Update app.py

app.py

@@ -5,31 +5,30 @@ import easyocr
 from PIL import Image
 import numpy as np
 
-# Sarcasm detection model (public, replace if needed)
+# Sarcasm Detection Model (public)
 SARCASM_MODEL_NAME = "j-hartmann/emotion-english-distilroberta-base"
 sarcasm_labels = ["not sarcastic", "sarcastic"]
-
 sarcasm_tokenizer = AutoTokenizer.from_pretrained(SARCASM_MODEL_NAME)
 sarcasm_model = AutoModelForSequenceClassification.from_pretrained(SARCASM_MODEL_NAME)
 
-# Hate speech classification (fine-tuned DeBERTa for your labels, replace with your actual model)
+# Hate Speech Model - DeBERTa fine-tuned (replace with your own model)
 HATE_MODEL_NAME = "your-username/deberta-hate-speech-custom"
 hate_labels = [
-    "abusive_words",
+    "abusive_language",
     "harassment",
-    "religious_hate",
+    "threat",
+    "racism",
     "sexism",
-    "other",
+    "religious_hate",
     "not_hate"
 ]
-
 hate_tokenizer = AutoTokenizer.from_pretrained(HATE_MODEL_NAME)
 hate_model = AutoModelForSequenceClassification.from_pretrained(HATE_MODEL_NAME)
 
-# OCR Reader
+# OCR
 reader = easyocr.Reader(['en'], gpu=False)
 
-def ocr_extract(image):
+def extract_text(image):
     if isinstance(image, Image.Image):
         image = np.array(image)
     result = reader.readtext(image, detail=0)
@@ -53,41 +52,38 @@ def classify_hate(text):
         confidence = float(probs[0][pred])
         return hate_labels[pred], confidence
 
-def chatbot(image=None, text=None):
-    input_text = ""
+def pipeline(image=None, text=None):
     if image is not None:
-        input_text = ocr_extract(image)
+        input_text = extract_text(image)
         if not input_text.strip():
-            return "No text found in image.", None, None
+            return "No text found in image.", None
     elif text and text.strip():
         input_text = text
     else:
-        return "Please provide an image or some text.", None, None
+        return "Please provide text or upload an image.", None
 
     sarcasm_label, sarcasm_conf = detect_sarcasm(input_text)
     if sarcasm_label == "sarcastic":
-        return f"Text is detected as SARCASTIC (Confidence: {sarcasm_conf:.2f}). Hate speech classification skipped.", None, sarcasm_label
+        return f"Text detected as SARCASTIC (Confidence: {sarcasm_conf:.2f}). Hate speech classification skipped.", sarcasm_label
 
     hate_label, hate_conf = classify_hate(input_text)
     return (
-        f"Input text: {input_text}\nHate Speech: {hate_label} (Confidence: {hate_conf:.2f})\nSarcasm: {sarcasm_label} (Confidence: {sarcasm_conf:.2f})",
-        hate_label,
-        sarcasm_label
+        f"Input Text: {input_text}\nHate Speech Category: {hate_label} (Confidence: {hate_conf:.2f})\nSarcasm: {sarcasm_label} (Confidence: {sarcasm_conf:.2f})",
+        hate_label
     )
 
 iface = gr.Interface(
-    fn=chatbot,
+    fn=pipeline,
     inputs=[
         gr.Image(type="pil", label="Upload Screenshot (optional)"),
         gr.Textbox(lines=3, placeholder="Or, type/paste text here")
     ],
     outputs=[
-        gr.Textbox(label="Output"),
-        gr.Label(num_top_classes=len(hate_labels), label="Hate Speech Class"),
-        gr.Label(num_top_classes=2, label="Sarcasm")
+        gr.Textbox(label="Result"),
+        gr.Label(num_top_classes=len(hate_labels), label="Hate Speech Class")
     ],
-    title="Sarcasm-aware Hate Speech Classifier with OCR",
-    description="Detect sarcasm first; if no sarcasm, classify hate speech from text or image screenshot."
+    title="Cyber Bully Detection System",
+    description="Detects sarcasm first; if no sarcasm detected, classifies hate speech into abusive language, harassment, threat, racism, sexism, religious hate, or no hate."
 )
 
 if __name__ == "__main__":