import os import streamlit as st from langchain.llms import HuggingFaceHub 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_length": self.max_token_length } os.environ['HUGGINGFACEHUB_API_TOKEN'] = self.API_KEY def generate_response(self, input_text): llm = HuggingFaceHub( repo_id = self.model_name, model_kwargs = self.model_kwargs ) return llm(input_text) 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")) 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) 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()