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import os
import streamlit as st
from langchain.llms import HuggingFaceHub
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
class UserInterface():
def __init__(self, ):
st.warning("Warning: Some models may not work and some models may require GPU to run")
st.text("An Open Source Chat Application")
st.header("Open LLMs")
# self.API_KEY = st.sidebar.text_input(
# 'API Key',
# type='password',
# help="Type in your HuggingFace API key to use this app"
# )
models_name = (
"HuggingFaceH4/zephyr-7b-beta",
"Sharathhebbar24/chat_gpt2",
"Sharathhebbar24/convo_bot_gpt_v1",
"Open-Orca/Mistral-7B-OpenOrca",
"TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"Sharathhebbar24/llama_7b_chat",
"CultriX/MistralTrix-v1",
"ahxt/LiteLlama-460M-1T",
)
self.models = st.sidebar.selectbox(
label="Choose your models",
options=models_name,
help="Choose your model",
)
self.temperature = st.sidebar.slider(
label='Temperature',
min_value=0.1,
max_value=1.0,
step=0.1,
value=0.5,
help="Set the temperature to get accurate or random result"
)
self.max_token_length = st.sidebar.slider(
label="Token Length",
min_value=32,
max_value=2048,
step=16,
value=64,
help="Set max tokens to generate maximum amount of text output"
)
self.model_kwargs = {
"temperature": self.temperature,
"max_new_tokens": self.max_token_length
}
os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.getenv("amira")
def form_data(self):
try:
# if not self.API_KEY.startswith('hf_'):
# st.warning('Please enter your API key!', icon='⚠')
# text_input_visibility = True
# else:
# text_input_visibility = False
text_input_visibility = False
if "messages" not in st.session_state:
st.session_state.messages = []
st.write(f"You are using {self.models} model")
for message in st.session_state.messages:
with st.chat_message(message.get('role')):
st.write(message.get("content"))
context = st.sidebar.text_input(
label="Context",
help="Context lets you know on what the answer should be generated"
)
question = st.chat_input(
key="question",
disabled=text_input_visibility
)
template = f"<|system|>\nYou are a intelligent chatbot and expertise in {context}.</s>\n<|user|>\n{question}.\n<|assistant|>"
# template = """
# Answer the question based on the context, if you don't know then output "Out of Context"
# Context: {context}
# Question: {question}
# Answer:
# """
prompt = PromptTemplate(
template=template,
input_variables=[
'question',
'context'
]
)
llm = HuggingFaceHub(
repo_id = self.models,
model_kwargs = self.model_kwargs
)
if question:
llm_chain = LLMChain(
prompt=prompt,
llm=llm,
)
result = llm_chain.run({
"question": question,
"context": context
})
if "Out of Context" in result:
result = "Out of Context"
st.session_state.messages.append(
{
"role":"user",
"content": f"Context: {context}\n\nQuestion: {question}"
}
)
with st.chat_message("user"):
st.write(f"Context: {context}\n\nQuestion: {question}")
if question.lower() == "clear":
del st.session_state.messages
return
st.session_state.messages.append(
{
"role": "assistant",
"content": result
}
)
with st.chat_message('assistant'):
st.markdown(result)
except Exception as e:
st.error(e, icon="🚨")
model = UserInterface()
model.form_data() |