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import streamlit as st
import random
import time
from openai import OpenAI
import pandas as pd
import elemeta.nlp.runners.metafeature_extractors_runner as metafeature_extractors_runner
from elemeta.nlp.runners.metafeature_extractors_runner import MetafeatureExtractorsRunner
from elemeta.nlp.extractors.high_level.text_length import TextLength
from elemeta.nlp.extractors.high_level.text_complexity import TextComplexity
from elemeta.nlp.extractors.high_level.word_count import WordCount
from elemeta.nlp.extractors.high_level.detect_language_langdetect import DetectLanguage
from elemeta.nlp.extractors.high_level.sentiment_polarity import SentimentPolarity
from elemeta.nlp.extractors.high_level.toxicity_extractor import ToxicityExtractor
runner = MetafeatureExtractorsRunner(metafeature_extractors=[TextLength(),WordCount(),DetectLanguage()
,SentimentPolarity(),TextComplexity(),ToxicityExtractor()])
def ask_gpt(messages,model="gpt-3.5-turbo"):
ret = client.chat.completions.create(model=model,
messages=messages
)
return ret.choices[0].message.content
client = OpenAI()
st.title("GPT Chatbot")
system_prompt = st.text_input("Enter your system's prompt",value="Translate the following into russian")
user_prompt = st.text_input("Enter your user's prompt",value="Hello, how are you?")
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
]
output = ask_gpt(messages)
st.header("Output")
st.write(output)
st.header("Metafeatures")
df = pd.DataFrame([
runner.run(system_prompt),
runner.run(user_prompt),
runner.run(output)])
df["prompt"] = ["system","user","output"]
df = df.set_index("prompt")
st.dataframe(df)
st.header("Chat Monitoring")
st.subheader("Chat")
# 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"])
# Accept user input
if prompt := st.chat_input("What is up?"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
assistant_response = ask_gpt(messages=st.session_state.messages)
# Simulate stream of response with milliseconds delay
for chunk in assistant_response.split():
full_response += chunk + " "
time.sleep(0.05)
# Add a blinking cursor to simulate typing
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": full_response})
st.subheader("Chat Logs")
user_messages = [message["content"] for message in st.session_state.messages if message["role"] == "user"]
assistant_messages = [message["content"] for message in st.session_state.messages if message["role"] == "assistant"]
# st.write("User Messages",user_messages)
# st.write("Assistant Messages",assistant_messages)
user_df = pd.DataFrame([runner.run(user_prompt) for user_prompt in user_messages])
user_df["prompt"] = user_messages
user_df.columns = 'user_' + user_df.columns.values
# st.dataframe(user_df)
assistant_df = pd.DataFrame([runner.run(assistant_prompt) for assistant_prompt in assistant_messages])
assistant_df["prompt"] = assistant_messages
assistant_df.columns = 'assistant_' + assistant_df.columns.values
# st.dataframe(assistant_df)
st.subheader("Logs Metafeatures")
st.dataframe(pd.concat([user_df,assistant_df],axis=1)) |