Create app.py

app.py

@@ -0,0 +1,55 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI()
+
+# Enable CORS (Allows your React Frontend to talk to this API)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load Model (Global Variable)
+MODEL_PATH = "/code/model"
+print("Loading AI Model...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+
+class InputData(BaseModel):
+    sentence: str
+
+@app.get("/")
+def home():
+    return {"status": "Online", "model": "BanglaBERT"}
+
+@app.post("/api/predict")
+def predict(data: InputData):
+    try:
+        # Tokenize
+        inputs = tokenizer(data.sentence, return_tensors="pt", padding=True, truncation=True, max_length=64)
+
+        # Predict
+        with torch.no_grad():
+            logits = model(**inputs).logits
+
+        # Calculate Confidence
+        probs = torch.nn.functional.softmax(logits, dim=1)
+        conf = torch.max(probs).item()
+        pred_id = torch.argmax(probs).item()
+
+        # Label Mapping (1=Shirk, 0=Not Shirk)
+        label = "shirk" if pred_id == 1 else "not shirk"
+
+        return {
+            "result": label,
+            "confidence": f"{conf:.2%}",
+            "cleaned_sentence": data.sentence
+        }
+    except Exception as e:
+        return {"error": str(e)}