added token

app.py

@@ -1,5 +1,7 @@
 from fastapi import FastAPI, UploadFile, File
 from transformers import pipeline
+from transformers import AutoTokenizer
+import transformers
 from fastai.vision.all import *
 from PIL import Image
 import os
@@ -12,24 +14,6 @@ access_token = os.getenv("HF_TOKEN")
 # of the app. This way, we can use the docs as a landing page for the app on Spaces.
 app = FastAPI(docs_url="/")
 
-pipe = pipeline("text2text-generation", model="google/flan-t5-small")
-categories = ('Heart', 'Oblong', 'Oval', 'Round', 'Square')
-learn = load_learner('model.pkl')
-
-# Überprüfe, ob das Zugriffstoken vorhanden ist
-if access_token is None:
-    raise ValueError("Access token is missing. Make sure it is set as an environment variable.")
-
-@app.get("/generate")
-def generate(text: str):
-    """
-    Using the text2text-generation pipeline from `transformers`, generate text
-    from the given input text. The model used is `google/flan-t5-small`, which
-    can be found [here](https://huggingface.co/google/flan-t5-small).
-    """
-    output = pipe(text)
-    return {"output": output[0]["generated_text"]}
-
 @app.post("/face-analyse")
 async def face_analyse(file: UploadFile = File(...)):
     # Read the uploaded file content
@@ -62,7 +46,7 @@ async def face_analyse(file: UploadFile = File(...)):
 
 # Initialisiere das Modell und den Tokenizer
 model = "meta-llama/CodeLlama-7b-hf"
-tokenizer = AutoTokenizer.from_pretrained(model)
+tokenizer = AutoTokenizer.from_pretrained(model, token=access_token)
 llama_pipeline = pipeline(
     "text-generation",
     model=model,