import gradio as gr import cv2 import numpy as np import mediapipe as mp from sklearn.linear_model import LinearRegression import random import base64 import joblib from fpdf import FPDF # 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) # Functions for feature extraction def extract_features(image, landmarks): red_channel = image[:, :, 2] green_channel = image[:, :, 1] blue_channel = image[:, :, 0] red_percent = 100 * np.mean(red_channel) / 255 green_percent = 100 * np.mean(green_channel) / 255 blue_percent = 100 * np.mean(blue_channel) / 255 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)] y = [random.uniform(*output_range) for _ in X] model = LinearRegression().fit(X, y) return model # Load models 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, "WBC Count": train_model((4.0, 11.0)), "Platelet Count": train_model((150, 450)), "Iron": train_model((60, 170)), "Ferritin": train_model((30, 300)), "TIBC": train_model((250, 400)), "Bilirubin": train_model((0.3, 1.2)), "Creatinine": train_model((0.6, 1.2)), "Urea": train_model((7, 20)), "Sodium": train_model((135, 145)), "Potassium": train_model((3.5, 5.1)), "TSH": train_model((0.4, 4.0)), "Cortisol": train_model((5, 25)), "FBS": train_model((70, 110)), "HbA1c": train_model((4.0, 5.7)), "Albumin": train_model((3.5, 5.5)), "BP Systolic": train_model((90, 120)), "BP Diastolic": train_model((60, 80)), "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", "๐Ÿ”ป", "#fff3cd") elif value > high: return ("High", "๐Ÿ”บ", "#f8d7da") else: return ("Normal", "โœ…", "#d4edda") # Function to build table for test results def build_table(title, rows): html = ( f'
' f'
' f'

{title}

' f'
' f'' f'' ) for i, (label, value, ref) in enumerate(rows): level, icon, bg = get_risk_color(value, ref) 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'' html += '
TestResultRangeLevel
{label}{value_str}{ref[0]} - {ref[1]}{icon} {level}
' return html # Generate PDF report using FPDF def generate_pdf(report_html): pdf = FPDF() pdf.set_auto_page_break(auto=True, margin=15) pdf.add_page() pdf.set_font("Arial", size=12) # Add a title pdf.cell(200, 10, txt="Face-Based Health Report", ln=True, align="C") # Write the report HTML content into the PDF pdf.multi_cell(0, 10, txt=report_html) # Save the PDF to a file pdf_output = "/mnt/data/health_report.pdf" pdf.output(pdf_output) return pdf_output # 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") html = f"""

HEALTH CARD

Report Date: {current_date}
{f'
Patient ID: {patient_id}
' if patient_id else ''}
Profile

{patient_name if patient_name else "Lab Test Results"}

{f"Age: {patient_age} | Gender: {patient_gender}" if patient_age and patient_gender else "AI-Generated Health Analysis"}

Face-Based Health Analysis Report

{test_results['Hematology']} {test_results['Iron Panel']} {test_results['Liver & Kidney']} {test_results['Electrolytes']} {test_results['Vitals']}

๐Ÿ“ Summary & Recommendations

{summary}
""" return html # 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 "
โš ๏ธ Error: Could not open video.
", None ret, frame = cap.read() cap.release() if not ret: return "
โš ๏ธ Error: Could not read video frame.
", None else: # Image input frame = input_data if frame is None: return "
โš ๏ธ Error: No image provided.
", 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 "
โš ๏ธ Error: Face not detected.
", 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("๐Ÿฉธ 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("๐Ÿงฌ Iron Panel", [("Iron", test_values["Iron"], (60, 170)), ("Ferritin", test_values["Ferritin"], (30, 300)), ("TIBC", test_values["TIBC"], (250, 400))]), "Liver & Kidney": build_table("๐Ÿงฌ 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("๐Ÿงช Electrolytes", [("Sodium", test_values["Sodium"], (135, 145)), ("Potassium", test_values["Potassium"], (3.5, 5.1))]), "Vitals": build_table("โค๏ธ 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 = "" _, 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 pdf_file_path = generate_pdf(health_card_html) return pdf_file_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 "
โš ๏ธ Error: No image provided.
", None if mode == "Video" and video is None: return "
โš ๏ธ Error: No video provided.
", 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 } pdf_file_path = analyze_face(image if mode == "Image" else video) return pdf_file_path # Create Gradio interface with gr.Blocks() as demo: gr.Markdown("""# ๐Ÿง  Face-Based Lab Test AI Report (Video Mode)""") with gr.Row(): with gr.Column(): 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="๐Ÿ“ธ Upload Face Image") video_input = gr.Video(label="Upload Face Video", sources=["upload", "webcam"]) submit_btn = gr.Button("๐Ÿ” Analyze") with gr.Column(): download_btn = gr.Button("Download Report (PDF)") download_btn.download(pdf_file_path, "health_report.pdf") submit_btn.click(fn=route_inputs, inputs=[mode_selector, image_input, video_input, patient_name, patient_age, patient_gender, patient_id], outputs=[download_btn]) # Launch Gradio for Replit demo.launch(server_name="0.0.0.0", server_port=7860)