import gradio as gr
import cv2
import numpy as np
import mediapipe as mp
from sklearn.linear_model import LinearRegression
import random
import pickle

# 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]
    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]

# 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),
          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 pre-trained models for Hemoglobin, SPO2, and Heart Rate using pickle
with open("hemoglobin_model_from_anemia_dataset.pkl", "rb") as f:
    hemoglobin_model = pickle.load(f)

with open("spo2_model_simulated.pkl", "rb") as f:
    spo2_model = pickle.load(f)

with open("heart_rate_model.pkl", "rb") as f:
    hr_model = pickle.load(f)

# Model dictionary setup for other tests
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))
}

# Function to determine risk level
def get_risk_color(value, normal_range):
    low, high = normal_range
    if value < low:
        return ("Low", "🔻", "#FFCCCC")
    elif value > high:
        return ("High", "🔺", "#FFE680")
    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;">'
        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>'
    )
    for label, value, ref in 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>'
    html += '</tbody></table></div>'
    return html

# Analyzing image for health metrics
def analyze_image(image):
    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;'>⚠️ Face not detected in image.</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  # simulate other 7D inputs

    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", test_values["SpO2"], (95, 100)), ("Heart Rate", test_values["Heart Rate"], (60, 100)), ("Temperature", test_values["Temperature"], (97, 99)), ("BP Systolic", test_values["BP Systolic"], (90, 120))]),
    ])
    return html_output, frame_rgb

# Gradio Interface setup
with gr.Blocks() as demo:
    gr.Markdown("""
    # 🧠 Face-Based Lab Test AI Report (Image Mode)
    Upload a face image to infer health diagnostics using AI-based analysis.
    """)
    with gr.Row():
        image_input = gr.Image(type="numpy", label="📸 Upload Face Image")
        submit_btn = gr.Button("🔍 Analyze")
    with gr.Column():
        result_html = gr.HTML(label="🧪 Health Report Table")
        result_image = gr.Image(label="📷 Key Frame Snapshot")

    submit_btn.click(fn=analyze_image, inputs=image_input, outputs=[result_html, result_image])

    gr.Markdown("""---
✅ Table Format • AI Prediction • Dynamic Summary""")

demo.launch()