Update app.py

app.py

@@ -47,6 +47,7 @@ def train_model(output_range):
     model = LinearRegression().fit(X, y)
     return model
 
+
 # Load models
 try:
     hemoglobin_model = joblib.load("hemoglobin_model_from_anemia_dataset.pkl")
@@ -114,7 +115,7 @@ def build_table(title, rows):
     html += '</tbody></table></div>'
     return html
 
-# Function to save the health report to PDF (showing only the Health Card section)
+# Function to save the health report to PDF
 def save_results_to_pdf(test_results, filename):
     try:
         # Create a PDF document
@@ -145,11 +146,11 @@ def save_results_to_pdf(test_results, filename):
         
         # Add title
         flowables.append(Paragraph("Health Report", title_style))
-        flowables.append(Spacer(1, 12))
         
-        # Only add the health card content here
+        # Add test results to the report
         for label, value in test_results.items():
-            flowables.append(Paragraph(value, body_style))
+            line = f"{label}: {value}"
+            flowables.append(Paragraph(line, body_style))
             flowables.append(Spacer(1, 12))
         
         # Build the PDF
@@ -158,15 +159,14 @@ def save_results_to_pdf(test_results, filename):
     except Exception as e:
         return f"Error saving PDF: {str(e)}", None
 
-
-# Function for Health Card Output (removes patient details)
-def build_health_card(profile_image, test_results, summary):
+# Build health card layout
+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")
     
-    # Build the Health Card HTML
     html = f"""
     <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;">
@@ -174,18 +174,25 @@ def build_health_card(profile_image, test_results, summary):
                 </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;">Face-Based Health Analysis Report</h2>
+                    <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="background-color: rgba(255, 255, 255, 0.95); border-radius: 12px; padding: 25px; margin-bottom: 25px; border: 1px solid #e0e0e0;">
-            {test_results}
+            {test_results['Hematology']}
+            {test_results['Iron Panel']}
+            {test_results['Liver & Kidney']}
+            {test_results['Electrolytes']}
+            {test_results['Vitals']}
         </div>
 
         <div style="background-color: rgba(255, 255, 255, 0.95); padding: 20px; border-radius: 12px; border: 1px solid #e0e0e0; margin-bottom: 25px;">
@@ -199,19 +206,144 @@ def build_health_card(profile_image, test_results, summary):
             <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>
     """
     return html
 
-# Define the Gradio interface
+# 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)
+    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  # Fixed: Use integer index
+    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] = 0.385
+        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
+
+    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 = spo2_model.predict([spo2_features])[0]
+    rr = int(12 + abs(heart_rate % 5 - 2))
+
+    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))])
+    }
+
+    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>"
+
+    _, buffer = cv2.imencode('.png', frame_rgb)
+    profile_image_base64 = base64.b64encode(buffer).decode('utf-8')
+
+    # 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']
+    )
+    
+    # Generate PDF and return for download
+    pdf_filename = f"Health_Report_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.pdf"
+    pdf_result, pdf_filepath = save_results_to_pdf(test_results, pdf_filename)
+    
+    if pdf_filepath:
+        # Copy the PDF to a temporary directory for Gradio to serve it
+        temp_pdf_path = "/tmp/" + os.path.basename(pdf_filepath)
+        shutil.copy(pdf_filepath, temp_pdf_path)
+        
+        return health_card_html, temp_pdf_path
+
+
+# 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
-
-    # Process the inputs for analysis
+    
+    # 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, pdf_file_path = analyze_face(image if mode == "Image" else video)
     return health_card_html, pdf_file_path
 
@@ -228,15 +360,20 @@ with gr.Blocks() as demo:
             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")
+            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_pdf = gr.File(label="Download Health Report PDF", interactive=False)
 
-            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_pdf])
+            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_pdf])
 
-# Launch Gradio interface
+# Launch Gradio for Replit
 demo.launch(server_name="0.0.0.0", server_port=7860)