Update app.py

app.py

@@ -20,13 +20,6 @@ HATE_LABELS = [
 hate_tokenizer = AutoTokenizer.from_pretrained(HATE_MODEL_NAME)
 hate_model = AutoModelForSequenceClassification.from_pretrained(HATE_MODEL_NAME)
 
-# Sarcasm Detection Model and Labels
-SARCASM_MODEL_NAME = "abhishek/sarcasm-detector-distilbert-base-uncased"
-SARCASM_LABELS = ["Not Sarcastic", "Sarcastic"]
-
-sarcasm_tokenizer = AutoTokenizer.from_pretrained(SARCASM_MODEL_NAME)
-sarcasm_model = AutoModelForSequenceClassification.from_pretrained(SARCASM_MODEL_NAME)
-
 reader = easyocr.Reader(['en'], gpu=False)
 
 def classify_text(text):
@@ -38,15 +31,6 @@ def classify_text(text):
         confidence = float(probs[0][pred])
         return HATE_LABELS[pred], confidence
 
-def detect_sarcasm(text):
-    inputs = sarcasm_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-    with torch.no_grad():
-        outputs = sarcasm_model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        pred = torch.argmax(probs).item()
-        confidence = float(probs[0][pred])
-        return SARCASM_LABELS[pred], confidence
-
 def ocr_extract(image):
     if isinstance(image, Image.Image):
         image = np.array(image)
@@ -57,24 +41,14 @@ def chatbot(image=None, text=None):
     if image is not None:
         extracted = ocr_extract(image)
         if not extracted.strip():
-            return "No text found in image.", None, None
-        hate_label, hate_conf = classify_text(extracted)
-        sarcasm_label, sarcasm_conf = detect_sarcasm(extracted)
-        return (
-            f"OCR Extracted: {extracted}\nHate Speech: {hate_label} (Confidence: {hate_conf:.2f})\nSarcasm: {sarcasm_label} (Confidence: {sarcasm_conf:.2f})",
-            hate_label,
-            sarcasm_label
-        )
+            return "No text found in image.", None
+        label, confidence = classify_text(extracted)
+        return f"OCR Extracted: {extracted}\nHate Speech: {label} (Confidence: {confidence:.2f})", label
     elif text and text.strip():
-        hate_label, hate_conf = classify_text(text)
-        sarcasm_label, sarcasm_conf = detect_sarcasm(text)
-        return (
-            f"Text: {text}\nHate Speech: {hate_label} (Confidence: {hate_conf:.2f})\nSarcasm: {sarcasm_label} (Confidence: {sarcasm_conf:.2f})",
-            hate_label,
-            sarcasm_label
-        )
+        label, confidence = classify_text(text)
+        return f"Text: {text}\nHate Speech: {label} (Confidence: {confidence:.2f})", label
     else:
-        return "Please provide an image or some text.", None, None
+        return "Please provide an image or some text.", None
 
 iface = gr.Interface(
     fn=chatbot,
@@ -83,16 +57,12 @@ iface = gr.Interface(
         gr.Textbox(lines=3, placeholder="Or, type/paste text here")
     ],
     outputs=[
-        gr.Textbox(label="Prediction & Sarcasm Detection"),
+        gr.Textbox(label="Prediction"),
         gr.Label(num_top_classes=len(HATE_LABELS), label="Hate Speech Class"),
-        gr.Label(num_top_classes=2, label="Sarcasm")
     ],
-    title="Hate Speech & Sarcasm Detection Chatbot",
-    description="""
-Classifies text (or extracted text from image) into hate speech categories and detects sarcasm independently.
-Upload an image or enter text below.
-"""
+    title="Hate Speech Detection Chatbot",
+    description="Detects hate speech categories from text or screenshots."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()