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import streamlit as st
import openai
import os
import sys
import argparse
sys.path.append('./lats')
from lats_main import lats_main
st.set_page_config(layout="wide")
# Initialize session state variables if they don't exist.
if 'response_content' not in st.session_state:
st.session_state.response_content = None
# Creating main columns for the chat and runtime notifications
chat_col = st.container()
chat_col.title("CodeLATS")
description = """This demo is an implementation of Language Agent Tree Search (LATS) (https://arxiv.org/abs/2310.04406) built specifically for generating code in the form of python functions. It achieves :green[**state-of-the-art**] results on HumanEval with a :green[**94.4% pass@1 rate**] on GPT-4.
Listed below is an example programming problem (https://leetcode.com/problems/longest-valid-parentheses/description/) to get started with.
```python
Given a string containing just the characters '(' and ')', return the length of the longest valid (well-formed) parentheses substring
```
:red[**NOTE:**] On average a call for a HumanEval or Leetcode question will cost around 5-30 cents on GPT-4, using the default parameters. This value may change depending on problem difficulty and parameters.
"""
chat_col.markdown(description)
sidebar = st.sidebar
# Runtime Section
runtime_container = st.container()
# Parameters Section
sidebar.title("**A Lapis Labs Project** (https://lapis.rocks/)")
parameters_section = sidebar.expander("Parameters", expanded=False)
tree_width = parameters_section.number_input("Tree Width", min_value=1, max_value=5, value=1)
tree_depth = parameters_section.number_input("Tree Depth", min_value=1, max_value=8, value=3)
iterations = parameters_section.number_input("Iterations", min_value=1, max_value=4, value=2)
key = st.sidebar.text_input("Enter your OpenAI Api Key:", type="password")
sidebar.markdown('<hr style="margin-top: 0.5rem; margin-bottom: 0.5rem;">', unsafe_allow_html=True)
with sidebar:
runtime_container = st.container()
runtime_container.empty()
runtime_messages = []
def make_args(instruction, tree_depth, tree_width, iterations):
parser = argparse.ArgumentParser()
parser.add_argument("--strategy", default="mcts", help="Strategy to use")
parser.add_argument("--language", default="py", help="Programming language")
parser.add_argument("--model", default="samba", help="Model type")
parser.add_argument("--max_iters", default=iterations, help="Maximum iterations")
parser.add_argument("--instruction", default=instruction, help="Instruction text")
parser.add_argument("--verbose", action="store_true", help="Verbose output")
parser.add_argument("--is_leetcode", action='store_true',
help="To run the leetcode benchmark") # Temporary
parser.add_argument("--n_samples", type=int,
help="The number of nodes added during expansion", default=tree_width)
parser.add_argument("--depth", type=int,
help="Tree depth", default=tree_depth)
args = parser.parse_args()
return args
def run_querry():
if user_input:
# Create a new container for each subsequent message
runtime_container.write("Initiating process...")
# Make it so that prints go to runtime_container writes instead
old_stdout = sys.stdout
sys.stdout = runtime_container
with chat_col:
with st.spinner('Running...'):
args = make_args(user_input, tree_depth, tree_width, iterations)
# main call
response = lats_main(args)
sys.stdout = old_stdout
runtime_container.write("Response fetched.")
chat_col.markdown('<hr style="margin-top: 0.5rem; margin-bottom: 0.5rem;">', unsafe_allow_html=True)
chat_col.write(f"```python\n{response} \n")
return response
# User input section at the bottom of the page
with chat_col:
user_input = st.text_area("Enter your message here:", placeholder="Type your message here...", label_visibility="collapsed")
button = st.button("Send")
if button:
fail = False
if key == "":
st.warning("Missing OpenAI API Key")
fail = True
if user_input == "":
st.warning("Missing a coding problem")
fail = True
if (not fail):
openai.api_key = key
run_querry()
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