Update app.py

app.py

@@ -29,8 +29,8 @@ logging.basicConfig(level=logging.INFO)
 HF_TOKEN = os.getenv("manhal")
 
 TEXT_MODEL_NAME = "aubmindlab/aragpt2-mega"
-text_tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_NAME, token = HF_TOKEN)
-text_model = AutoModelForCausalLM.from_pretrained(TEXT_MODEL_NAME,  token = HF_TOKEN)
+text_tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_NAME, token = HF_TOKEN,trust_remote_code=True)
+text_model = AutoModelForCausalLM.from_pretrained(TEXT_MODEL_NAME,  token = HF_TOKEN, trust_remote_code=True)
 
 general_prompt_template = """
 أنت الآن نموذج لغة مخصص لتوليد نصوص عربية تعليمية بناءً على المادة والمستوى التعليمي. سيتم إعطاؤك مادة تعليمية ومستوى تعليمي، وعليك إنشاء نص مناسب بناءً على ذلك. النص يجب أن يكون:
@@ -89,8 +89,8 @@ def generate_text(request: GenerateTextRequest):
 # Question & Answer Generation Model
 ####################################
 QA_MODEL_NAME = "Mihakram/AraT5-base-question-generation"
-qa_tokenizer = AutoTokenizer.from_pretrained(QA_MODEL_NAME, token = HF_TOKEN)
-qa_model = AutoModelForSeq2SeqLM.from_pretrained(QA_MODEL_NAME, token = HF_TOKEN)
+qa_tokenizer = AutoTokenizer.from_pretrained(QA_MODEL_NAME, token = HF_TOKEN, trust_remote_code=True)
+qa_model = AutoModelForSeq2SeqLM.from_pretrained(QA_MODEL_NAME, token = HF_TOKEN, trust_remote_code=True)
 
 def extract_answer(context: str) -> str:
     """Extract the first sentence (or a key phrase) from the context."""
@@ -142,6 +142,6 @@ def read_root():
 ####################################
 # Running the FastAPI Server
 ####################################
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+import uvicorn
+uvicorn.run(app, host="0.0.0.0", port=7860)