Update app.py

app.py

@@ -81,6 +81,47 @@ def build_table(title, rows):
     html += '</tbody></table></div>'
     return html
 
+def analyze_video(video_path):
+    import matplotlib.pyplot as plt
+    from PIL import Image
+    cap = cv2.VideoCapture(video_path)
+    brightness_vals = []
+    green_vals = []
+    frame_sample = None
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if frame_sample is None:
+            frame_sample = frame.copy()
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        green = frame[:, :, 1]
+        brightness_vals.append(np.mean(gray))
+        green_vals.append(np.mean(green))
+    cap.release()
+    # simulate HR via std deviation signal
+    brightness_std = np.std(brightness_vals) / 255
+    green_std = np.std(green_vals) / 255
+    tone_index = np.mean(frame_sample[100:150, 100:150]) / 255 if frame_sample[100:150, 100:150].size else 0.5
+    hr_features = [brightness_std, green_std, tone_index]
+    heart_rate = float(np.clip(hr_model.predict([hr_features])[0], 60, 100))
+    skin_tone_index = np.mean(frame_sample[100:150, 100:150]) / 255 if frame_sample[100:150, 100:150].size else 0.5
+    brightness_variation = np.std(cv2.cvtColor(frame_sample, cv2.COLOR_BGR2GRAY)) / 255
+    spo2_features = [heart_rate, brightness_variation, skin_tone_index]
+    spo2 = spo2_model.predict([spo2_features])[0]
+    rr = int(12 + abs(heart_rate % 5 - 2))
+    plt.figure(figsize=(6, 2))
+    plt.plot(brightness_vals, label='rPPG Signal')
+    plt.title("Simulated rPPG Signal")
+    plt.xlabel("Frame")
+    plt.ylabel("Brightness")
+    plt.legend()
+    plt.tight_layout()
+    plot_path = "/tmp/ppg_plot.png"
+    plt.savefig(plot_path)
+    plt.close()
+    return f"<h4>❤️ HR: {heart_rate:.2f} bpm, SpO₂: {spo2:.2f}%, RR: {rr} bpm</h4><img src='file/{plot_path}' width='100%'>", cv2.cvtColor(frame_sample, cv2.COLOR_BGR2RGB)
+
 def analyze_face(image):
     if image is None:
         return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
@@ -106,7 +147,7 @@ def analyze_face(image):
     brightness_std = np.std(gray) / 255
     tone_index = np.mean(frame_rgb[100:150, 100:150]) / 255 if frame_rgb[100:150, 100:150].size else 0.5
     hr_features = [brightness_std, green_std, tone_index]
-    heart_rate = hr_model.predict([hr_features])[0]
+    heart_rate = float(np.clip(hr_model.predict([hr_features])[0], 60, 100))
     skin_patch = frame_rgb[100:150, 100:150]
     skin_tone_index = np.mean(skin_patch) / 255 if skin_patch.size else 0.5
     brightness_variation = np.std(cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2GRAY)) / 255
@@ -144,17 +185,18 @@ def analyze_face(image):
 
 with gr.Blocks() as demo:
     gr.Markdown("""
-    # 🧠 Face-Based Lab Test AI Report
-    Upload a face photo to infer health diagnostics with AI-based visual markers.
+    # 🧠 Face-Based Lab Test AI Report (Video Mode)
+    Upload a short face video (10–30s) to infer health diagnostics using rPPG analysis.
     """)
     with gr.Row():
         with gr.Column():
-            image_input = gr.Image(type="numpy", label="📸 Upload Face Image")
+            video_input = gr.Video(label="📽 Upload Face Video", sources=["upload", "webcam"], type="filepath")
             submit_btn = gr.Button("🔍 Analyze")
         with gr.Column():
             result_html = gr.HTML(label="🧪 Health Report Table")
-            result_image = gr.Image(label="📷 Face Scan Annotated")
-    submit_btn.click(fn=analyze_face, inputs=image_input, outputs=[result_html, result_image])
-    gr.Markdown("---\n✅ Table Format • AI Prediction • Dynamic Summary • Multilingual Support • CTA")
+            result_image = gr.Image(label="📷 Key Frame Snapshot")
+    submit_btn.click(fn=analyze_face, inputs=video_input, outputs=[result_html, result_image])
+    gr.Markdown("---
+✅ Table Format • AI Prediction • rPPG-based HR • Dynamic Summary • Multilingual Support • CTA")
 
 demo.launch()