Upload 5 files

api.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AlbertForSequenceClassification, AlbertTokenizer
+import torch
+
+app = FastAPI()
+
+MODEL_PATH = "./saved_model"
+
+tokenizer = AlbertTokenizer.from_pretrained(MODEL_PATH)
+model = AlbertForSequenceClassification.from_pretrained(MODEL_PATH)
+
+model.eval()
+
+class JobInput(BaseModel):
+    description: str
+
+@app.get("/")
+def read_root():
+    return {
+        "message": "ScamZero Prediction API is running!",
+        "endpoints": {
+            "predict": "/predict (POST)",
+            "health": "/health (GET)"
+        }
+    }
+
+@app.get("/health")
+def health_check():
+    return {"status": "healthy", "model": "albert-base-v2"}
+
+@app.post("/predict")
+def predict(data: JobInput):
+
+    text = data.description
+
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+        max_length=256
+    )
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+        probs = torch.softmax(outputs.logits, dim=-1)
+        prediction = torch.argmax(probs, dim=-1).item()
+        confidence = probs[0][prediction].item()
+
+    return {
+        "prediction": prediction,
+        "label": "Scam" if prediction == 1 else "Safe",
+        "confidence": confidence
+    }

saved_model/config.json

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+size 981

saved_model/model.safetensors

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+size 46743904

saved_model/tokenizer.json

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+size 1957992

saved_model/tokenizer_config.json

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