app.py

import os
import streamlit as st
from datasets import load_dataset
import chromadb
import string

from openai import OpenAI

import numpy as np
import pandas as pd

from scipy.spatial.distance import cosine

from typing import Dict, List

def merge_dataframes(dataframes):
    # Concatenate the list of dataframes
    combined_dataframe = pd.concat(dataframes, ignore_index=True)

    # Ensure that the resulting dataframe only contains the columns "context", "questions", "answers"
    combined_dataframe = combined_dataframe[['context', 'questions', 'answers']]

    return combined_dataframe

def call_chatgpt(prompt: str) -> str:
    """
    Uses the OpenAI API to generate an AI response to a prompt.
    Args:
        prompt: A string representing the prompt to send to the OpenAI API.
    Returns:
        A string representing the AI's generated response.
    """

    # Use the OpenAI API to generate a response based on the input prompt.
    client = OpenAI(api_key = os.environ["OPENAI_API_KEY"])

    completion = client.chat.completions.create(
    model="gpt-3.5-turbo-0125",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ]
    )

    # Extract the text from the first (and only) choice in the response output.
    ans = completion.choices[0].message.content

    # Return the generated AI response.
    return ans

def openai_text_embedding(prompt: str) -> str:
    return openai.Embedding.create(input=prompt, model="text-embedding-ada-002")[
        "data"
    ][0]["embedding"]

def calculate_sts_openai_score(sentence1: str, sentence2: str) -> float:
    # Compute sentence embeddings
    embedding1 = openai_text_embedding(sentence1)  # Flatten the embedding array
    embedding2 = openai_text_embedding(sentence2)  # Flatten the embedding array

    # Convert to array
    embedding1 = np.asarray(embedding1)
    embedding2 = np.asarray(embedding2)

    # Calculate cosine similarity between the embeddings
    similarity_score = 1 - cosine(embedding1, embedding2)

    return similarity_score

def add_dist_score_column(
    dataframe: pd.DataFrame, sentence: str,
) -> pd.DataFrame:
    dataframe["stsopenai"] = dataframe["questions"].apply(
            lambda x: calculate_sts_openai_score(str(x), sentence)
    )
    
    sorted_dataframe = dataframe.sort_values(by="stsopenai", ascending=False)


    return sorted_dataframe.iloc[:5, :]

def convert_to_list_of_dict(df: pd.DataFrame) -> List[Dict[str, str]]:
    """
    Reads in a pandas DataFrame and produces a list of dictionaries with two keys each, 'question' and 'answer.'
    Args:
        df: A pandas DataFrame with columns named 'questions' and 'answers'.
    Returns:
        A list of dictionaries, with each dictionary containing a 'question' and 'answer' key-value pair.
    """

    # Initialize an empty list to store the dictionaries
    result = []

    # Loop through each row of the DataFrame
    for index, row in df.iterrows():
        # Create a dictionary with the current question and answer
        qa_dict_quest = {"role": "user", "content": row["questions"]}
        qa_dict_ans = {"role": "assistant", "content": row["answers"]}

        # Add the dictionary to the result list
        result.append(qa_dict_quest)
        result.append(qa_dict_ans)

    # Return the list of dictionaries
    return result

st.sidebar.markdown("""This is a chatbot to help you learn more about Youth Spirit Artworks!""")

domain = st.sidebar.selectbox("What do you want to learn about?", ("About YSA", "Our Team and Youth Leaders", "Tiny House Village", "Qualify/Apply for Village", "YSA Supporters"))

special_threshold = 0.3

n_results = 3

clear_button = st.sidebar.button("Clear Conversation", key="clear")

if clear_button:
    st.session_state.messages = []

# Load the dataset from a provided source.
if domain == "About YSA":
    dataset = load_dataset(
        "KeshavRa/About_YSA_Database"
    )
elif domain == "Our Team and Youth Leaders":
    dataset = load_dataset(
        "KeshavRa/Our_Team_Youth_Leaders_Database"
    )
elif domain == "Tiny House Village":
    dataset = load_dataset(
        "KeshavRa/Tiny_House_Village_Database"
    )
elif domain == "Qualify/Apply for Village":
    dataset = load_dataset(
        "KeshavRa/Qualify_Apply_For_Village_Database"
    )
elif domain == "YSA Supporters":
    dataset = load_dataset(
        "KeshavRa/YSA_Supporters_Database"
    )

initial_input = "Tell me about YSA"

# Initialize a new client for ChromeDB.
client = chromadb.Client()

# Generate a random number between 1 billion and 10 billion.
random_number: int = np.random.randint(low=1e9, high=1e10)

# Generate a random string consisting of 10 uppercase letters and digits.
random_string: str = "".join(
    np.random.choice(list(string.ascii_uppercase + string.digits), size=10)
)

# Combine the random number and random string into one identifier.
combined_string: str = f"{random_number}{random_string}"

# Create a new collection in ChromeDB with the combined string as its name.
collection = client.create_collection(combined_string)

st.title("Youth Spirit Artworks Chatbot")

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

if "curr_database" not in st.session_state:
    st.session_state.curr_database = None

init_messages = {
    "About YSA": '''
    On this page, you can learn about what YSA does, how YSA was started, the advisory board, and the programs we offer.

    Examples

    --> What is the purpose of Youth Spirit Artworks?

    --> Who created YSA?

    --> What is the Advisory Board for Youth Spirit Artworks?

    --> What are the three empowerment-focused program areas of YSA?
    ''',
    
    "Our Team and Youth Leaders": ''' b ''',
    
    "Tiny House Village": ''' c ''',
    
    "Qualify/Apply for Village": ''' d ''',
    
    "YSA Supporters": ''' e ''',
}

# Embed and store the first N supports for this demo
with st.spinner("Loading, please be patient with us ... 🙏"):
    L = len(dataset["train"]["questions"])
    collection.add(
        ids=[str(i) for i in range(0, L)],  # IDs are just strings
        documents=dataset["train"]["questions"],  # Enter questions here
        metadatas=[{"type": "support"} for _ in range(0, L)],
    )
    db=collection
    if st.session_state.curr_database != dataset:
        st.session_state.messages = []
        init_message = init_messages[domain]
        st.session_state.messages.append({"role": "assistant", "content": init_message})

# Current database variable
st.session_state.curr_database = dataset

# 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"])

# React to user input
if prompt := st.chat_input("Tell me about YSA"):
    # Display user message in chat message container
    st.chat_message("user").markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})

    question = prompt

    results = collection.query(query_texts=question, n_results=n_results)

    idx = results["ids"][0]
    idx = [int(i) for i in idx]
    ref = pd.DataFrame(
        {
            "idx": idx,
            "questions": [dataset["train"]["questions"][i] for i in idx],
            "answers": [dataset["train"]["answers"][i] for i in idx],
            "distances": results["distances"][0],
        }
    )
    # special_threshold = st.sidebar.slider('How old are you?', 0, 0.6, 0.1) # 0.3
    # special_threshold = 0.3
    filtered_ref = ref[ref["distances"] < special_threshold]
    if filtered_ref.shape[0] > 0:
        # st.success("There are highly relevant information in our database.")
        ref_from_db_search = filtered_ref["answers"].str.cat(sep=" ")
        final_ref = filtered_ref
    else:
        # st.warning(
        #     "The database may not have relevant information to help your question so please be aware of hallucinations."
        # )
        ref_from_db_search = ref["answers"].str.cat(sep=" ")
        final_ref = ref

    engineered_prompt = f"""
        Based on the context: {ref_from_db_search},
        answer the user question: {question}.
    """

    answer = call_chatgpt(engineered_prompt)

    response = answer
    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        st.markdown(response)
        with st.expander("See reference:"):
            st.table(final_ref) 
    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})