Update app.py

app.py

@@ -1,15 +1,16 @@
-# Enhanced Face-Based Lab Test Predictor with AI Models for 30 Lab Metrics
-
 import gradio as gr
 import cv2
 import numpy as np
 import mediapipe as mp
 from sklearn.linear_model import LinearRegression
 import random
+import joblib
 
+# Setup for Face Mesh detection
 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 extract color features from the image
 def extract_features(image, landmarks):
     red_channel = image[:, :, 2]
     green_channel = image[:, :, 1]
@@ -21,6 +22,7 @@ def extract_features(image, landmarks):
 
     return [red_percent, green_percent, blue_percent]
 
+# Mock models training (for demonstration)
 def train_model(output_range):
     X = [[random.uniform(0.2, 0.5), random.uniform(0.05, 0.2), random.uniform(0.05, 0.2),
           random.uniform(0.2, 0.5), random.uniform(0.2, 0.5), random.uniform(0.2, 0.5),
@@ -29,14 +31,12 @@ def train_model(output_range):
     model = LinearRegression().fit(X, y)
     return model
 
-import joblib
+# Load pre-trained models for Hemoglobin, SPO2, and Heart Rate
 hemoglobin_model = joblib.load("hemoglobin_model_from_anemia_dataset.pkl")
-
-hemoglobin_r2 = 0.385
-import joblib
 spo2_model = joblib.load("spo2_model_simulated.pkl")
 hr_model = joblib.load("heart_rate_model.pkl")
 
+# Model dictionary setup for other tests
 models = {
     "Hemoglobin": hemoglobin_model,
     "WBC Count": train_model((4.0, 11.0)),
@@ -59,6 +59,7 @@ models = {
     "Temperature": train_model((97, 99))
 }
 
+# Function to determine risk level
 def get_risk_color(value, normal_range):
     low, high = normal_range
     if value < low:
@@ -68,6 +69,7 @@ def get_risk_color(value, normal_range):
     else:
         return ("Normal", "✅", "#CCFFCC")
 
+# Function to build an HTML table for displaying test results
 def build_table(title, rows):
     html = (
         f'<div style="margin-bottom: 24px;">'
@@ -81,9 +83,9 @@ def build_table(title, rows):
     html += '</tbody></table></div>'
     return html
 
+# Analyzing video for health metrics
 def analyze_video(video_path):
     import matplotlib.pyplot as plt
-    from PIL import Image
     cap = cv2.VideoCapture(video_path)
     brightness_vals = []
     green_vals = []
@@ -99,135 +101,26 @@ def analyze_video(video_path):
         brightness_vals.append(np.mean(gray))
         green_vals.append(np.mean(green))
     cap.release()
-    # simulate HR via std deviation signal
+    
+    # Simulate heart rate and SPO2 estimation
     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()
-    # Reuse frame_sample for full analysis
-    frame_rgb = cv2.cvtColor(frame_sample, cv2.COLOR_BGR2RGB)
-    result = face_mesh.process(frame_rgb)
-    if not result.multi_face_landmarks:
-        return "<div style='color:red;'>⚠️ Face not detected in video.</div>", frame_rgb
-    landmarks = result.multi_face_landmarks[0].landmark
-    features = extract_features(frame_rgb, landmarks)
-    test_values = {}
-    r2_scores = {}
-    for label in models:
-        if label == "Hemoglobin":
-            prediction = models[label].predict([features])[0]
-            test_values[label] = prediction
-            r2_scores[label] = hemoglobin_r2
-        else:
-            value = models[label].predict([[random.uniform(0.2, 0.5) for _ in range(7)]])[0]
-            test_values[label] = value
-            r2_scores[label] = 0.0
-    html_output = "".join([
-        f'<div style="font-size:14px;color:#888;margin-bottom:10px;">Hemoglobin R² Score: {r2_scores.get("Hemoglobin", "NA"):.2f}</div>',
-        build_table("🩸 Hematology", [("Hemoglobin", test_values["Hemoglobin"], (13.5, 17.5)), ("WBC Count", test_values["WBC Count"], (4.0, 11.0)), ("Platelet Count", test_values["Platelet Count"], (150, 450))]),
-        build_table("🧬 Iron Panel", [("Iron", test_values["Iron"], (60, 170)), ("Ferritin", test_values["Ferritin"], (30, 300)), ("TIBC", test_values["TIBC"], (250, 400))]),
-        build_table("🧬 Liver & Kidney", [("Bilirubin", test_values["Bilirubin"], (0.3, 1.2)), ("Creatinine", test_values["Creatinine"], (0.6, 1.2)), ("Urea", test_values["Urea"], (7, 20))]),
-        build_table("🧪 Electrolytes", [("Sodium", test_values["Sodium"], (135, 145)), ("Potassium", test_values["Potassium"], (3.5, 5.1))]),
-        build_table("🧁 Metabolic & Thyroid", [("FBS", test_values["FBS"], (70, 110)), ("HbA1c", test_values["HbA1c"], (4.0, 5.7)), ("TSH", test_values["TSH"], (0.4, 4.0))]),
-        build_table("❤️ Vitals", [("SpO2", spo2, (95, 100)), ("Heart Rate", heart_rate, (60, 100)), ("Respiratory Rate", rr, (12, 20)), ("Temperature", test_values["Temperature"], (97, 99)), ("BP Systolic", test_values["BP Systolic"], (90, 120)), ("BP Diastolic", test_values["BP Diastolic"], (60, 80))]),
-        build_table("🩹 Other Indicators", [("Cortisol", test_values["Cortisol"], (5, 25)), ("Albumin", test_values["Albumin"], (3.5, 5.5))])
-    ])
-    summary = "<div style='margin-top:20px;padding:12px;border:1px dashed #999;background:#fcfcfc;'>"
-    summary += "<h4>📝 Summary for You</h4><ul>"
-    if test_values["Hemoglobin"] < 13.5:
-        summary += "<li>Your hemoglobin is a bit low — this could mean mild anemia.</li>"
-    if test_values["Iron"] < 60 or test_values["Ferritin"] < 30:
-        summary += "<li>Low iron storage detected — consider an iron profile test.</li>"
-    if test_values["Bilirubin"] > 1.2:
-        summary += "<li>Elevated bilirubin — possible jaundice. Recommend LFT.</li>"
-    if test_values["HbA1c"] > 5.7:
-        summary += "<li>High HbA1c — prediabetes indication. Recommend glucose check.</li>"
-    if spo2 < 95:
-        summary += "<li>Low SpO₂ — suggest retesting with a pulse oximeter.</li>"
-    summary += "</ul><p><strong>💡 Tip:</strong> This is an AI-based estimate. Please follow up with a lab.</p></div>"
-    html_output += summary
-    html_output += "<br><div style='margin-top:20px;padding:12px;border:2px solid #2d87f0;background:#f2faff;text-align:center;border-radius:8px;'>"
-    html_output += "<h4>📞 Book a Lab Test</h4><p>Prefer confirmation? Find certified labs near you.</p>"
-    html_output += "<button style='padding:10px 20px;background:#007BFF;color:#fff;border:none;border-radius:5px;cursor:pointer;'>Find Labs Near Me</button></div>"
-    return html_output, frame_rgb
-
-def analyze_face(image):
-    if image is None:
-        return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
-    frame_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    result = face_mesh.process(frame_rgb)
-    if not result.multi_face_landmarks:
-        return "<div style='color:red;'>⚠️ Error: Face not detected.</div>", None
-    landmarks = result.multi_face_landmarks[0].landmark
-    features = extract_features(frame_rgb, landmarks)
-    test_values = {}
-    r2_scores = {}
-    for label in models:
-        if label == "Hemoglobin":
-            prediction = models[label].predict([features])[0]
-            test_values[label] = prediction
-            r2_scores[label] = hemoglobin_r2
-        else:
-            value = models[label].predict([[random.uniform(0.2, 0.5) for _ in range(7)]])[0]
-            test_values[label] = value
-            r2_scores[label] = 0.0  # simulate other 7D inputs
-    gray = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2GRAY)
-    green_std = np.std(frame_rgb[:, :, 1]) / 255
-    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 = 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
-    spo2_features = [heart_rate, brightness_variation, skin_tone_index]
+    spo2_features = [heart_rate, np.std(brightness_vals), np.mean(frame_sample[100:150, 100:150])]
     spo2 = spo2_model.predict([spo2_features])[0]
-    rr = int(12 + abs(heart_rate % 5 - 2))
+    
+    # Generating the health card with test results
     html_output = "".join([
-        f'<div style="font-size:14px;color:#888;margin-bottom:10px;">Hemoglobin R² Score: {r2_scores.get("Hemoglobin", "NA"):.2f}</div>',
-        build_table("🩸 Hematology", [("Hemoglobin", test_values["Hemoglobin"], (13.5, 17.5)), ("WBC Count", test_values["WBC Count"], (4.0, 11.0)), ("Platelet Count", test_values["Platelet Count"], (150, 450))]),
-        build_table("🧬 Iron Panel", [("Iron", test_values["Iron"], (60, 170)), ("Ferritin", test_values["Ferritin"], (30, 300)), ("TIBC", test_values["TIBC"], (250, 400))]),
-        build_table("🧬 Liver & Kidney", [("Bilirubin", test_values["Bilirubin"], (0.3, 1.2)), ("Creatinine", test_values["Creatinine"], (0.6, 1.2)), ("Urea", test_values["Urea"], (7, 20))]),
-        build_table("🧪 Electrolytes", [("Sodium", test_values["Sodium"], (135, 145)), ("Potassium", test_values["Potassium"], (3.5, 5.1))]),
-        build_table("🧁 Metabolic & Thyroid", [("FBS", test_values["FBS"], (70, 110)), ("HbA1c", test_values["HbA1c"], (4.0, 5.7)), ("TSH", test_values["TSH"], (0.4, 4.0))]),
-        build_table("❤️ Vitals", [("SpO2", spo2, (95, 100)), ("Heart Rate", heart_rate, (60, 100)), ("Respiratory Rate", rr, (12, 20)), ("Temperature", test_values["Temperature"], (97, 99)), ("BP Systolic", test_values["BP Systolic"], (90, 120)), ("BP Diastolic", test_values["BP Diastolic"], (60, 80))]),
-        build_table("🩹 Other Indicators", [("Cortisol", test_values["Cortisol"], (5, 25)), ("Albumin", test_values["Albumin"], (3.5, 5.5))])
+        build_table("🩸 Hematology", [("Hemoglobin", models["Hemoglobin"].predict([hr_features])[0], (13.5, 17.5))]),
+        build_table("🧬 Iron Panel", [("Iron", models["Iron"].predict([hr_features])[0], (60, 170))]),
+        build_table("🧪 Electrolytes", [("Sodium", models["Sodium"].predict([hr_features])[0], (135, 145))]),
+        build_table("❤️ Vitals", [("Heart Rate", heart_rate, (60, 100)), ("SpO2", spo2, (95, 100))]),
     ])
-    summary = "<div style='margin-top:20px;padding:12px;border:1px dashed #999;background:#fcfcfc;'>"
-    summary += "<h4>📝 Summary for You</h4><ul>"
-    if test_values["Hemoglobin"] < 13.5:
-        summary += "<li>Your hemoglobin is a bit low — this could mean mild anemia.</li>"
-    if test_values["Iron"] < 60 or test_values["Ferritin"] < 30:
-        summary += "<li>Low iron storage detected — consider an iron profile test.</li>"
-    if test_values["Bilirubin"] > 1.2:
-        summary += "<li>Elevated bilirubin — possible jaundice. Recommend LFT.</li>"
-    if test_values["HbA1c"] > 5.7:
-        summary += "<li>High HbA1c — prediabetes indication. Recommend glucose check.</li>"
-    if spo2 < 95:
-        summary += "<li>Low SpO₂ — suggest retesting with a pulse oximeter.</li>"
-    summary += "</ul><p><strong>💡 Tip:</strong> This is an AI-based estimate. Please follow up with a lab.</p></div>"
-    html_output += summary
-    html_output += "<br><div style='margin-top:20px;padding:12px;border:2px solid #2d87f0;background:#f2faff;text-align:center;border-radius:8px;'>"
-    html_output += "<h4>📞 Book a Lab Test</h4><p>Prefer confirmation? Find certified labs near you.</p>"
-    html_output += "<button style='padding:10px 20px;background:#007BFF;color:#fff;border:none;border-radius:5px;cursor:pointer;'>Find Labs Near Me</button></div>"
-    return html_output, frame_rgb
+    return html_output
 
+# Gradio Interface setup
 with gr.Blocks() as demo:
     gr.Markdown("""
     # 🧠 Face-Based Lab Test AI Report (Video Mode)
@@ -244,7 +137,7 @@ with gr.Blocks() as demo:
             result_image = gr.Image(label="📷 Key Frame Snapshot")
 
     def route_inputs(mode, image, video):
-        return analyze_video(video) if mode == "Video" else analyze_face(image)
+        return analyze_video(video) if mode == "Video" else analyze_video(image)
 
     submit_btn.click(fn=route_inputs, inputs=[mode_selector, image_input, video_input], outputs=[result_html, result_image])