Update app.py

app.py

@@ -1,30 +1,22 @@
 from fastapi import FastAPI, Request
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+import uvicorn
 
 app = FastAPI(title="AI Detector API")
 
-# Load model once at startup
+# Load model
 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 the AI probability (0–100%) for a given text."""
     inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
     with torch.no_grad():
         logits = model(**inputs).logits
         probs = torch.softmax(logits, dim=1)
-    ai_score = probs[0][1].item() * 100
-    return round(ai_score, 2)
-
-@app.get("/")
-async def home():
-    return {
-        "message": "Welcome to Saber’s AI Detector API 👋",
-        "usage": "Send a POST request to /analyze with JSON {'text': 'your article text'}"
-    }
+    return round(probs[0][1].item() * 100, 2)
 
 @app.post("/analyze")
 async def analyze_text(request: Request):
@@ -42,7 +34,7 @@ async def analyze_text(request: Request):
             "paragraph": i,
             "ai_score": ai_score,
             "human_score": round(100 - ai_score, 2),
-            "content": para[:200] + ("..." if len(para) > 200 else "")
+            "content_preview": para[:200] + ("..." if len(para) > 200 else "")
         })
 
     overall = sum([r["ai_score"] for r in results]) / len(results)
@@ -51,3 +43,10 @@ async def analyze_text(request: Request):
         "overall_human_score": round(100 - overall, 2),
         "paragraphs": results
     }
+
+@app.get("/")
+async def root():
+    return {"message": "AI Detector API running on port 8080"}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8080)