add the fine tuned BERT model with FAST API integrated in the Flask app

model/api/api.py

@@ -13,7 +13,7 @@ BASE_DIR = os.path.dirname(BASE_DIR)
 MODEL_DIR = os.path.join(BASE_DIR, "intent_classifier_model")
 TOKENIZER_DIR = os.path.join(BASE_DIR, "intent_classifier_tokenizer")
 
-# Ensure model and tokenizer directories exist
+# Ensure model and tokenizer directories exist  
 if not os.path.isdir(MODEL_DIR):
     raise FileNotFoundError(f"Model directory not found: {MODEL_DIR}")
 if not os.path.isdir(TOKENIZER_DIR):

model/api/start_server.py

@@ -2,3 +2,4 @@ import uvicorn
 
 if __name__ == "__main__":
     uvicorn.run("api:app", host="0.0.0.0", port=8000, reload=True)
+ 

model/api/test.py

@@ -6,3 +6,4 @@ data = {"text": "I want to set an alarm for 7 AM tomorrow."}
 response = requests.post(url, json=data)
 print("Status code:", response.status_code)
 print("Response:", response.json())
+ 

src/fastapi_server.py

@@ -15,3 +15,4 @@ def predict(req: PredictRequest):
     else:
         intent = "unknown"
     return {"intent": intent}
+