Added clean_text function to remove filler words and extra spaces

app.py

@@ -2,6 +2,7 @@ from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+import re
 
 app = FastAPI()
 
@@ -11,6 +12,12 @@ model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-r
 class TextRequest(BaseModel):
     text: str
 
+def clean_text(text: str) -> str:
+    fillers = ["um", "uh", "like", "you know", "I mean", "sort of", "kind of", "hmm", "uhh"]
+    text = re.sub(r'\b(?:' + '|'.join(fillers) + r')\b', '', text, flags=re.IGNORECASE)
+    text = re.sub(r'\s+', ' ', text).strip()  
+    return text
+
 @app.get("/")
 def home():
     return {"message": "Speak your mind emotion API is running"}
@@ -18,20 +25,28 @@ def home():
 @app.post("/classify-emotion")
 async def classify_emotion(request: TextRequest):
     try:
-        text = request.text  
+        text = request.text.strip()
+
+        if not text:
+            raise HTTPException(status_code=400, detail="Text cannot be empty")
+        cleaned_text = clean_text(text)
 
-        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+        inputs = tokenizer(cleaned_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
 
         with torch.no_grad():
             outputs = model(**inputs)
 
-
         logits = outputs.logits
         predicted_class_id = torch.argmax(logits, dim=-1).item()
-        predicted_emotion = model.config.id2label[predicted_class_id]  
+        predicted_emotion = model.config.id2label[predicted_class_id]
 
-        return {"predicted_emotion": predicted_emotion}
+        return {
+            "original_text": text,
+            "cleaned_text": cleaned_text,
+            "predicted_emotion": predicted_emotion
+        }
 
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))  
+        raise HTTPException(status_code=500, detail=f"Error processing text: {str(e)}")
+
 

requirements.txt

@@ -3,4 +3,5 @@ uvicorn
 transformers
 torch
 httpx
-pytest
+pytest
+pydantic