feat: added model file

main.py

@@ -1,46 +1,47 @@
-# from fastapi import FastAPI
-# from pydantic import BaseModel
-# from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, TextClassificationPipeline
-# import uvicorn
-
-# # Define FastAPI app
-# app = FastAPI()
-
-# # Load Model on Startup
-# HUGGINGFACE_MODEL_PATH = "bespin-global/klue-roberta-small-3i4k-intent-classification"
-
-# print("Loading model...")  # Log message
-# try:
-#     loaded_tokenizer = RobertaTokenizerFast.from_pretrained(HUGGINGFACE_MODEL_PATH)
-#     loaded_model = RobertaForSequenceClassification.from_pretrained(HUGGINGFACE_MODEL_PATH)
-
-#     # Create Text Classification Pipeline
-#     text_classifier = TextClassificationPipeline(
-#         tokenizer=loaded_tokenizer,
-#         model=loaded_model,
-#         return_all_scores=True
-#     )
-#     print("Model loaded successfully.")  # Log message
-# except Exception as e:
-#     print(f"Error loading model: {e}")
-#     text_classifier = None
-
-
-# # Define Request Model
-# class PredictionRequest(BaseModel):
-#     text: str
-
-# @app.get("/")
-# def home(request):
-#     return {"message":"Running fine"}
-# # Prediction Endpoint
-# @app.post("/predict")
-# def predict_intent(request: PredictionRequest):
-#     if text_classifier is None:
-#         return {"error": "Model not found"}
-#     preds_list = text_classifier(request.text)
-#     best_pred = max(preds_list[0], key=lambda x: x["score"])  # Get highest-scoring intent
-#     return {"intent": best_pred["label"], "confidence": best_pred["score"]}
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, TextClassificationPipeline
+import uvicorn
+
+# Define FastAPI app
+app = FastAPI()
+
+# Load Model on Startup
+HUGGINGFACE_MODEL_PATH = "bespin-global/klue-roberta-small-3i4k-intent-classification"
+
+print("Loading model...")  # Log message
+try:
+    loaded_tokenizer = RobertaTokenizerFast.from_pretrained(HUGGINGFACE_MODEL_PATH)
+    loaded_model = RobertaForSequenceClassification.from_pretrained(HUGGINGFACE_MODEL_PATH)
+
+    # Create Text Classification Pipeline
+    text_classifier = TextClassificationPipeline(
+        tokenizer=loaded_tokenizer,
+        model=loaded_model,
+        return_all_scores=True
+    )
+    print("Model loaded successfully.")  # Log message
+except Exception as e:
+    print(f"Error loading model: {e}")
+    text_classifier = None
+
+
+# Define Request Model
+class PredictionRequest(BaseModel):
+    text: str
+
+@app.get("/")
+def hello():
+    return {"Message":"Space is running Good.",
+            "Status":"Healthy"}
+# Prediction Endpoint
+@app.post("/predict")
+def predict_intent(request: PredictionRequest):
+    if text_classifier is None:
+        return {"error": "Model not found"}
+    preds_list = text_classifier(request.text)
+    best_pred = max(preds_list[0], key=lambda x: x["score"])  # Get highest-scoring intent
+    return {"intent": best_pred["label"], "confidence": best_pred["score"]}
 
 
 # # Launch FastAPI with Uvicorn
@@ -48,11 +49,9 @@
 #     uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
 
 
-from fastapi import FastAPI
+# from fastapi import FastAPI
+
+# app=FastAPI()
 
-app=FastAPI()
 
-@app.get("/")
-def hello():
-    return {"hello":"success"}