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import os
import streamlit as st
from langchain.llms import HuggingFaceHub
from models import return_models, return_text2text_generation_models, return_task_name, return_text_generation_models
dummy_parent = "google"
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 {models_count} model available")
st.text("Made with Langchain, StreamLit, Hugging Face and 💖")
st.header('🦜🔗 One stop for Open Source Models')
API_KEY = st.sidebar.text_input(
'API Key',
type='password',
help="Type in your HuggingFace API key to use this app")
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 task_name is None:
model_parent_visibility = True
else:
model_parent_visibility = False
model_parent_options = return_models(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 task_name == "text2text-generation":
options = return_text2text_generation_models(model_parent)
else:
options = return_text_generation_models(model_parent)
model_name = st.sidebar.selectbox(
label = "Choose your Models",
options = options,
help="Choose your open source LLM to get started",
disabled=model_name_visibility
)
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"
)
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"
)
os.environ['HUGGINGFACEHUB_API_TOKEN'] = API_KEY
def generate_response(input_text):
model_kwargs = {
"temperature": temperature,
"max_length": max_token_length
}
llm = HuggingFaceHub(
repo_id = model_name,
model_kwargs = model_kwargs
)
st.info(llm(input_text))
with st.form('my_form'):
try:
text = st.text_area('Enter Your Prompt', 'What are the three key pieces of advice for learning how to code?')
submitted = st.form_submit_button('Submit')
if not API_KEY.startswith('hf_'):
st.warning('Please enter your API key!', icon='⚠')
if submitted and API_KEY.startswith('hf_'):
with st.spinner("Running...."):
generate_response(text)
except Exception as e:
st.error(e, icon="🚨") |