Spaces:
Running
Running
| 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", | |
| "Open-Orca/Mistral-7B-OpenOrca", | |
| ) | |
| 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_length": self.max_token_length | |
| } | |
| os.environ['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 | |
| 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 = """ | |
| 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() |