adding app.py

app.py

@@ -11,6 +11,19 @@ def load_model_and_tokenizer(model_name):
 
 tokenizer,model = load_model_and_tokenizer(MODEL_NAME)
 
+MAX_TOKS = 128
+MAX_NEW_TOKS = 128 
+def generate_text(prompt):
+    inputs = tokenizer(prompt, truncation=True, return_tensors="pt")
+    output_seq = model.generate(
+        input_ids=inputs.input_ids, max_length=MAX_TOKS,
+        max_new_tokens=MAX_NEW_TOKS,
+        do_sample=True, temperature=0.8,
+        num_return_sequences=1
+    )
+
+    outputs = tokenizer.batch_decode(output_seq, skip_special_tokens=False)
+    return outputs
 st.set_page_config(
 page_title= "Code Representation Learning",
     initial_sidebar_state= "expanded"
@@ -19,18 +32,8 @@ 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 = {}
+    prompt = st.text_input("Natural Language prompt ","list all the files in the directory 'data\' ")
     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")
+    generated_text = generate_text(prompt)
+    st.write(generated_text)