 import matplotlib.pyplot as plt
 from scipy import signal
+from scipy.signal import find_peaks
 
 MAX_LEN = 500
 FPS = 30
@@ -56,19 +57,6 @@ def calculate_signal_quality(signal_data):
         quality = 0
     return quality
 
-def find_peaks_simple(signal_data, fps=30, min_distance=0.4):
-    if len(signal_data) < int(fps * 1.0):
-        return []
-    data = np.array(signal_data)
-    data_norm = (data - data.mean()) / (data.std() + 1e-9)
-    min_dist = int(min_distance * fps)
-    peaks = []
-    for i in range(min_dist, len(data_norm) - min_dist):
-        if data_norm[i] > data_norm[i-1] and data_norm[i] > data_norm[i+1]:
-            if not peaks or i - peaks[-1] >= min_dist:
-                peaks.append(i)
-    return peaks
-
 def process_frame(frame, state):
     if frame is None:
         return None, state["red"], state["green"], state["time"]
@@ -108,14 +96,22 @@ def process_frame(frame, state):
     return img, red_list, green_list, time_list
 
 def make_plots(time_list, red_list, green_list):
-    """2-subplot layout like app_old.py"""
+    """Create 2-subplot plot matching app_old.py layout:
+    Left subplot: Red and Green filtered signals overlay
+    Right subplot: Green signal with peak detection
+    """
     if len(time_list) < 10:
         return None
+    
     time_array = np.array(time_list)
     red_array = np.array(red_list)
     green_array = np.array(green_list)
+    
+    # Normalize signals
     red_norm = (red_array - red_array.mean()) / (red_array.std() + 1e-9)
     green_norm = (green_array - green_array.mean()) / (green_array.std() + 1e-9)
+    
+    # Apply bandpass filter
     nyquist = FPS / 2
     low = 0.67 / nyquist
     high = 3.33 / nyquist
@@ -126,27 +122,45 @@ def make_plots(time_list, red_list, green_list):
     else:
         red_filt = red_norm
         green_filt = green_norm
+    
+    # Detect peaks on green channel
+    min_dist = int(0.35 * FPS)
+    prom = 0.3 * np.std(green_filt) if np.std(green_filt) > 0 else 0.1
+    peaks_idx, _ = find_peaks(green_filt, distance=min_dist, prominence=prom)
+    
+    # Estimate HR
     hr_bpm, _ = estimate_heart_rate(green_array, fps=FPS, window_secs=WINDOW_SIZE)
-    peaks_idx = find_peaks_simple(green_filt, fps=FPS)
+    
+    # Create figure with 2 subplots (1 row, 2 columns)
     fig = plt.figure(figsize=(12, 4))
+    
+    # Left subplot: Both red and green signals
     ax1 = fig.add_subplot(1, 2, 1)
-    ax2 = fig.add_subplot(1, 2, 2)
     ax1.plot(time_array, red_filt, label='Red (filtered)', color='red', linewidth=1.5)
     ax1.plot(time_array, green_filt, label='Green (filtered)', color='green', linewidth=1.5)
     ax1.set_xlabel('Time (s)')
     ax1.set_ylabel('Normalized Intensity')
-    ax1.set_title('Filtered PPG Signals (Last 5s)')
+    ax1.set_title('Filtered PPG (last 5s)')
     ax1.legend(loc='upper right')
     ax1.grid(True, alpha=0.3)
+    
+    # Right subplot: Green signal with peaks
+    ax2 = fig.add_subplot(1, 2, 2)
     ax2.plot(time_array, green_filt, label='Green (filtered)', color='green', linewidth=1.5)
     if len(peaks_idx) > 0:
         ax2.scatter(time_array[peaks_idx], green_filt[peaks_idx], color='red', s=50, label='Peaks', zorder=5)
-    hr_title = f'Peak Detection (HR ≈ {hr_bpm:.1f} bpm)' if hr_bpm is not None else 'Peak Detection'
+    
+    # Set title with HR estimate
+    if hr_bpm is not None:
+        ax2.set_title(f'Peaks (HR = {hr_bpm:.1f} bpm)')
+    else:
+        ax2.set_title('Peaks')
+    
     ax2.set_xlabel('Time (s)')
     ax2.set_ylabel('Normalized Intensity')
-    ax2.set_title(hr_title)
     ax2.legend(loc='upper right')
     ax2.grid(True, alpha=0.3)
+    
     fig.tight_layout()
     return fig
 
@@ -156,12 +170,15 @@ def snap(frame, state):
     red_quality = calculate_signal_quality(state["red"])
     green_quality = calculate_signal_quality(state["green"])
     plot_fig = make_plots(time_list, red_list, green_list)
+    
     hr_text = f'{bpm:.1f} bpm' if bpm is not None else 'Measuring...'
     red_quality_text = f'Red: {red_quality:.0f}%'
     green_quality_text = f'Green: {green_quality:.0f}%'
     stats_text = f'Frames: {state["frame_count"]} | Duration: {state["session_duration"]:.1f}s | Samples: {len(state["red"])}/{MAX_LEN}'
+    
     return img, plot_fig, hr_text, red_quality_text, green_quality_text, stats_text, state
 
+
 with gr.Blocks(title="Finger Vital Sign Monitor") as demo:
     gr.Markdown("# Real-time Finger Vital Sign Monitoring")
     gr.Markdown("Place your finger on the camera and keep it steady. The system will continuously monitor PPG signals and estimate heart rate.")