import cv2
import numpy as np
import pandas as pd
import gradio as gr
import matplotlib.pyplot as plt
from datetime import datetime

def preprocess_image(image):
    """Convert image to HSV and apply adaptive thresholding for better detection."""
    if len(image.shape) == 2:
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
    
    hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    
    # Adaptive thresholding for better contrast
    adaptive_thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
    
    # Morphological operations to remove noise
    kernel = np.ones((3,3), np.uint8)
    clean_mask = cv2.morphologyEx(adaptive_thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
    clean_mask = cv2.morphologyEx(clean_mask, cv2.MORPH_OPEN, kernel, iterations=2)
    
    return clean_mask

def detect_blood_cells(image):
    """Detect blood cells using contour analysis with refined filtering."""
    mask = preprocess_image(image)
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    features = []
    for i, contour in enumerate(contours, 1):
        area = cv2.contourArea(contour)
        perimeter = cv2.arcLength(contour, True)
        circularity = 4 * np.pi * area / (perimeter * perimeter) if perimeter > 0 else 0
        
        if 100 < area < 5000 and circularity > 0.7:
            M = cv2.moments(contour)
            if M["m00"] != 0:
                cx = int(M["m10"] / M["m00"])
                cy = int(M["m01"] / M["m00"])
                features.append({'label': i, 'area': area, 'perimeter': perimeter, 'circularity': circularity, 'centroid_x': cx, 'centroid_y': cy})
    
    return contours, features, mask

def process_image(image):
    if image is None:
        return None, None, None, None
    
    contours, features, mask = detect_blood_cells(image)
    vis_img = image.copy()
    
    for feature in features:
        contour = contours[feature['label'] - 1]
        cv2.drawContours(vis_img, [contour], -1, (0, 255, 0), 2)
        cv2.putText(vis_img, str(feature['label']), (feature['centroid_x'], feature['centroid_y']), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
    
    df = pd.DataFrame(features)
    return vis_img, mask, df

def analyze(image):
    vis_img, mask, df = process_image(image)
    
    plt.style.use('dark_background')
    fig, axes = plt.subplots(1, 2, figsize=(12, 5))
    
    if not df.empty:
        axes[0].hist(df['area'], bins=20, color='cyan', edgecolor='black')
        axes[0].set_title('Cell Size Distribution')
        
        axes[1].scatter(df['area'], df['circularity'], alpha=0.6, c='magenta')
        axes[1].set_title('Area vs Circularity')
    
    return vis_img, mask, fig, df

# Gradio Interface
demo = gr.Interface(fn=analyze, inputs=gr.Image(type="numpy"), outputs=[gr.Image(), gr.Image(), gr.Plot(), gr.Dataframe()])
demo.launch()