Update app.py

app.py

@@ -2,17 +2,19 @@ from fastapi import FastAPI, Request
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 import uvicorn
+import os
+
+# ✅ Fix permission issue on Hugging Face Spaces
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
 
 app = FastAPI(title="AI Detector API")
 
-# Load the model once at startup
 MODEL_NAME = "roberta-base-openai-detector"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
 model.eval()
 
 def get_ai_probability(text: str) -> float:
-    """Return AI probability (0–100%) for the given text."""
     inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
     with torch.no_grad():
         logits = model(**inputs).logits
@@ -22,12 +24,6 @@ def get_ai_probability(text: str) -> float:
 
 @app.post("/analyze")
 async def analyze_text(request: Request):
-    """
-    Example body:
-    {
-      "text": "Your text here"
-    }
-    """
     data = await request.json()
     text = data.get("text", "").strip()
     if not text:
@@ -39,22 +35,3 @@ async def analyze_text(request: Request):
     for i, para in enumerate(paragraphs, start=1):
         ai_score = get_ai_probability(para)
         results.append({
-            "paragraph": i,
-            "ai_score": ai_score,
-            "human_score": round(100 - ai_score, 2),
-            "content_preview": para[:200] + ("..." if len(para) > 200 else "")
-        })
-
-    overall = sum([r["ai_score"] for r in results]) / len(results)
-    return {
-        "overall_ai_score": round(overall, 2),
-        "overall_human_score": round(100 - overall, 2),
-        "paragraphs": results
-    }
-
-@app.get("/")
-async def root():
-    return {"message": "AI Detector API is running. Use POST /analyze"}
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)