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import streamlit as st
from sentence_transformers import SentenceTransformer
from qdrant_client import models, QdrantClient
import pandas as pd
from datasets import load_dataset
#************************************************************* LOAD DATA
data = load_dataset("ManuelAlv/academic_conuseling")
# Main dataset
bert_dataset = data['dataset'].to_pandas()
# Dataset used to test the chatbot
test_dataset = data['test'].to_pandas()
test_dataset.columns = ["test_question", "original_question"]
#************************************************************* Create Functions
# function to add values
def add_value(collection, key, value, id):
encoder = SentenceTransformer(collection)
qdrant.upsert(
collection_name = collection,
wait=True,
points = [
models.PointStruct(
id = id,
vector = encoder.encode(key).tolist(),
payload = {
'text': value,
'question': key
}
)
]
)
# Function to search for a value
def search(collection, query):
search = qdrant.search(
collection_name = collection,
query_vector = encoder.encode(query).tolist(),
limit = 1
)
return search
#************************************************************* Create VD
# Create a local Vector Database
qdrant = QdrantClient(":memory:")
# Load the model
model = "all-MiniLM-L6-v2"
encoder = SentenceTransformer(model)
# Create a collection for the model with its embeddings
qdrant.recreate_collection(
collection_name = model,
vectors_config = models.VectorParams(
size = encoder.get_sentence_embedding_dimension(),
distance = models.Distance.COSINE
)
)
# Add the data to model
for index, row in bert_dataset.iterrows():
key = row['question']
value = row['answer']
id = index + 1
add_value(model, key, value, id)
# ************************************************************* QUERY
# Enter a question
question = "I'm feeling sad and lonely"
result = search(model, question)
result = result[0].payload['text']
def get_response(input):
result = search(model, input)
result = result[0].payload['text']
return result
st.set_page_config(page_title="RAG", page_icon="🧊", layout="wide")
st.title("UniSA Academic Support")
# with st.sidebar:
# st.header("Settings")
# st.text_input("Enter a website URL")
if 'conversation_ended' not in st.session_state:
st.session_state['conversation_ended'] = False
if not st.session_state['conversation_ended']:
with st.chat_message("AI"):
st.write("Hi! I'm BrainHug AI, your supportive AI friend.")
st.write("Feel free to chat with me at any time, just enter your question. If I can't answer, try rephrasing it again.")
st.write("If you want to finish the conversation, just say BYE")
user_q = st.chat_input("Start typing here")
if user_q:
if user_q.upper() == "BYE":
st.session_state['conversation_ended'] = True
with st.chat_message("AI"):
st.write("Goodbye! Feel free to come back anytime.")
st.stop()
elif user_q is not None or user_q is "":
response = get_response(user_q)
with st.chat_message("Human"):
st.write(user_q)
with st.chat_message("AI"):
st.write(response)
else:
st.write("The conversation has ended. Refresh the page to start over.") |