app.py

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

# Paths
MODEL_PATH = "./distilbert_spam_model"
RESULTS_JSON = "results.json"

# 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)

# Ensure model is in evaluation mode
model.eval()

# OCR Functions
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)

# Save results to JSON
def save_to_json(text, label):
    data = {"text": text, "classification": label}
    if os.path.exists(RESULTS_JSON):
        with open(RESULTS_JSON, "r") as file:
            try:
                results = json.load(file)
            except json.JSONDecodeError:
                results = []
    else:
        results = []
    
    results.append(data)
    with open(RESULTS_JSON, "w") as file:
        json.dump(results, file, indent=4)

# OCR & Classification Function
def generate_ocr(method, img):
    if img is None:
        raise gr.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)

    text_output = text_output.strip()
    if len(text_output) == 0:
        return "No text detected!", "Cannot classify"

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

    # Perform inference
    with torch.no_grad():
        outputs = model(**inputs)
        probs = F.softmax(outputs.logits, dim=1)
        spam_prob = probs[0][1].item()

    label = "Spam" if spam_prob > 0.5 else "Not Spam"

    # Save results to JSON
    save_to_json(text_output, label)

    return text_output, label

# Gradio Interface
image_input = gr.Image()
method_input = gr.Radio(["PaddleOCR", "EasyOCR", "KerasOCR"], value="PaddleOCR")
output_text = gr.Textbox(label="Extracted Text")
output_label = gr.Textbox(label="Spam Classification")

demo = gr.Interface(
    generate_ocr,
    inputs=[method_input, image_input],
    outputs=[output_text, output_label],
    title="OCR Spam Classifier",
    description="Upload an image, extract text, and classify it as Spam or Not Spam.",
    theme="compact",
)

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