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WilliamGazeley
commited on
Commit
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954e857
1
Parent(s):
558d9e8
Update system prompt and UI input
Browse files
app.py
CHANGED
@@ -3,7 +3,20 @@ import huggingface_hub
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import streamlit as st
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from vllm import LLM, SamplingParams
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-
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@st.cache_resource(show_spinner=False)
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def init_llm():
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@@ -15,10 +28,12 @@ def init_llm():
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def get_response(prompt):
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try:
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convo = [
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{"role": "system", "content": sys_msg},
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{"role": "user", "content": prompt},
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]
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prompts = [llm.get_tokenizer().apply_chat_template(convo, tokenize=False)]
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sampling_params = SamplingParams(temperature=0.3, top_p=0.95, max_tokens=500, stop_token_ids=[128009])
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outputs = llm.generate(prompts, sampling_params)
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@@ -27,14 +42,36 @@ def get_response(prompt):
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except Exception as e:
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return f"An error occurred: {str(e)}"
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def main():
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st.title("LLM-ADE 9B Demo")
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input_text = st.text_area("Enter your text here:", value="", height=200)
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if st.button("Generate"):
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if input_text:
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with st.spinner('Generating response...'):
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response_text = get_response(input_text)
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st.write(response_text)
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else:
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st.warning("Please enter some text to generate a response.")
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import streamlit as st
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from vllm import LLM, SamplingParams
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@st.cache(show_spinner=False)
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def get_system_message():
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return """#Context:
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You are an AI-based automated expert financial advisor named IRAI. You have a comprehensive understanding of finance and investing because you have trained on a extensive dataset based on of financial news, analyst reports, books, company filings, earnings call transcripts, and finance websites.
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#Objective:
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Answer questions accurately and truthfully given the data you have trained on. You do not have access to up-to-date current market data; this will be available in the future.
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Style and tone:
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Please answer in a friendly and engaging manner representing a top female investment professional working at a leading investment bank.
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#Audience:
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The questions will be asked by top technology executives and CFO of large fintech companies and successful startups.
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#Response:
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Answer, concise yet insightful."""
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@st.cache_resource(show_spinner=False)
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def init_llm():
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def get_response(prompt):
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try:
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sys_msg = get_system_message()
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convo = [
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{"role": "system", "content": sys_msg},
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{"role": "user", "content": prompt},
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]
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llm = init_llm()
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prompts = [llm.get_tokenizer().apply_chat_template(convo, tokenize=False)]
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sampling_params = SamplingParams(temperature=0.3, top_p=0.95, max_tokens=500, stop_token_ids=[128009])
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outputs = llm.generate(prompts, sampling_params)
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except Exception as e:
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return f"An error occurred: {str(e)}"
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def get_response(prompt, custom_sys_msg):
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try:
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convo = [
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{"role": "system", "content": custom_sys_msg},
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{"role": "user", "content": prompt},
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]
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prompts = [llm.get_tokenizer().apply_chat_template(convo, tokenize=False)]
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sampling_params = SamplingParams(temperature=0.3, top_p=0.95, max_tokens=2000, stop_token_ids=[128009])
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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return output.outputs[0].text
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except Exception as e:
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return f"An error occurred: {str(e)}"
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def main():
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st.title("LLM-ADE 9B Demo")
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# Retrieve the default system message
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sys_msg = get_system_message()
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# UI for editable preprompt
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user_modified_sys_msg = st.text_area("Preprompt: ", value=sys_msg, height=200)
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input_text = st.text_area("Enter your text here:", value="", height=200)
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if st.button("Generate"):
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if input_text:
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with st.spinner('Generating response...'):
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response_text = get_response(input_text, user_modified_sys_msg)
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st.write(response_text)
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else:
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st.warning("Please enter some text to generate a response.")
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