Update app.py

app.py

@@ -1,58 +1,3 @@
-# from transformers import AutoTokenizer, AutoModelForCausalLM, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments
-# import torch
-# import streamlit as st
-
-# st.write("im here")
-# # Load the pre-trained tokenizer and model
-# tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
-# model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
-
-
-# dataa = "My name is youssef khemiri i am 21 years old and i am a data scientist"
-# st.write(dataa)
-
-# # Prepare the dataset
-# train_dataset = TextDataset(
-#     tokenizer=tokenizer,
-#     file_path=dataa,
-#     block_size=128,
-# )
-# st.write("hi1")
-# # Prepare the data collator
-# data_collator = DataCollatorForLanguageModeling(
-#     tokenizer=tokenizer, mlm=False,
-# )
-
-# # Initialize the trainer
-# training_args = TrainingArguments(
-#     output_dir='./results',          # output directory
-#     num_train_epochs=3,              # total number of training epochs
-#     per_device_train_batch_size=16,  # batch size per device during training
-#     save_steps=10_000,              # number of steps between saving checkpoints
-#     save_total_limit=2,             # limit the total amount of checkpoints to save
-#     prediction_loss_only=True,
-#     learning_rate=5e-5,
-# )
-# st.write("hi2")
-
-# trainer = Trainer(
-#     model=model,
-#     args=training_args,
-#     train_dataset=train_dataset,
-#     data_collator=data_collator,
-# )
-
-# # Fine-tune the model
-# trainer.train()
-# st.write("finished training")
-
-# # infer
-# inputs = tokenizer("<human>: Tell me about youssef khemiri\n<bot>:", return_tensors='pt').to(model.device)
-# outputs = model.generate(**inputs, max_new_tokens=10, do_sample=True, temperature=0.8)
-# output_str = tokenizer.decode(outputs[0])
-# st.write(output_str)
-
-
 from transformers import pipeline
 import streamlit as st
 from streamlit_chat import message
@@ -101,8 +46,8 @@ question = get_text()
 # while True:
 
 # Generate the answer using the model
-answer = qa_model(question=question, context=context)
 message(question,is_user=True)
+answer = qa_model(question=question, context=context)
 message(answer)
 
 # Print the answer