Remove detector and evaluator, simplify API

detector.py

@@ -1,40 +0,0 @@
-import torch
-from transformers import pipeline
-
-# Cache the pipeline at module level — load once, reuse forever
-_pipeline = None
-
-
-def _get_pipeline():
-    global _pipeline
-    if _pipeline is None:
-        _pipeline = pipeline(
-            "text-classification",
-            model="Hello-SimpleAI/chatgpt-detector-roberta",
-            device=0 if torch.cuda.is_available() else -1,
-        )
-    return _pipeline
-
-
-def get_ai_score(text: str) -> float:
-    """
-    Returns a float between 0.0 and 1.0 representing the probability
-    that the text was AI-generated.
-    1.0 = definitely AI
-    0.0 = definitely human
-    """
-    # Model has 512 token limit — truncate to be safe
-    truncated = text[:512]
-
-    result = _get_pipeline()(truncated)[0]
-
-    label = result["label"].upper()
-    score = result["score"]
-
-    # This model returns:
-    # "ChatGPT" label = AI-generated
-    # "Human" label = human-written
-    if label == "CHATGPT":
-        return round(score, 4)
-    else:
-        return round(1 - score, 4)

evaluator.py

@@ -1,22 +0,0 @@
-from sentence_transformers import SentenceTransformer, util
-
-# Cache the SentenceTransformer model globally after first load
-_model = None
-
-def get_similarity(text_a: str, text_b: str) -> float:
-    global _model
-    if _model is None:
-        _model = SentenceTransformer('all-MiniLM-L6-v2')
-    
-    # Encode both inputs into embeddings
-    embedding_a = _model.encode(text_a, convert_to_tensor=True)
-    embedding_b = _model.encode(text_b, convert_to_tensor=True)
-    
-    # Compute cosine similarity
-    similarity = util.cos_sim(embedding_a, embedding_b)
-    
-    return float(similarity[0][0])
-
-def meaning_preserved(text_a: str, text_b: str, threshold: float = 0.85) -> bool:
-    similarity = get_similarity(text_a, text_b)
-    return similarity >= threshold

main.py

@@ -8,8 +8,6 @@ from dotenv import load_dotenv
 load_dotenv()
 
 from humanizer import humanize_text
-from detector import get_ai_score
-from evaluator import get_similarity, meaning_preserved
 
 from fastapi.middleware.cors import CORSMiddleware
 
@@ -33,35 +31,17 @@ class HumanizeRequest(BaseModel):
 class HumanizeResponse(BaseModel):
     humanized: str
     mode: str
-    score_before: float
-    score_after: float
-    similarity_score: float
-    meaning_preserved: bool
 
 @app.post("/humanize", response_model=HumanizeResponse)
 async def humanize(request: HumanizeRequest):
     if request.mode not in ("simple", "standard", "enhanced"):
-        raise HTTPException(status_code=400, detail="mode must be one of: simple, standard, enhanced")
-        
+        raise HTTPException(
+            status_code=400,
+            detail="mode must be one of: simple, standard, enhanced"
+        )
     try:
-        score_before = get_ai_score(request.text)
         humanized_text = await humanize_text(request.text, mode=request.mode)
-        
-        if not meaning_preserved(request.text, humanized_text):
-            humanized_text = await humanize_text(request.text, mode=request.mode)
-            
-        score_after = get_ai_score(humanized_text)
-        similarity_score = get_similarity(request.text, humanized_text)
-        preserved = meaning_preserved(request.text, humanized_text)
-        
-        return HumanizeResponse(
-            humanized=humanized_text,
-            mode=request.mode,
-            score_before=score_before,
-            score_after=score_after,
-            similarity_score=similarity_score,
-            meaning_preserved=preserved
-        )
+        return HumanizeResponse(humanized=humanized_text, mode=request.mode)
     except Exception as e:
         import traceback
         raise HTTPException(status_code=500, detail=traceback.format_exc())

requirements.txt

@@ -2,8 +2,3 @@ fastapi
 uvicorn
 python-dotenv
 groq
-
-# Large installs: pip install may take a few minutes
-transformers
-torch
-sentence-transformers