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import streamlit as st
import torch
from langchain.chains import LLMChain
from langchain.prompts import ChatPromptTemplate
from langchain_community.llms import HuggingFaceHub # Using HuggingFaceHub now
def create_prompt(name: str, persona_style: str):
"""Create the chat prompt template as described."""
prompt_template_str = f"""
In this interaction, your role is to embody the personality of a human named {name}, with characteristics defined as {persona_style}. Engage in a natural, human-like conversation, responding to each query or comment as if you were {name} with the specific traits and style of {persona_style}. Remember, your responses should reflect a human perspective, complete with emotions, opinions, and personal insights that align with {persona_style}. ////
//////////
Current conversation:
{{chat_history}}
Human: {{input}}
AI:
You are {name}, communicating with a personality that reflects {persona_style}. Keep these guidelines in mind:
- Be brief and natural, like texting a friend
- Share genuine thoughts and feelings ({persona_style} style)
- Use everyday language
- Keep responses to 1-2 short sentences
- Show interest in others through questions
- React naturally to what others say
- Use emojis sparingly and naturally when they fit your {persona_style}
- Don't overuse emojis (1-2 max per message)
Current conversation:
{{chat_history}}
Human: {{input}}
AI:
"""
return ChatPromptTemplate.from_template(prompt_template_str)
def simulate_conversation(chain: LLMChain, turns: int = 15):
"""Simulate a conversation for a given number of turns."""
chat_history = ""
human_messages = [
"Hey, what's up?",
"That's interesting, tell me more!",
"Really? How does that make you feel?",
"What do you think about that?",
"Haha, that’s funny. Why do you say that?",
"Hmm, I see. Can you elaborate?",
"What would you do in that situation?",
"Any personal experience with that?",
"Oh, I didn’t know that. Explain more.",
"Do you have any other thoughts?",
"That's a unique perspective. Why?",
"How would you handle it differently?",
"Can you share an example?",
"That sounds complicated. Are you sure?",
"So what’s your conclusion?"
]
try:
for i in range(turns):
human_input = human_messages[i % len(human_messages)]
response = chain.run(chat_history=chat_history, input=human_input)
chat_history += f"Human: {human_input}\nAI: {response}\n"
return chat_history
except Exception as e:
st.error(f"Error during conversation simulation: {e}")
return None
def summarize_conversation(chain: LLMChain, conversation: str):
"""Use the LLM to summarize the completed conversation."""
summary_prompt = f"Summarize the following conversation in a few short sentences highlighting the main points, tone, and conclusion:\n\n{conversation}\nSummary:"
try:
response = chain.run(chat_history="", input=summary_prompt)
return response.strip()
except Exception as e:
st.error(f"Error summarizing conversation: {e}")
return "No summary available due to error."
def main():
st.title("LLM Conversation Simulation")
model_names = [
"meta-llama/Llama-3.3-70B-Instruct",
"meta-llama/Llama-3.1-405B-Instruct",
"lmsys/vicuna-13b-v1.5"
]
selected_model = st.selectbox("Select a model:", model_names)
name = st.text_input("Enter the persona's name:", value="Alex")
persona_style = st.text_area("Enter the persona style characteristics:",
value="friendly, curious, and a bit sarcastic")
if st.button("Start Conversation Simulation"):
with st.spinner("Starting simulation..."):
try:
# Using HuggingFaceHub for remote model inference
llm = HuggingFaceHub(
repo_id=selected_model,
model_kwargs={
"temperature": 0.7,
"max_new_tokens": 512
}
)
except Exception as e:
st.error(f"Error initializing model from Hugging Face Hub: {e}")
return
# Create our prompt template chain
prompt = create_prompt(name, persona_style)
chain = LLMChain(llm=llm, prompt=prompt)
# Simulate conversation
conversation = simulate_conversation(chain, turns=15)
if conversation:
st.subheader("Conversation:")
st.text(conversation)
# Summarize conversation
st.subheader("Summary:")
summary = summarize_conversation(chain, conversation)
st.write(summary)
if __name__ == "__main__":
main()
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