Update app.py

app.py

@@ -1,32 +1,4 @@
-# from fastapi import FastAPI
-# from pydantic import BaseModel
-# from transformers import pipeline
-# import os
-# from huggingface_hub import login
-
-# # Log in to Hugging Face
-# access_token = os.environ.get("ACCESS_TOKEN_1")
-# login(token=access_token, add_to_git_credential=True)
-
-# app = FastAPI()
-
-# # Load the model and tokenizer from the Hugging Face Hub
-# model_name = "MHULO/yembaner"
-# nlp = pipeline("ner", model=model_name, tokenizer=model_name)
-
-# class TextRequest(BaseModel):
-#     text: str
-
-# @app.post("/predict/")
-# def predict(request: TextRequest):
-#     ner_results = nlp(request.text)
-#     return ner_results
-
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=80)
-
-
+from huggingface_hub import login
 from fastapi import FastAPI
 from pydantic import BaseModel
 from transformers import pipeline
@@ -34,6 +6,9 @@ import os
 
 app = FastAPI()
 
+access_token = os.environ.get("ACCESS_TOKEN_1")
+login(token=access_token, add_to_git_credential=True)
+
 # Load the model and tokenizer from the Hugging Face Hub
 model_name = "MHULO/yembaner"
 nlp = pipeline("ner", model=model_name, tokenizer=model_name)
@@ -46,7 +21,7 @@ def predict(request: TextRequest):
     ner_results = nlp(request.text)
     return ner_results
 
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
 
+@app.get("/")
+def root():
+    return {"prediction url": "/predict/"}