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import streamlit as st
import os
import pandas as pd
from utils.helper import *
os.system("pip install together")
os.system("python3 -m pip install --no-cache-dir together")
from together import Together
st.set_page_config(layout="wide")
st.title("Dual Agent ChatBot 🤖")
with st.sidebar:
with st.expander("Instruction Manual"):
st.markdown("""
# Duel Agent ChatBot Streamlit App
## Overview
Welcome to the **Duel Agent ChatBot** app! This Streamlit application allows you to chat with Meta's Llama3 model in a unique interview simulation. The app features two agents in an interview scenario, with a judge providing feedback. The best part? You simply provide a topic, and the simulation runs itself!
## Features
### 📝 Instruction Manual
**Meta Llama3 🦙 Chatbot**
This application lets you interact with Meta's Llama3 model through a fun interview-style chat.
**How to Use:**
1. **Input:** Type a topic into the input box labeled "Enter a topic".
2. **Submit:** Press the "Submit" button to start the simulation.
3. **Chat History:** View the previous conversations as the simulation unfolds.
**Credits:**
- **Developer:** Ali Inamdar
""")
# Text input
user_topic = st.text_input("Enter a topic", "Data Science")
# Add a button to submit
submit_button = st.button("Run Simulation!")
# Add a button to clear the session state
if st.button("Clear Session"):
st.session_state.messages = []
st.experimental_rerun()
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Create agents
interviewer = call_llama
interviewee = call_llama
judge = call_llama
# React to user input
iter = 0
list_of_iters = []
list_of_questions = []
list_of_answers = []
list_of_judge_comments = []
list_of_passes = []
if submit_button:
# Initiatization
prompt = f"Ask a question about this topic: {user_topic}"
# Display user message in chat message container
# Default: user=interviewee, assistant=interviewer
st.chat_message("user").markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
while True:
# Interview asks a question
question = interviewer(prompt)
# Display assistant response in chat message container
st.chat_message("assistant").markdown(question)
st.session_state.messages.append({"role": "assistant", "content": question})
# Interviewee attempts an answer
if iter < 5:
answer = interviewee(
f"""
Answer the question: {question} in a mediocre way
Because you are an inexperienced interviewee.
"""
)
st.chat_message("user").markdown(answer)
st.session_state.messages.append({"role": "user", "content": answer})
else:
answer = interviewee(
f"""
Answer the question: {question} in a mediocre way
Because you are an inexperienced interviewee but you really want to learn,
so you learn from the judge comments: {judge_comments}
"""
)
st.chat_message("user").markdown(answer)
st.session_state.messages.append({"role": "user", "content": answer})
# Judge thinks and advises but the thoughts are hidden
judge_comments = judge(
f"""
The question is: {question}
The answer is: {answer}
Provide feedback and rate the answer from 1 to 10 while 10 being the best and 1 is the worst.
"""
)
# Collect all responses
passed_or_not = 1 if '8' in judge_comments else 0
list_of_iters.append(iter)
list_of_questions.append(question)
list_of_answers.append(answer)
list_of_judge_comments.append(judge_comments)
list_of_passes.append(passed_or_not)
results_tab = pd.DataFrame({
"Iter.": list_of_iters,
"Questions": list_of_questions,
"Answers": list_of_answers,
"Judge Comments": list_of_judge_comments,
"Passed": list_of_passes
})
with st.expander("See explanation"):
st.table(results_tab)
# Stopping rule
if '8' in judge_comments:
break
# Checkpoint
iter += 1 |