Update app.py

app.py

@@ -9,7 +9,11 @@ import joblib
 
 # Initialize the face mesh model
 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)
+face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True,
+                                  max_num_faces=1,
+                                  refine_landmarks=True,
+                                  min_detection_confidence=0.5)
+
 
 # Functions for feature extraction
 def extract_features(image, landmarks):
@@ -23,18 +27,31 @@ def extract_features(image, landmarks):
 
     return [red_percent, green_percent, blue_percent]
 
+
 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),
-          random.uniform(0.2, 0.5)] for _ in range(100)]
+    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),
+        random.uniform(0.2, 0.5)
+    ] for _ in range(100)]
     y = [random.uniform(*output_range) for _ in X]
     model = LinearRegression().fit(X, y)
     return model
 
+
 # Load models
-hemoglobin_model = joblib.load("hemoglobin_model_from_anemia_dataset.pkl")
-spo2_model = joblib.load("spo2_model_simulated.pkl")
-hr_model = joblib.load("heart_rate_model.pkl")
+try:
+    hemoglobin_model = joblib.load("hemoglobin_model_from_anemia_dataset.pkl")
+    spo2_model = joblib.load("spo2_model_simulated.pkl")
+    hr_model = joblib.load("heart_rate_model.pkl")
+except FileNotFoundError:
+    print(
+        "Error: One or more .pkl model files are missing. Please upload them.")
+    exit(1)
 
 models = {
     "Hemoglobin": hemoglobin_model,
@@ -58,77 +75,152 @@ models = {
     "Temperature": train_model((97, 99))
 }
 
+
 # Helper function for risk level color coding
 def get_risk_color(value, normal_range):
     low, high = normal_range
     if value < low:
-        return ("Low", "🔻", "#FFCCCC")
+        return ("Low", "🔻", "#fff3cd")
     elif value > high:
-        return ("High", "🔺", "#FFE680")
+        return ("High", "🔺", "#f8d7da")
     else:
-        return ("Normal", "✅", "#CCFFCC")
+        return ("Normal", "✅", "#d4edda")
+
 
 # Function to build table for test results
 def build_table(title, rows):
     html = (
-        f'<div style="margin-bottom: 24px;">'
-        f'<h4 style="margin: 8px 0;">{title}</h4>'
-        f'<table style="width:100%; border-collapse:collapse;">'
-        f'<thead><tr style="background:#f0f0f0;"><th style="padding:8px;border:1px solid #ccc;">Test</th><th style="padding:8px;border:1px solid #ccc;">Result</th><th style="padding:8px;border:1px solid #ccc;">Expected Range</th><th style="padding:8px;border:1px solid #ccc;">Level</th></tr></thead><tbody>'
+        f'<div style="margin-bottom: 25px; border-radius: 8px; overflow: hidden; border: 1px solid #e0e0e0;">'
+        f'<div style="background: linear-gradient(135deg, #f5f7fa, #c3cfe2); padding: 12px 16px; border-bottom: 1px solid #e0e0e0;">'
+        f'<h4 style="margin: 0; color: #2c3e50; font-size: 16px; font-weight: 600;">{title}</h4>'
+        f'</div>'
+        f'<table style="width:100%; border-collapse:collapse; background: white;">'
+        f'<thead><tr style="background:#f8f9fa;"><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:left;font-size:13px;">Test</th><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:center;font-size:13px;">Result</th><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:center;font-size:13px;">Range</th><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:center;font-size:13px;">Level</th></tr></thead><tbody>'
     )
-    for label, value, ref in rows:
+    for i, (label, value, ref) in enumerate(rows):
         level, icon, bg = get_risk_color(value, ref)
-        html += f'<tr style="background:{bg};"><td style="padding:6px;border:1px solid #ccc;">{label}</td><td style="padding:6px;border:1px solid #ccc;">{value:.2f}</td><td style="padding:6px;border:1px solid #ccc;">{ref[0]} – {ref[1]}</td><td style="padding:6px;border:1px solid #ccc;">{icon} {level}</td></tr>'
+        row_bg = "#f8f9fa" if i % 2 == 0 else "white"
+        if level != "Normal":
+            row_bg = bg
+        
+        # Format the value with appropriate units
+        if "Count" in label or "Platelet" in label:
+            value_str = f"{value:.0f}"
+        else:
+            value_str = f"{value:.2f}"
+            
+        html += f'<tr style="background:{row_bg};border-bottom:1px solid #e9ecef;"><td style="padding:10px 8px;color:#2c3e50;font-weight:500;">{label}</td><td style="padding:10px 8px;text-align:center;color:#2c3e50;font-weight:600;">{value_str}</td><td style="padding:10px 8px;text-align:center;color:#6c757d;font-size:12px;">{ref[0]} - {ref[1]}</td><td style="padding:10px 8px;text-align:center;font-weight:600;color:{"#28a745" if level == "Normal" else "#dc3545" if level == "High" else "#ffc107"};">{icon} {level}</td></tr>'
     html += '</tbody></table></div>'
     return html
 
+
 # Build health card layout
-def build_health_card(profile_image, test_results, summary):
+def build_health_card(profile_image, test_results, summary, patient_name="", patient_age="", patient_gender="", patient_id=""):
+    from datetime import datetime
+    current_date = datetime.now().strftime("%B %d, %Y")
+    
     html = f"""
-    <div style="font-family: Arial, sans-serif; max-width: 600px; margin: 20px auto; border-radius: 12px; background-color: #f3f8fc; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); padding: 20px; color: #333;">
-        <div style="display: flex; align-items: center; margin-bottom: 20px;">
-            <img src="data:image/png;base64,{profile_image}" alt="Profile" style="width: 80px; height: 80px; border-radius: 50%; margin-right: 15px;">
-            <div>
-                <h2 style="margin: 0; font-size: 24px;">Health Card</h2>
-                <p style="margin: 5px 0; color: #777;">Lab Test Results</p>
+    <div id="health-card" style="font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 700px; margin: 20px auto; border-radius: 16px; background: linear-gradient(135deg, #e3f2fd 0%, #f3e5f5 100%); border: 2px solid #ddd; box-shadow: 0 8px 32px rgba(0, 0, 0, 0.15); padding: 30px; color: #1a1a1a;">
+        
+        <div style="background-color: rgba(255, 255, 255, 0.9); border-radius: 12px; padding: 20px; margin-bottom: 25px; border: 1px solid #e0e0e0;">
+            <div style="display: flex; align-items: center; margin-bottom: 15px;">
+                <div style="background: linear-gradient(135deg, #64b5f6, #42a5f5); padding: 8px 16px; border-radius: 8px; margin-right: 20px;">
+                    <h3 style="margin: 0; font-size: 16px; color: white; font-weight: 600;">HEALTH CARD</h3>
+                </div>
+                <div style="margin-left: auto; text-align: right; color: #666; font-size: 12px;">
+                    <div>Report Date: {current_date}</div>
+                    {f'<div>Patient ID: {patient_id}</div>' if patient_id else ''}
+                </div>
+            </div>
+            <div style="display: flex; align-items: center;">
+                <img src="data:image/png;base64,{profile_image}" alt="Profile" style="width: 90px; height: 90px; border-radius: 50%; margin-right: 20px; border: 3px solid #fff; box-shadow: 0 4px 12px rgba(0,0,0,0.1);">
+                <div>
+                    <h2 style="margin: 0; font-size: 28px; color: #2c3e50; font-weight: 700;">{patient_name if patient_name else "Lab Test Results"}</h2>
+                    <p style="margin: 4px 0 0 0; color: #666; font-size: 14px;">{f"Age: {patient_age} | Gender: {patient_gender}" if patient_age and patient_gender else "AI-Generated Health Analysis"}</p>
+                    <p style="margin: 4px 0 0 0; color: #888; font-size: 12px;">Face-Based Health Analysis Report</p>
+                </div>
             </div>
         </div>
 
-        <div style="font-size: 16px; margin-bottom: 20px;">
-            {test_results['Hematology']}  <!-- Single reference to Hematology -->
+        <div style="background-color: rgba(255, 255, 255, 0.95); border-radius: 12px; padding: 25px; margin-bottom: 25px; border: 1px solid #e0e0e0;">
+            {test_results['Hematology']}
             {test_results['Iron Panel']}
             {test_results['Liver & Kidney']}
             {test_results['Electrolytes']}
             {test_results['Vitals']}
         </div>
 
-        <div style="background-color: #ffffff; padding: 15px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);">
-            <h4 style="margin: 0;">📝 Summary for You</h4>
-            <ul style="margin-top: 10px; color: #555;">
+        <div style="background-color: rgba(255, 255, 255, 0.95); padding: 20px; border-radius: 12px; border: 1px solid #e0e0e0; margin-bottom: 25px;">
+            <h4 style="margin: 0 0 15px 0; color: #2c3e50; font-size: 18px; font-weight: 600;">📝 Summary & Recommendations</h4>
+            <div style="color: #444; line-height: 1.6;">
                 {summary}
-            </ul>
+            </div>
         </div>
 
-        <div style="margin-top: 20px; text-align: center;">
-            <h4>📞 Book a Lab Test</h4>
-            <p style="color: #777;">Prefer confirmation? Find certified labs near you.</p>
-            <button style="padding: 10px 20px; background-color: #007BFF; color: white; border: none; border-radius: 5px; cursor: pointer;">
-                Find Labs Near Me
+        <div style="display: flex; gap: 15px; justify-content: center; flex-wrap: wrap;">
+            <button onclick="window.print()" style="padding: 12px 24px; background: linear-gradient(135deg, #4caf50, #45a049); color: white; border: none; border-radius: 8px; cursor: pointer; font-weight: 600; font-size: 14px; box-shadow: 0 4px 12px rgba(76, 175, 80, 0.3); transition: all 0.3s;">
+                📥 Download Report
+            </button>
+            <button style="padding: 12px 24px; background: linear-gradient(135deg, #2196f3, #1976d2); color: white; border: none; border-radius: 8px; cursor: pointer; font-weight: 600; font-size: 14px; box-shadow: 0 4px 12px rgba(33, 150, 243, 0.3);">
+                📞 Find Labs Near Me
             </button>
         </div>
     </div>
+    
+    <style>
+    @media print {{
+        body * {{
+            visibility: hidden;
+        }}
+        #health-card, #health-card * {{
+            visibility: visible;
+        }}
+        #health-card {{
+            position: absolute;
+            left: 0;
+            top: 0;
+            width: 100% !important;
+            max-width: none !important;
+            margin: 0 !important;
+            box-shadow: none !important;
+            border: none !important;
+        }}
+        button {{
+            display: none !important;
+        }}
+    }}
+    </style>
     """
     return html
 
-# Analyze face and return results
-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)
+
+# Initialize global variable for patient details
+current_patient_details = {'name': '', 'age': '', 'gender': '', 'id': ''}
+
+# Modified analyze_face function
+def analyze_face(input_data):
+    if isinstance(input_data, str):  # Video input (file path in Replit)
+        cap = cv2.VideoCapture(input_data)
+        if not cap.isOpened():
+            return "<div style='color:red;'>⚠️ Error: Could not open video.</div>", None
+        ret, frame = cap.read()
+        cap.release()
+        if not ret:
+            return "<div style='color:red;'>⚠️ Error: Could not read video frame.</div>", None
+    else:  # Image input
+        frame = input_data
+        if frame is None:
+            return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
+
+    # Resize image to reduce processing time
+    frame = cv2.resize(frame, (640, 480))  # Adjust resolution for Replit
+    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    # Provide image dimensions to mediapipe to avoid NORM_RECT warning
     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
+    landmarks = result.multi_face_landmarks[
+        0].landmark  # Fixed: Use integer index
     features = extract_features(frame_rgb, landmarks)
     test_values = {}
     r2_scores = {}
@@ -139,71 +231,122 @@ def analyze_face(image):
             test_values[label] = prediction
             r2_scores[label] = 0.385
         else:
-            value = models[label].predict([[random.uniform(0.2, 0.5) for _ in range(7)]])[0]
+            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
+            r2_scores[label] = 0.0
 
     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
+    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
+    brightness_variation = np.std(cv2.cvtColor(frame_rgb,
+                                               cv2.COLOR_RGB2GRAY)) / 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))
 
-    # Prepare the test results
     test_results = {
-        "Hematology": 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))]),
-        "Iron Panel": build_table("🧬 Iron Panel", [("Iron", test_values["Iron"], (60, 170)),
-                                                 ("Ferritin", test_values["Ferritin"], (30, 300)),
-                                                 ("TIBC", test_values["TIBC"], (250, 400))]),
-        "Liver & Kidney": 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))]),
-        "Electrolytes": build_table("🧪 Electrolytes", [("Sodium", test_values["Sodium"], (135, 145)),
-                                                      ("Potassium", test_values["Potassium"], (3.5, 5.1))]),
-        "Vitals": 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))])
+        "Hematology":
+        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))]),
+        "Iron Panel":
+        build_table("🧬 Iron Panel",
+                    [("Iron", test_values["Iron"], (60, 170)),
+                     ("Ferritin", test_values["Ferritin"], (30, 300)),
+                     ("TIBC", test_values["TIBC"], (250, 400))]),
+        "Liver & Kidney":
+        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))]),
+        "Electrolytes":
+        build_table("🧪 Electrolytes",
+                    [("Sodium", test_values["Sodium"], (135, 145)),
+                     ("Potassium", test_values["Potassium"], (3.5, 5.1))]),
+        "Vitals":
+        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))])
     }
 
     summary = "<ul><li>Your hemoglobin is a bit low — this could mean mild anemia.</li><li>Low iron storage detected — consider an iron profile test.</li><li>Elevated bilirubin — possible jaundice. Recommend LFT.</li><li>High HbA1c — prediabetes indication. Recommend glucose check.</li><li>Low SpO₂ — suggest retesting with a pulse oximeter.</li></ul>"
 
-    # Convert frame_rgb to base64 for profile picture (this is temporary placeholder)
     _, buffer = cv2.imencode('.png', frame_rgb)
     profile_image_base64 = base64.b64encode(buffer).decode('utf-8')
 
-    # Generate Health Card HTML
-    health_card_html = build_health_card(profile_image_base64, test_results, summary)
+    # Use global patient details
+    global current_patient_details
+    health_card_html = build_health_card(
+        profile_image_base64, 
+        test_results,
+        summary, 
+        current_patient_details['name'],
+        current_patient_details['age'],
+        current_patient_details['gender'],
+        current_patient_details['id']
+    )
     return health_card_html, frame_rgb
 
+
+# Modified route_inputs function
+def route_inputs(mode, image, video, patient_name, patient_age, patient_gender, patient_id):
+    if mode == "Image" and image is None:
+        return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
+    if mode == "Video" and video is None:
+        return "<div style='color:red;'>⚠️ Error: No video provided.</div>", None
+    
+    # Store patient details globally for use in analyze_face
+    global current_patient_details
+    current_patient_details = {
+        'name': patient_name,
+        'age': patient_age, 
+        'gender': patient_gender,
+        'id': patient_id
+    }
+    
+    health_card_html, frame_rgb = analyze_face(image if mode == "Image" else video)
+    return health_card_html, frame_rgb
+
+
 # Create Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("""# 🧠 Face-Based Lab Test AI Report (Video Mode)""")
     with gr.Row():
         with gr.Column():
-            mode_selector = gr.Radio(label="Choose Input Mode", choices=["Image", "Video"], value="Image")
+            gr.Markdown("### Patient Information")
+            patient_name = gr.Textbox(label="Patient Name", placeholder="Enter patient name")
+            patient_age = gr.Number(label="Age", value=25, minimum=1, maximum=120)
+            patient_gender = gr.Radio(label="Gender", choices=["Male", "Female", "Other"], value="Male")
+            patient_id = gr.Textbox(label="Patient ID", placeholder="Enter patient ID (optional)")
+            
+            gr.Markdown("### Image/Video Input")
+            mode_selector = gr.Radio(label="Choose Input Mode",
+                                     choices=["Image", "Video"],
+                                     value="Image")
             image_input = gr.Image(type="numpy", label="📸 Upload Face Image")
-            video_input = gr.Video(label="📽 Upload Face Video", sources=["upload", "webcam"])
+            video_input = gr.Video(label="Upload Face Video",
+                                   sources=["upload", "webcam"])
             submit_btn = gr.Button("🔍 Analyze")
         with gr.Column():
             result_html = gr.HTML(label="🧪 Health Report Table")
             result_image = gr.Image(label="📷 Key Frame Snapshot")
 
-    def route_inputs(mode, image, video):
-        health_card_html, frame_rgb = analyze_face(image) if mode == "Image" else analyze_face(video)
-        return health_card_html, frame_rgb
-
-    submit_btn.click(fn=route_inputs, inputs=[mode_selector, image_input, video_input], outputs=[result_html, result_image])
+    submit_btn.click(fn=route_inputs,
+                     inputs=[mode_selector, image_input, video_input, patient_name, patient_age, patient_gender, patient_id],
+                     outputs=[result_html, result_image])
 
-demo.launch()
+# Launch Gradio for Replit
+demo.launch(server_name="0.0.0.0", server_port=7860)