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from langchain.chains import LLMChain, SequentialChain |
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from langchain.memory import ConversationBufferMemory |
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import streamlit as st |
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from transformers import AutoModel |
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import torch |
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import transformers |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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st.title('🦜Seon\'s Legal QA For Dummies 🔗 ') |
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model = AutoModelForCausalLM.from_pretrained("PyaeSoneK/Fine_Tuned_Pythia_smallest_140_legal", |
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device_map='auto', |
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torch_dtype=torch.float32, |
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use_auth_token= st.secrets['hf_access_token'], |
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) |
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tokenizer = AutoTokenizer.from_pretrained("PyaeSoneK/Fine_Tuned_Pythia_smallest_140_legal", |
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use_auth_token=st.secrets['hf_access_token'],) |
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from transformers import pipeline |
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pipe = pipeline("text-generation", |
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model=model, |
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tokenizer= tokenizer, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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max_new_tokens = 512, |
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do_sample=True, |
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top_k=30, |
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num_return_sequences=1, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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import json |
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import textwrap |
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import torch |
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B_INST, E_INST = "[INST]", "[/INST]" |
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B_SYS, E_SYS = "<>\n", "\n<>\n\n" |
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DEFAULT_SYSTEM_PROMPT = """\ |
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You are a helpful, respectful and honest legal assistant who will answer legal questions a user would ask with step-by-step explanation and advice. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. |
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If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.Just say you don't know and you are sorry!""" |
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def get_prompt(instruction, new_system_prompt=DEFAULT_SYSTEM_PROMPT, citation=None): |
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SYSTEM_PROMPT = B_SYS + new_system_prompt + E_SYS |
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prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST |
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if citation: |
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prompt_template += f"\n\nCitation: {citation}" |
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return prompt_template |
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def cut_off_text(text, prompt): |
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cutoff_phrase = prompt |
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index = text.find(cutoff_phrase) |
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if index != -1: |
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return text[:index] |
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else: |
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return text |
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def remove_substring(string, substring): |
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return string.replace(substring, "") |
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def generate(text, citation=None): |
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prompt = get_prompt(text, citation=citation) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model.generate(**inputs, |
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max_length=512, |
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eos_token_id=tokenizer.eos_token_id, |
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pad_token_id=tokenizer.eos_token_id, |
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) |
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final_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] |
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final_outputs = cut_off_text(final_outputs, '') |
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final_outputs = remove_substring(final_outputs, prompt) |
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return final_outputs |
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def parse_text(text): |
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wrapped_text = textwrap.fill(text, width=100) |
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print(wrapped_text + '\n\n') |
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from langchain import HuggingFacePipeline |
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from langchain import PromptTemplate, LLMChain |
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llm = HuggingFacePipeline(pipeline = pipe, model_kwargs = {'temperature':0.7,'max_length': 256, 'top_k' :50}) |
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system_prompt = "You are an advanced legal assistant that excels at giving advice. " |
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instruction = "Convert the following input text from stupid to legally reasoned and step-by-step throughout advice:\n\n {text}" |
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template = get_prompt(instruction, system_prompt) |
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print(template) |
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prompt = PromptTemplate(template=template, input_variables=["text"]) |
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llm_chain = LLMChain(prompt=prompt, llm=llm) |
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text = st.text_input('Plug in your prompt here : Try (Employment law: Can I discuss my salary with coworkers?)') |
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if text: |
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response = llm_chain.run(text) |
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st.write(response) |
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st.write(" ") |
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st.write(" ") |
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st.markdown("---") |
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st.write(""" |
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### 💌 Your voice matters |
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""") |
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feedback_input = st.text_area(label= 'please leave your feedback or any ideas to make this bot more knowledgeable and fun') |
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feedback_button = st.button(label = 'Submit feedback!') |
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if feedback_button: |
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update_worksheet_comment() |
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