Create app.py

app.py

@@ -0,0 +1,36 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+app = FastAPI()
+
+model_name = "King-8/help-classifier"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+labels = [
+    "learning_help",
+    "project_help",
+    "attendance_issue",
+    "technical_issue",
+    "general_guidance"
+]
+
+class InputText(BaseModel):
+    text: str
+
+@app.post("/predict")
+def predict(input: InputText):
+    inputs = tokenizer(input.text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+
+    probs = F.softmax(outputs.logits, dim=1)
+    predicted_class = probs.argmax().item()
+
+    return {
+        "label": labels[predicted_class]
+    }
+