Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import easyocr
+from PIL import Image
+import numpy as np
+
+# -------------------- MODEL SETUP --------------------
+MODEL_NAME = "microsoft/deberta-v3-base"  # Context-rich NLP model
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+# Classes (Your categories)
+LABELS = [
+    "abusive_language",
+    "harassment",
+    "threat",
+    "racism",
+    "sexism",
+    "religious_hate",
+    "not_hate"
+]
+
+# Initialize OCR reader
+reader = easyocr.Reader(['en'], gpu=False)
+
+# -------------------- FUNCTIONS --------------------
+def extract_text_from_image(image):
+    """Extracts text from uploaded image using EasyOCR."""
+    if image is None:
+        return ""
+    if isinstance(image, Image.Image):
+        image = np.array(image)
+    extracted = reader.readtext(image, detail=0)
+    return " ".join(extracted)
+
+
+def classify_text_with_deberta(text):
+    """Runs text through DeBERTa model for classification."""
+    if not text.strip():
+        return "No text found for analysis.", None
+    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 f"Prediction: {LABELS[pred]} (Confidence: {confidence:.2f})", LABELS[pred]
+
+
+def respond(chat_history, user_text, user_image):
+    """Chatbot pipeline: OCR → DeBERTa classification → Chat output"""
+    # OCR extraction if image uploaded
+    if user_image is not None:
+        extracted_text = extract_text_from_image(user_image)
+        if extracted_text.strip():
+            text = extracted_text
+            display_text = f"[Extracted from OCR] {extracted_text}"
+        elif user_text and user_text.strip():
+            text = user_text
+            display_text = text
+        else:
+            chat_history.append(("User", ""))
+            chat_history.append(("Bot", "Please enter text or upload a readable image."))
+            return chat_history, "", None
+    else:
+        text = user_text or ""
+        display_text = text.strip()
+
+    if not display_text:
+        chat_history.append(("User", ""))
+        chat_history.append(("Bot", "Empty input provided."))
+        return chat_history, "", None
+
+    # Run DeBERTa classification
+    result, label = classify_text_with_deberta(text)
+    chat_history.append(("User", display_text))
+    chat_history.append(("Cyber Bully Bot", result))
+    return chat_history, "", None
+
+
+# -------------------- GRADIO INTERFACE --------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## 💬 Cyber Bully Detection System (OCR + DeBERTa Context Analysis)")
+    chat_history = gr.State([])
+
+    chatbot = gr.Chatbot(label="Chat History")
+    with gr.Row():
+        text_input = gr.Textbox(show_label=False, placeholder="Type a message or paste text here")
+        image_input = gr.Image(source="upload", type="pil", label="Upload Screenshot (optional)")
+    with gr.Row():
+        submit_btn = gr.Button("Analyze")
+        clear_btn = gr.Button("Clear Chat")
+
+    submit_btn.click(respond, [chatbot, text_input, image_input], [chatbot, text_input, image_input])
+    clear_btn.click(lambda: ([], "", None), None, [chatbot, text_input, image_input])
+
+    gr.Markdown(
+        "🔍 **How it works:** Upload a screenshot or type text. "
+        "The system extracts text via OCR and uses DeBERTa to understand contextual meaning and classify it."
+    )
+
+# -------------------- LAUNCH --------------------
+if __name__ == "__main__":
+    demo.launch()