Update app.py

app.py

@@ -1,16 +1,14 @@
 # Face Detection-Based AI Automation of Lab Tests
-# Gradio App with OpenCV + MediaPipe + rPPG Integration for Hugging Face Spaces
+# Gradio App with Mobile-Responsive UI and Risk-Level Coloring
 
 import gradio as gr
 import cv2
 import numpy as np
 import mediapipe as mp
 
-# Setup Mediapipe Face Mesh
 mp_face_mesh = mp.solutions.face_mesh
 face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, refine_landmarks=True, min_detection_confidence=0.5)
 
-# Function to calculate mean green intensity (simplified rPPG)
 def estimate_heart_rate(frame, landmarks):
     h, w, _ = frame.shape
     forehead_pts = [landmarks[10], landmarks[338], landmarks[297], landmarks[332]]
@@ -19,19 +17,56 @@ def estimate_heart_rate(frame, landmarks):
     cv2.fillConvexPoly(mask, pts, 255)
     green_channel = cv2.split(frame)[1]
     mean_intensity = cv2.mean(green_channel, mask=mask)[0]
-    heart_rate = int(60 + 30 * np.sin(mean_intensity / 255.0 * np.pi))  # Simulated
+    heart_rate = int(60 + 30 * np.sin(mean_intensity / 255.0 * np.pi))
     return heart_rate
 
-# Estimate SpO2 and Respiratory Rate (simulated based on heart rate)
 def estimate_spo2_rr(heart_rate):
     spo2 = min(100, max(90, 97 + (heart_rate % 5 - 2)))
     rr = int(12 + abs(heart_rate % 5 - 2))
     return spo2, rr
 
-# Main analysis function
+def get_risk_color(value, normal_range):
+    low, high = normal_range
+    if value < low:
+        return "🔻 LOW"
+    elif value > high:
+        return "🔺 HIGH"
+    else:
+        return "✅ Normal"
+
+def generate_flags_extended(params):
+    hb, wbc, platelets, iron, ferritin, tibc, bilirubin, creatinine, tsh, cortisol, fbs, hba1c = params
+    flags = []
+    if hb < 13.5:
+        flags.append("Hemoglobin Low - Possible Anemia")
+    if wbc < 4.0 or wbc > 11.0:
+        flags.append("Abnormal WBC Count - Possible Infection")
+    if platelets < 150:
+        flags.append("Platelet Drop Risk - Bruising Possible")
+    if iron < 60:
+        flags.append("Iron Deficiency Detected")
+    if ferritin < 30:
+        flags.append("Low Ferritin - Iron Store Low")
+    if tibc > 400:
+        flags.append("High TIBC - Iron Absorption Issue")
+    if bilirubin > 1.2:
+        flags.append("Jaundice Detected - Elevated Bilirubin")
+    if creatinine > 1.2:
+        flags.append("Kidney Function Concern - High Creatinine")
+    if tsh < 0.4 or tsh > 4.0:
+        flags.append("Thyroid Imbalance - Check TSH")
+    if cortisol < 5 or cortisol > 25:
+        flags.append("Stress Hormone Abnormality - Cortisol")
+    if fbs > 110:
+        flags.append("High Fasting Blood Sugar")
+    if hba1c > 5.7:
+        flags.append("Elevated HbA1c - Diabetes Risk")
+    flags.append("Mood / Stress analysis requires separate behavioral model")
+    return flags
+
 def analyze_face(image):
     if image is None:
-        return {"error": "No image provided"}, None
+        return {}, None
 
     frame_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     result = face_mesh.process(frame_rgb)
@@ -41,25 +76,90 @@ def analyze_face(image):
         heart_rate = estimate_heart_rate(frame_rgb, landmarks)
         spo2, rr = estimate_spo2_rr(heart_rate)
 
-        report = {
-            "Hemoglobin": "12.3 g/dL (Estimated)",
-            "SpO2": f"{spo2}%",
-            "Heart Rate": f"{heart_rate} bpm",
-            "Blood Pressure": "Low",
-            "Respiratory Rate": f"{rr} breaths/min",
-            "Risk Flags": ["Anemia Mild", "Hydration Low"]
+        hb, wbc, platelets = 12.3, 6.4, 210
+        iron, ferritin, tibc = 55, 45, 340
+        bilirubin, creatinine = 1.5, 1.3
+        tsh, cortisol = 2.5, 18
+        fbs, hba1c = 120, 6.2
+
+        flags = generate_flags_extended([hb, wbc, platelets, iron, ferritin, tibc, bilirubin, creatinine, tsh, cortisol, fbs, hba1c])
+
+        sections = {
+            "🩸 Hematology": [
+                f"Hemoglobin (Hb): {hb} g/dL - {get_risk_color(hb, (13.5, 17.5))}",
+                f"WBC Count: {wbc} x10^3/uL - {get_risk_color(wbc, (4.0, 11.0))}",
+                f"Platelet Count: {platelets} x10^3/uL - {get_risk_color(platelets, (150, 450))}"
+            ],
+            "🧬 Iron & Liver Panel": [
+                f"Iron: {iron} µg/dL - {get_risk_color(iron, (60, 170))}",
+                f"Ferritin: {ferritin} ng/mL - {get_risk_color(ferritin, (30, 300))}",
+                f"TIBC: {tibc} µg/dL - {get_risk_color(tibc, (250, 400))}",
+                f"Bilirubin: {bilirubin} mg/dL - {get_risk_color(bilirubin, (0.3, 1.2))}"
+            ],
+            "🧪 Kidney, Thyroid & Stress": [
+                f"Creatinine: {creatinine} mg/dL - {get_risk_color(creatinine, (0.6, 1.2))}",
+                f"TSH: {tsh} µIU/mL - {get_risk_color(tsh, (0.4, 4.0))}",
+                f"Cortisol: {cortisol} µg/dL - {get_risk_color(cortisol, (5, 25))}"
+            ],
+            "🧁 Metabolic Panel": [
+                f"Fasting Blood Sugar: {fbs} mg/dL - {get_risk_color(fbs, (70, 110))}",
+                f"HbA1c: {hba1c}% - {get_risk_color(hba1c, (4.0, 5.7))}"
+            ],
+            "❤️ Vital Signs": [
+                f"SpO2: {spo2}% - {get_risk_color(spo2, (95, 100))}",
+                f"Heart Rate: {heart_rate} bpm - {get_risk_color(heart_rate, (60, 100))}",
+                f"Respiratory Rate: {rr} breaths/min - {get_risk_color(rr, (12, 20))}",
+                "Blood Pressure: Low (simulated)"
+            ],
+            "⚠️ Risk Flags": flags
         }
-        return report, frame_rgb
+        return sections, frame_rgb
     else:
-        return {"error": "Face not detected"}, None
-
-# Launch UI
-demo = gr.Interface(
-    fn=analyze_face,
-    inputs=gr.Image(type="numpy", label="Upload a Face Image"),
-    outputs=[gr.JSON(label="AI Diagnostic Report"), gr.Image(label="Annotated Image")],
-    title="Face-Based AI Lab Test Automation",
-    description="Upload a face image to estimate basic vital signs and lab test indicators using AI-based visual inference."
-)
+        return {"⚠️ Error": ["Face not detected"]}, None
+
+# Mobile-optimized UI with styled labels
+demo = gr.Blocks(css="""
+@media only screen and (max-width: 768px) {
+  .gr-block.gr-column { width: 100% !important; }
+}
+""")
+with demo:
+    gr.Markdown("""
+    # 🧠 Face-Based AI Lab Test Inference
+    Upload a clear face image to simulate categorized lab reports with visual grouping.
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="numpy", label="📸 Upload a Face Image")
+            submit_btn = gr.Button("🔍 Analyze Now")
+        with gr.Column(scale=2):
+            accordion_output = gr.Accordion("📂 Diagnostic Summary", open=True)
+            with accordion_output:
+                result_html = gr.HighlightedText(label="📊 Grouped Report", combine_adjacent=True)
+            result_image = gr.Image(label="🧍 Annotated Face Scan")
+
+    def format_report(sections):
+        lines = []
+        for title, values in sections.items():
+            lines.append((f"{title}",))
+            for item in values:
+                lines.append((f" - {item}",))
+        return lines
+
+    submit_btn.click(
+        fn=analyze_face,
+        inputs=image_input,
+        outputs=[result_html, result_image],
+        preprocess=False,
+        postprocess=False,
+        _js="(data) => [data]"
+    ).then(
+        fn=format_report,
+        inputs=None,
+        outputs=result_html
+    )
+
+    gr.Markdown("---\n✅ Optimized for Mobile · Risk Indicators: 🔻 Low, 🔺 High, ✅ Normal")
 
 demo.launch()