app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import easyocr
from PIL import Image
import numpy as np

# Set up model and labels
MODEL_NAME = "cardiffnlp/twitter-roberta-base-hate-multiclass-latest"
LABELS = [
    "sexism",             # 0
    "racism",             # 1
    "disability",         # 2
    "sexual_orientation", # 3
    "religion",           # 4
    "other",              # 5
    "not_hate"            # 6
]

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
reader = easyocr.Reader(['en'])

def classify_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=-1)
        pred = torch.argmax(probs).item()
        confidence = float(probs[0][pred])
        return LABELS[pred], confidence

def ocr_extract(image):
    # Convert to numpy if Image
    if isinstance(image, Image.Image):
        image = np.array(image)
    result = reader.readtext(image, detail=0)
    return ' '.join(result)

def chatbot(image=None, text=None):
    # Prioritize image
    if image is not None:
        extracted = ocr_extract(image)
        if not extracted.strip():
            return "No text found in image.", None
        label, confidence = classify_text(extracted)
        return f"OCR Extracted: {extracted}\nPrediction: {label} (Confidence: {confidence:.2f})", label
    elif text and text.strip():
        label, confidence = classify_text(text)
        return f"Text: {text}\nPrediction: {label} (Confidence: {confidence:.2f})", label
    else:
        return "Please provide an image or some text.", None

iface = gr.Interface(
    fn=chatbot,
    inputs=[
        gr.Image(type="pil", label="Upload Screenshot (optional)"),
        gr.Textbox(lines=2, placeholder="Or, type/paste text here")
    ],
    outputs=[
        gr.Textbox(label="Prediction & OCR"),
        gr.Label(num_top_classes=7)
    ],
    title="Cyberbyully Detection System (with OCR)",
    description="Detects: sexism, racism, religion, other, not_hate. Enter text or upload screenshot."
)

if __name__ == "__main__":
    iface.launch()