added final files

app.py

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+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
+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)
+
+#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,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'])
+
+input_id=input_id.reshape(1,4,-1)
+att_mask=att_mask.reshape(1,4,-1)
+
+if st.button("Run"):
+    with st.spinner(text="Getting results..."):
+        outputs=model(input_id,att_mask)
+        final_output=jnp.argmax(outputs,axis=-1)
+        #output=jax.device_get(final_output).item()
+        st.success(f"The answer is choice {final_output}")
+
+
+