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_dpo", "Sharathhebbar24/chat_gpt2", "Sharathhebbar24/math_gpt2_sft", "Sharathhebbar24/math_gpt2", "Sharathhebbar24/convo_bot_gpt_v1", "Sharathhebbar24/Instruct_GPT", "Sharathhebbar24/Mistral-7B-v0.1-sharded", "Sharathhebbar24/llama_chat_small_7b", "Deci/DeciCoder-6B", "Deci/DeciLM-7B-instruct", "Deci/DeciCoder-1b", "Deci/DeciLM-7B-instruct-GGUF", "Open-Orca/Mistral-7B-OpenOrca", "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "Sharathhebbar24/llama_7b_chat", "CultriX/MistralTrix-v1", "ahxt/LiteLlama-460M-1T", "gorilla-llm/gorilla-7b-hf-delta-v0", "codeparrot/codeparrot" ) 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("HF_KEY") 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}.\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()