adding basic model addition

main.py

@@ -0,0 +1,36 @@
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+MODEL_NAME = "reshinthadith"
+def load_model_and_tokenizer(model_name):
+    """Adding load_model_and_tokenizer function to keep the model in the memory"""
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    return tokenizer,model
+
+tokenizer,model = load_model_and_tokenizer(MODEL_NAME)
+
+st.set_page_config(
+page_title= "Code Representation Learning",
+    initial_sidebar_state= "expanded"
+    )
+st.sidebar.title("Code Representation Learning")
+workflow = st.sidebar.selectbox('select a task', ['Bash Synthesis'])
+if workflow == "Bash Synthesis":
+    st.title("Program Synthesis for Bash")
+    arxiv_id = st.text_input("Natural Language prompt ","list all the files in the directory 'data\' ")
+    output_diction = {}
+    button = st.button("synthesize")
+if button:
+    link_gen = r"https://arxiv.org/abs/"
+    webbrowser.open_new_tab(link_gen+str(arxiv_id))
+# Abstract 
+    with st.beta_expander("Abstract"):
+        st.write(output_diction["abstract"])
+    with st.beta_expander("Influencing Citations"):
+        st.write(output_diction["influenital_citations"])
+    with st.beta_expander("Citation Graph"):
+        print("")
+elif workflow == "Rebuttal Analysis":
+    st.title("Rebuttal Analysis")