Create app.py

app.py

@@ -0,0 +1,47 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from fastapi import FastAPI
+from pydantic import BaseModel
+import torch
+
+# Initialize FastAPI
+app = FastAPI(title="Check-ins Classifier API", version="1.0")
+
+# Load model and tokenizer
+MODEL_NAME = "mjpsm/check-ins-classifier"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+model.eval()
+
+# Define label mapping
+id2label = {
+    0: "Bad",
+    1: "Mediocre",
+    2: "Good"
+}
+
+# Input schema
+class InputText(BaseModel):
+    text: str
+
+@app.post("/predict")
+async def predict(data: InputText):
+    # Tokenize input
+    inputs = tokenizer(data.text, return_tensors="pt", truncation=True, padding=True)
+    
+    # Model inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        predicted_label_id = torch.argmax(probs, dim=-1).item()
+    
+    # Return JSON response
+    return {
+        "input_text": data.text,
+        "predicted_label": id2label[predicted_label_id],
+        "label_id": predicted_label_id,
+        "probabilities": probs.tolist()
+    }
+
+@app.get("/")
+async def home():
+    return {"message": "Welcome to the Check-ins Classifier API. Use POST /predict to classify text."}