new files

app.py

@@ -1,67 +0,0 @@
-import streamlit as st
-import transformers
-from transformers import (
-    GPT2Config,
-    GPT2Tokenizer)
-tokenizer = GPT2Tokenizer.from_pretrained("gpt2",pad_token='<|endoftext|>') 
-from model_file import FlaxGPT2ForMultipleChoice
-import jax.numpy as jnp
-
-st.title('GPT2 for common sense reasoning')
-st.write('Multiple Choice Question Answering using CosmosQA Dataset')
-
-context=st.text_area('Context',height=25)
-st.write(context)
-#context = st.text_input('Context :')
-
-
-
-
-
-question=st.text_input('Question')
-
-
-buff, col, buff2 = st.beta_columns([5,1,2])
-choice_a=buff.text_input('choice 0:')
-choice_b=buff.text_input('choice 1:')
-choice_c=buff.text_input('choice 2:')
-choice_d=buff.text_input('choice 3:')
-
-a={}
-def preprocess(context,question,choice_a,choice_b,choice_c,choice_d):
-    a['context&question']=context+question
-    a['first_sentence']=[a['context&question'],a['context&question'],a['context&question'],a['context&question']]
-    a['second_sentence']=choice_a,choice_b,choice_c,choice_d
-    return a
-
-preprocessed_data=preprocess(context,question,choice_a,choice_b,choice_c,choice_d)
-
-def tokenize(examples):
-    b=tokenizer(examples['first_sentence'],examples['second_sentence'],padding='max_length',truncation=True,max_length=256,return_tensors='jax')
-    return b
-
-tokenized_data=tokenize(preprocessed_data)
-
-
-model = FlaxGPT2ForMultipleChoice.from_pretrained("flax-community/gpt2-Cosmos",input_shape=(1,4,1))
-
-input_id=jnp.array(tokenized_data['input_ids'])
-att_mask=jnp.array(tokenized_data['attention_mask'])
-
-
-if st.button("Run"):
-    with st.spinner(text="Getting results..."):
-        outputs=model(input_id,att_mask)
-        final_output=jnp.argmax(outputs,axis=-1)
-        if final_output==0:
-            result='0'
-        elif final_output==1:
-            result='1'
-        elif final_output==2:
-            result='2'
-        elif final_output==3:
-            result='3'
-    st.success(f"The answer is choice {result1}")
-
-
-