Update app.py

app.py

@@ -21,17 +21,20 @@ def calculate_hrv(peaks):
 def estimate_respiratory_rate(signal_buffer, fps=30):
     respiration_peaks, _ = find_peaks(signal_buffer, distance=fps * 2)
     if len(respiration_peaks) > 1:
-        return len(respiration_peaks) * 60.0 / (len(signal_buffer) / fps)
+        respiratory_rate = len(respiration_peaks) * 60.0 / (len(signal_buffer) / fps)
+        return respiratory_rate
     return None
 
 def estimate_spo2(signal_buffer):
     green_channel = signal_buffer[:, 1]
     red_channel = signal_buffer[:, 2]
-    return 100 - 5 * (np.mean(red_channel) / np.mean(green_channel))
+    spo2_estimation = 100 - 5 * (np.mean(red_channel) / np.mean(green_channel))
+    return spo2_estimation
 
-def get_heart_rate_from_video(video):
+def get_heart_rate_from_video(video_path):
     face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-    cap = cv2.VideoCapture(video)
+    cap = cv2.VideoCapture(video_path)
+
     buffer_size = 256
     bpm_list = []
     color_buffer = []
@@ -42,62 +45,81 @@ def get_heart_rate_from_video(video):
         ret, frame = cap.read()
         if not ret:
             break
+
         forehead = extract_forehead_region(frame, face_cascade)
         if forehead is not None:
             face_detected = True
             avg_color = np.mean(forehead, axis=(0, 1))
             color_buffer.append(avg_color)
+
         if len(color_buffer) >= buffer_size:
             color_buffer = np.array(color_buffer)
             green_channel = color_buffer[:, 1]
             green_channel = green_channel - np.mean(green_channel)
             signal_buffer.append(green_channel)
             peaks, _ = find_peaks(green_channel, distance=15)
+
             if len(peaks) > 1:
-                avg_interval = np.mean(np.diff(peaks))
+                peak_intervals = np.diff(peaks)
+                avg_interval = np.mean(peak_intervals)
                 avg_interval_sec = avg_interval / 30.0
                 bpm = 60.0 / avg_interval_sec
                 bpm_list.append(bpm)
                 hrv = calculate_hrv(peaks)
+
             color_buffer = []
 
     cap.release()
 
     if not face_detected:
-        return {"Error": "No face detected or unsupported video format."}, None
+        return {"Error": "No face detected or unsupported video."}, None
 
     results = {}
+
     if bpm_list:
         avg_bpm = np.mean(bpm_list)
         adjusted_bpm = avg_bpm - 15
         results['Heart Rate'] = f"{adjusted_bpm:.2f} BPM"
     else:
-        results['Heart Rate'] = "Not detected"
+        results['Heart Rate'] = "No heart rate detected."
 
-    systolic_bp = 120 + 0.5 * (avg_bpm - 60)
-    diastolic_bp = 80 + 0.3 * (avg_bpm - 60)
-    results['Blood Pressure'] = f"{systolic_bp:.2f}/{diastolic_bp:.2f} mmHg"
-    body_temperature = 36.5 if avg_bpm <= 100 else 37.0 + 0.1 * (avg_bpm - 100)
+    if bpm_list:
+        systolic_bp = 120 + 0.5 * (avg_bpm - 60)
+        diastolic_bp = 80 + 0.3 * (avg_bpm - 60)
+        results['Blood Pressure'] = f"{systolic_bp:.2f}/{diastolic_bp:.2f} mmHg"
+    else:
+        results['Blood Pressure'] = "Unavailable"
+
+    if bpm_list and avg_bpm > 100:
+        body_temperature = 37.0 + 0.1 * (avg_bpm - 100)
+    else:
+        body_temperature = 36.5
     results['Body Temperature'] = f"{body_temperature:.2f} °C"
 
     if signal_buffer:
         respiration_signal = np.concatenate(signal_buffer)
-        resp_rate = estimate_respiratory_rate(respiration_signal)
-        results['Respiratory Rate'] = (
-            f"{resp_rate:.2f} breaths/min" if resp_rate else "Not detected"
-        )
-
-    if color_buffer != []:
-        spo2_estimation = estimate_spo2(np.array(color_buffer))
-        results['SpO2'] = f"{spo2_estimation:.2f}%"
-
-    if adjusted_bpm < 60:
-        results['Fatigue/Alertness'] = "Possible fatigue detected."
-    elif adjusted_bpm > 100:
-        results['Fatigue/Alertness'] = "Possible alertness or stress."
-    else:
-        results['Fatigue/Alertness'] = "Normal state detected."
+        respiratory_rate = estimate_respiratory_rate(respiration_signal)
+        if respiratory_rate:
+            results['Respiratory Rate'] = f"{respiratory_rate:.2f} breaths/min"
+        else:
+            results['Respiratory Rate'] = "Unable to estimate"
+
+    if color_buffer:
+        try:
+            spo2_estimation = estimate_spo2(np.array(color_buffer))
+            results['SpO2'] = f"{spo2_estimation:.2f}%"
+        except:
+            results['SpO2'] = "Error estimating SpO2"
 
+    if bpm_list:
+        if adjusted_bpm < 60:
+            results['Fatigue/Alertness'] = "Possible fatigue detected."
+        elif adjusted_bpm > 100:
+            results['Fatigue/Alertness'] = "Possible alertness or stress detected."
+        else:
+            results['Fatigue/Alertness'] = "Normal state detected."
+
+    plot_path = None
     if signal_buffer:
         plt.figure(figsize=(12, 6))
         signal_array = np.concatenate(signal_buffer)
@@ -106,30 +128,34 @@ def get_heart_rate_from_video(video):
         plt.ylabel('Signal Intensity')
         plt.title('Green Channel Signal from Forehead')
         plt.legend()
-        plt.savefig("signal_plot.png")
+        plot_path = "signal_plot.png"
+        plt.savefig(plot_path)
         plt.close()
-        return results, "signal_plot.png"
 
-    return results, None
+    return results, plot_path
 
-def process_video(video):
-    return get_heart_rate_from_video(video)
+def process_video(video_path):
+    return get_heart_rate_from_video(video_path)
 
-objective = "Objective: AI-powered health app that estimates vital signs using face video.\n"
-team_members = (
-    "Team:\n"
-    "- Irfan Jamshed (ID#2697) - engr.irfan@must.edu.pk\n"
-    "- Adnan Munir (ID#2031) - adnanmunir41@yahoo.com\n"
-    "- Muhammad Hannan Rauf (ID#2421) - hananrauf1@gmail.com\n"
-    "- Waseem Hassan (ID#2807) - engr.waseem77@gmail.com\n"
-    "- Dua Javed (ID#647) - duaajaved321@gmail.com\n"
-)
+# Gradio interface
+objective = "Objective: To develop a Gradio-based application that estimates vital signs using facial video.\n\n"
+team_members = """
+Team Members:
+- Irfan Jamshed (ID#2697) - engr.irfan@must.edu.pk
+- Adnan Munir (ID#2031) - adnanmunir41@yahoo.com
+- Muhammad Hannan Rauf (ID#2421) - hananrauf1@gmail.com
+- Waseem Hassan (ID#2807) - engr.waseem77@gmail.com
+- Dua Javed (ID#647) - duaajaved321@gmail.com
+"""
 
 iface = gr.Interface(
     fn=process_video,
-    inputs=gr.Video(label="Upload Facial Video"),
-    outputs=[gr.JSON(label="Vital Signs"), gr.Image(label="Signal Plot")],
-    title="HeartSense: Video-Based Health Monitoring",
+    inputs=gr.Video(label="Upload Video", type="filepath", format="mp4"),
+    outputs=[
+        gr.JSON(label="Vital Signs"),
+        gr.Image(label="Signal Plot")
+    ],
+    title="HeartSense: AI-Based Health Monitoring from Facial Video",
     description=objective + team_members
 )