app.py

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 = "123")

    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 an app to help you navigate the websites of YSA/Larkin Street""")

org = st.sidebar.selectbox("Which website do you want to ask?", ("YSA", "Larkin"))

if org == "YSA":
    domain = st.sidebar.selectbox("What do you want to learn about?", ("About Us: Our Mission and Programs", "The Tiny House Empowerment Village", "How to Qualify/Apply to the Tiny House Village", "Our Team and Youth Leaders", "Our Supporters"))
if org == "Larkin":
    domain = st.sidebar.selectbox("What do you want to learn about?", ("Domain1", "Domain2"))

special_threshold = st.sidebar.number_input(
    "Insert a threshold for distances score to filter data (default 0.2):",
    value=0.2,
    placeholder="Type a number...",
)

n_results = st.sidebar.slider(
    "Insert n-results (default 5)",
    0, 10, 5
)

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 Us: Our Mission and Programs":
    dataset = load_dataset(
        "KeshavRa/About_YSA_Database"
    )
elif domain == "The Tiny House Empowerment Village":
    dataset = load_dataset(
        "KeshavRa/Tiny_House_Village_Database"
    )
elif domain == "How to Qualify/Apply for the Tiny House Village":
    dataset = load_dataset(
        "KeshavRa/Qualify_Apply_For_Village_Database"
    )
elif domain == "Our Team and Youth Leaders":
    dataset = load_dataset(
        "KeshavRa/Our_Team_Youth_Leaders_Database"
    )
elif domain == "Our Supporters":
    dataset = load_dataset(
        "KeshavRa/YSA_Supporters_Database"
    )
else:
    dataset = load_dataset(
        "eagle0504/youthless-homeless-shelter-web-scrape-dataset-qa-formatted"
    )
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)


# 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

st.title("Youth Homelessness Chatbot")

# 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"])

# 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})