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import streamlit as st
import os
import langchain
import langchain_huggingface
from langchain_huggingface import HuggingFaceEndpoint,HuggingFacePipeline,ChatHuggingFace
from langchain_core.messages import HumanMessage,AIMessage,SystemMessage
deep_seek_skeleton = HuggingFaceEndpoint(repo_id='meta-llama/Llama-3.2-3B-Instruct',
provider = 'sambanova',
temperature=0.7,
max_new_tokens=150,
task = 'conversational')
deep_seek = ChatHuggingFace(llm=deep_seek_skeleton,
repo_id='meta-llama/Llama-3.2-3B-Instruct',
provider = 'sambanova',
temperature=0.7,
max_new_tokens=150,
task = 'conversational')
exp1 = ['<1', '1', '2', '3', '4', '5', '5+']
exp = st.selectbox("Select experience:", exp1)
if exp == '<1':
experince = 'New bie mentor'
elif exp == '1':
experince = '1'
elif exp == '2':
experince = '2'
elif exp == '3':
experince = '3'
elif exp == '4':
experince = '4'
elif exp == '5':
experince = '5'
elif exp == '5+':
experince = 'professional'
selec = ['Python', 'Machine Learning', 'Deep Learning', 'Statistics', 'SQL', 'Excel']
sub = st.selectbox("Select experience:", selec)
user_input = st.text_input("Enter your query:")
l = []
st.write(l)
message = [SystemMessage(content=f'Act as {sub} mentor who has {experince} years of experience and the one who teaches in very friendly manner and also he explains everything within 150 words'),
HumanMessage(content=user_input)]
while user_input!='end':
l.append(user_input)
l.append(result.content)
st.write(l)
user_input = st.text_input("Enter your query:")
message = [SystemMessage(content=f'Act as {sub} mentor who has {experince} years of experience and the one who teaches in very friendly manner and also he explains everything within 150 words'),
HumanMessage(content=user_input)]
result = deep_seek.invoke(message)
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