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import streamlit as st
import langchain
import pandas as pd
import numpy as np
import os
import re
from langchain.chat_models import ChatOpenAI
import openai
from langchain import HuggingFaceHub, LLMChain, PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationalRetrievalChain
trait_content_df=pd.read_csv('AI Personality Chart trait_content.csv')
trait_content_df=trait_content_df.drop(0,axis=0)
trait_content_df.rename(columns={'Column 1':'Question','Column 2':'Options','Column 3':'Traits','Column 4':'Content'},inplace=True)
trait_content_df['Title'].fillna(method='ffill',inplace=True)
trait_content_df['Question'].fillna(method='ffill',inplace=True)
template = """
Generate a unique dating app fun bio for the user based upon detected personality traits. Craft distinctive responses for each user based on traits and refer from the content provided, even if the prompts are similar. Avoid using the same sentences for different users while keeping the response within 100 words.
{history}
Me:{human_input}
Jack:
"""
prompt = PromptTemplate(
input_variables=["history", "human_input"],
template=template
)
llm_chain = LLMChain(
llm = ChatOpenAI(temperature=1.0,model_name='gpt-3.5-turbo'),
prompt=prompt,
verbose=True,
# memory=ConversationBufferWindowMemory(k=2)
)
def extract_text_from_html(html):
cleanr = re.compile('<.*?>')
cleantext = re.sub(cleanr, '', html)
return cleantext.strip()
def conversational_chat(query, replacement_word=None):
hist_dict['past'].append(query)
output = llm_chain.predict(human_input=query)
hist_dict['generated'].append(output)
if replacement_word is not None:
# Use a regular expression with the re module for case-insensitive replacement
output = re.sub(r'\bjack\b', replacement_word, output, flags=re.IGNORECASE)
return extract_text_from_html(output)
hist_dict={}
hist_dict['generated']=["Hello ! Ask me anything about " + " 🤗"]
hist_dict['past'] = ["Hey ! 👋"]
trait_content_df_org=pd.read_csv('AI Personality Chart trait_content.csv')
trait_content_df_org=trait_content_df_org.drop(0,axis=0)
trait_content_df_org.rename(columns={'Column 1':'Question','Column 2':'Options','Column 3':'Traits','Column 4':'Content'},inplace=True)
def ui():
# Initialize a dictionary to store responses
responses = {}
# Create checkboxes for each question and options
index = 0
while index < len(trait_content_df_org):
question = trait_content_df_org.iloc[index]["Question"]
st.write(question)
option_a = st.checkbox(f"Option A: {trait_content_df_org.iloc[index]['Options']}", key=f"option_a_{index}")
# Check if Option B has a corresponding question (not None)
if trait_content_df_org.iloc[index + 1]["Question"] is not None:
option_b = st.checkbox(f"Option B: {trait_content_df_org.iloc[index + 1]['Options']}", key=f"option_b_{index + 1}")
else:
option_b = False
st.write("") # Add some spacing between questions
# Store responses in the dictionary
if option_a:
responses[question] = f"{trait_content_df_org.iloc[index]['Options']}"
if option_b:
responses[question] = f"{trait_content_df_org.iloc[index + 1]['Options']}"
index += 2 # Move to the next question and options (skipping None)
st.write("Responses:")
for question, selected_option in responses.items():
st.write(question)
st.write(selected_option)
# Generate a prompt based on selected options
selected_traits = [responses[question] for question in responses]
options_list = []
traits_list = []
content_list = []
for trait_str in selected_traits:
matching_rows = trait_content_df_org[trait_content_df_org["Options"] == trait_str]
if not matching_rows.empty:
options_list.append(matching_rows["Options"].values[0])
traits_list.append(matching_rows["Traits"].values[0])
content_list.append(matching_rows["Content"].values[0])
prompt = f"The following are Traits {', '.join(traits_list)}, and the content for the options is {', '.join(content_list)}"
# Display user input field
name_input = st.text_input("Enter your name:")
# Add a submit button
if st.button("Submit"):
# Generate a chatbot response
bio = conversational_chat(prompt, name_input)
st.write(bio)
if __name__=='__main__':
ui()
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