import gradio as gr
import torch
import json
import os
import cv2
import numpy as np
import easyocr
import keras_ocr
from paddleocr import PaddleOCR
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch.nn.functional as F
from save_results import save_results_to_repo

# Paths
MODEL_PATH = "./distilbert_spam_model"

# Ensure model exists
if not os.path.exists(os.path.join(MODEL_PATH, "pytorch_model.bin")):
    print(f"⚠️ Model not found in {MODEL_PATH}. Downloading from Hugging Face Hub...")
    model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
    model.save_pretrained(MODEL_PATH)
    tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
    tokenizer.save_pretrained(MODEL_PATH)
    print(f"✅ Model saved at {MODEL_PATH}.")
else:
    model = DistilBertForSequenceClassification.from_pretrained(MODEL_PATH)
    tokenizer = DistilBertTokenizer.from_pretrained(MODEL_PATH)

# Set model to evaluation mode
model.eval()

# OCR Methods
def ocr_with_paddle(img):
    ocr = PaddleOCR(lang='en', use_angle_cls=True)
    result = ocr.ocr(img)
    return ' '.join([item[1][0] for item in result[0]])

def ocr_with_keras(img):
    pipeline = keras_ocr.pipeline.Pipeline()
    images = [keras_ocr.tools.read(img)]
    predictions = pipeline.recognize(images)
    return ' '.join([text for text, _ in predictions[0]])

def ocr_with_easy(img):
    gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    reader = easyocr.Reader(['en'])
    results = reader.readtext(gray_image, detail=0)
    return ' '.join(results)

# OCR Extraction Function
def extract_text(method, img):
    if img is None:
        return "Error: Please upload an image!", ""

    # Convert PIL Image to OpenCV format
    img = np.array(img)

    # Select OCR method
    if method == "PaddleOCR":
        text_output = ocr_with_paddle(img)
    elif method == "EasyOCR":
        text_output = ocr_with_easy(img)
    else:  # KerasOCR
        text_output = ocr_with_keras(img)

    # Clean extracted text
    text_output = text_output.strip()
    
    if len(text_output) == 0:
        return "No text detected!", ""

    return text_output, ""

# Classification Function
def classify_text(text_output):
    if text_output.strip() in ["No text detected!", "Error: Please upload an image!"]:
        return text_output, "Cannot classify"

    # Tokenize text
    inputs = tokenizer(text_output, return_tensors="pt", truncation=True, padding=True, max_length=512)

    # Model inference
    with torch.no_grad():
        outputs = model(**inputs)
        probs = F.softmax(outputs.logits, dim=1)
        prediction = torch.argmax(probs, dim=1).item()

    label_map = {0: "Not Spam", 1: "Spam"}
    label = label_map[prediction]

    # Save results automatically
    save_results_to_repo(text_output, label)

    return text_output, label

# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("## OCR Spam Classifier")

    method_input = gr.Radio(["PaddleOCR", "EasyOCR", "KerasOCR"], value="PaddleOCR", label="Choose OCR Method")
    image_input = gr.Image(label="Upload Image")

    extract_button = gr.Button("Submit")
    classify_button = gr.Button("Classify")

    output_text = gr.Textbox(label="Extracted Text", interactive=True)
    output_label = gr.Textbox(label="Spam Classification", interactive=False)

    # Button Click Bindings
    extract_button.click(fn=extract_text, inputs=[method_input, image_input], outputs=[output_text, output_label])
    classify_button.click(fn=classify_text, inputs=[output_text], outputs=[output_text, output_label])

# Launch App
if __name__ == "__main__":
    demo.launch()