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("SambaLATS") description = """This demo is an implementation of Language Agent Tree Search (LATS) (https://arxiv.org/abs/2310.04406) with Samba-1 in the backend. Thank you to the original authors of demo on which this is based from [Lapis Labs](https://lapis.rocks/)! Given Samba-1's lightning quick inference, not only can we accelerate our system's speeds but also improve our system's accuracy. Using many inference calls in this LATS style, we can solve programming questions with higher accuracy. In fact, this system reaches GPT-3.5 accuracy on HumanEval Python, 74% accuracy, with LLaMa 3 8B, taking 8 seconds on average. This is a 15.5% boost on LLaMa 3 8B alone. Listed below is an example programming problem (https://leetcode.com/problems/median-of-two-sorted-arrays/description/) to get started with. ```python Given two sorted arrays `nums1` and `nums2` of size `m` and `n` respectively, return **the median** of the two sorted arrays. The overall run time complexity should be `O(log (m+n))`. **Example 1:** **Input:** nums1 = \[1,3\], nums2 = \[2\] **Output:** 2.00000 **Explanation:** merged array = \[1,2,3\] and median is 2. **Example 2:** **Input:** nums1 = \[1,2\], nums2 = \[3,4\] **Output:** 2.50000 **Explanation:** merged array = \[1,2,3,4\] and median is (2 + 3) / 2 = 2.5. **Constraints:** * `nums1.length == m` * `nums2.length == n` * `0 <= m <= 1000` * `0 <= n <= 1000` * `1 <= m + n <= 2000` * `-106 <= nums1[i], nums2[i] <= 106` ``` """ chat_col.markdown(description) sidebar = st.sidebar # Runtime Section runtime_container = st.container() # Parameters Section sidebar.title("From SambaNova Systems") 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) sidebar.markdown('