import gradio as gr
import cv2
import numpy as np
from PIL import Image
import pickle
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
import easyocr

# === Load Model and Label Encoder ===
model_path = "MobileNetBest_Model.h5"
label_path = "MobileNet_Label_Encoder.pkl"

model = load_model(model_path)
print("Model loaded.")

# Load label encoder
try:
    with open(label_path, 'rb') as f:
        label_map = pickle.load(f)
        index_to_label = {v: k for k, v in label_map.items()}
    print("Label encoder loaded:", index_to_label)
except:
    index_to_label = {0: "Handwritten", 1: "Computerized"}
    print("Label encoder not found. Using default:", index_to_label)

# === Initialize EasyOCR Reader Once (with GPU) ===
reader = easyocr.Reader(['en'], gpu=True)
print("EasyOCR Reader initialized with GPU.")

# === Classify Region ===
def classify_text_region(region_img):
    try:
        region_img = cv2.resize(region_img, (224, 224))
        region_img = region_img.astype("float32") / 255.0
        region_img = img_to_array(region_img)
        region_img = np.expand_dims(region_img, axis=0)

        preds = model.predict(region_img)

        if preds.shape[-1] == 1:
            return "Computerized" if preds[0][0] > 0.5 else "Handwritten"
        else:
            class_idx = np.argmax(preds[0])
            return index_to_label.get(class_idx, "Unknown")
    except Exception as e:
        print("Classification error:", e)
        return "Unknown"

# === OCR + Annotation ===
def AnnotatedTextDetection_EasyOCR_from_array(img):
    results = reader.readtext(img)
    annotated_results = []

    for (bbox, text, conf) in results[:50]:  # Limit to top 20 boxes
        if conf < 0.3 or text.strip() == "":
            continue

        x1, y1 = map(int, bbox[0])
        x2, y2 = map(int, bbox[2])
        crop = img[y1:y2, x1:x2]
        if crop.size == 0:
            continue

        label = classify_text_region(crop)
        annotated_results.append(f"{text.strip()} → {label}")

        color = (0, 255, 0) if label == "Computerized" else (255, 0, 0)
        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
        cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1)

    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB), "\n".join(annotated_results)

# === Gradio Wrapper ===
def infer(image):
    img = np.array(image)

    # Resize if image is too large
    max_dim = 1000
    if img.shape[0] > max_dim or img.shape[1] > max_dim:
        scale = max_dim / max(img.shape[0], img.shape[1])
        img = cv2.resize(img, (int(img.shape[1]*scale), int(img.shape[0]*scale)))

    annotated_img, result_text = AnnotatedTextDetection_EasyOCR_from_array(img)
    return Image.fromarray(annotated_img), result_text

# === Custom CSS ===
custom_css = """
body {
    background-color: #e6f2ff;
}
.gradio-container {
    border-radius: 12px;
    padding: 20px;
    border: 2px solid #007acc;
}
.gr-input, .gr-output {
    border: 1px solid #007acc;
    border-radius: 10px;
}
"""

# === Launch Interface ===
demo = gr.Interface(
    fn=infer,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=[
        gr.Image(type="pil", label="Annotated Image"),
        gr.Textbox(label="Detected Text and Classification")
    ],
    title="Text Detection and Classification",
    description="This application detects text using EasyOCR and classifies each text region as Handwritten or Computerized using a MobileNet model.",
    theme="soft",
    css=custom_css
)
demo.launch()