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import os
import streamlit as st
from langchain.llms import HuggingFaceHub
from langchain.chains import LLMChain
from llm import similarity
from file_manipulation import make_directory_if_not_exists
from models import return_models, return_text2text_generation_models, return_task_name, return_text_generation_models
class LLM_Langchain():
def __init__(self):
dummy_parent = "google"
self.models_count = return_text2text_generation_models(dummy_parent, True) + return_text_generation_models(dummy_parent, True)
st.warning("Warning: Some models may not work and some models may require GPU to run")
st.text(f"As of now there are {self.models_count} model available")
st.text("Made with Langchain, StreamLit, Hugging Face and 💖")
st.header('🦜🔗 One stop for Open Source Models')
# self.API_KEY = st.sidebar.text_input(
# 'API Key',
# type='password',
# help="Type in your HuggingFace API key to use this app")
self.task_name = st.sidebar.selectbox(
label = "Choose the task you want to perform",
options = return_task_name(),
help="Choose your open source LLM to get started"
)
if self.task_name is None:
model_parent_visibility = True
else:
model_parent_visibility = False
model_parent_options = return_models(self.task_name)
model_parent = st.sidebar.selectbox(
label = "Choose your Source",
options = model_parent_options,
help="Choose your source of models",
disabled=model_parent_visibility
)
if model_parent is None:
model_name_visibility = True
else:
model_name_visibility = False
if self.task_name == "text2text-generation":
options = return_text2text_generation_models(model_parent)
else:
options = return_text_generation_models(model_parent)
self.model_name = st.sidebar.selectbox(
label = "Choose your Models",
options = options,
help="Choose your open source LLM to get started",
disabled=model_name_visibility
)
self.temperature = st.sidebar.slider(
label="Temperature",
min_value=0.1,
max_value=1.0,
step=0.1,
value=0.9,
help="Set the temperature to get accurate results"
)
self.max_token_length = st.sidebar.slider(
label="Token Length",
min_value=32,
max_value=1024,
step=32,
value=1024,
help="Set the max tokens to get accurate results"
)
self.model_kwargs = {
"temperature": self.temperature,
"max_new_tokens": self.max_token_length
}
# os.environ['HUGGINGFACEHUB_API_TOKEN'] = self.API_KEY
os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.getenv("HF_KEY")
def generate_response(self, input_text, context):
template = f"<|system|>\nYou are a intelligent chatbot.</s>\n<|user|>\n{input_text}.\n<|assistant|>"
llm = HuggingFaceHub(
repo_id = self.model_name,
model_kwargs = self.model_kwargs
)
llm_chain = LLMChain(
prompt=template,
llm=llm,
)
result = llm_chain.run({
"question": input_text,
"context": context
})
# return llm(input_text)
return result
def radio_button(self):
options = ['FineTune', 'Inference']
selected_option = st.radio(
label="Choose your options",
options=options
)
return selected_option
def pdf_uploader(self):
if self.selected_option == "Inference":
self.uploader_visibility = True
else:
self.uploader_visibility = False
self.file_upload_status = st.file_uploader(
label="Upload PDF file",
disabled=self.uploader_visibility
)
make_directory_if_not_exists('assets/')
if self.file_upload_status is not None:
self.pdf_file_path = f"assets/{self.file_upload_status.name}"
with open(self.pdf_file_path, "wb") as f:
f.write(self.file_upload_status.getbuffer())
st.write("File Uploaded Successfully")
def form_data(self):
# with st.form('my_form'):
try:
# if not self.API_KEY.startswith('hf_'):
# st.warning('Please enter your API key!', icon='⚠')
self.selected_option = self.radio_button()
self.pdf_uploader()
if self.selected_option == "FineTune":
if self.file_upload_status is None:
text_input_visibility = True
else:
text_input_visibility = False
else:
text_input_visibility = False
if "messages" not in st.session_state:
st.session_state.messages = []
st.write(f"You are using {self.model_name} 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"
)
text = st.chat_input(disabled=text_input_visibility)
if text:
st.session_state.messages.append(
{
"role":"user",
"content": text
}
)
with st.chat_message("user"):
st.write(text)
if text.lower() == "clear":
del st.session_state.messages
return
if self.selected_option == 'FineTune':
result = similarity(self.pdf_file_path, self.model_name, self.model_kwargs, text)
else:
result = self.generate_response(text, context)
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 = LLM_Langchain()
model.form_data() |