app_original.py

# app.py

## Quick Start w UI input + OCR image function as fifth iteration

import gradio as gr
from transformers import pipeline
from PIL import Image
import pytesseract

# Your model pipeline


classifier = pipeline("text-classification", model="didulantha/sms-spam-detector")

custom_label_map = {
    'LABEL_1': 'Possible Scam',
    'LABEL_0': 'Harmless Perhaps',
}

def classify_image_or_text(input_text, input_image=None):
    try:
        # If image is provided, extract text using pytesseract
        if input_image is not None:
            import pytesseract
            from PIL import Image
            text = pytesseract.image_to_string(Image.open(input_image))
            if not text.strip():
                return "No text detected in image. Please provide a clearer image."
        else:
            text = input_text
            if not text.strip():
                return "No text provided. Please enter text or upload an image with text."

        # Pass extracted text to classifier
        output = classifier(text)
        label = output[0]['label']
        # Custom label mapping
        if label == "LABEL_1":
            return "Possible Scam"
        elif label == "LABEL_0":
            return "Harmless Perhaps"
        else:
            return f"Unexpected model label: {label}"
    except Exception:
        # Return more informative error
        return ("Processing error: Something went wrong. "
                "Please check if the image quality is good and the text is readable.")


iface = gr.Interface(
    fn=classify_image_or_text,
    inputs=[
        gr.Textbox(label="SMS Message (or leave blank if uploading image)"),
        gr.Image(label="Image with Text (optional)", type="pil"),
    ],
    outputs=gr.Label(label="Prediction"),
    title="SMS Spam Detector",
    description="Paste SMS or upload image of SMS. Prioritizes text if both."
)

iface.launch()






## Detailed Usage

## from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
## import torch
## tokenizer = DistilBertTokenizer.from_pretrained("didulantha/sms-spam-detector")
## model = DistilBertForSequenceClassification.from_pretrained("didulantha/sms-spam-detector")
## def predict(text):
##     inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
##     outputs = model(**inputs)
##     probs = torch.softmax(outputs.logits, dim=1)
##     return "SPAM" if probs[0][1] > 0.5 else "HAM"
## print(predict("Free prize!"))