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app.py
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app.py
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import streamlit as st # type: ignore
import pandas as pd
import numpy as np
import random
from matplotlib import pyplot as plt
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
dataset = "./data/covid_qA.csv"
data = pd.read_csv(dataset)
vectorizer = TfidfVectorizer()
count_vec = vectorizer.fit_transform(data['Question']).toarray()
def COVIDbot(user_response):
text = vectorizer.transform([user_response]).toarray()
data['similarity'] = cosine_similarity(count_vec, text)
return data.sort_values(['similarity'], ascending=False).iloc[0]['Answer']
welcome_input = ("hello", "hi", "greetings", "sup", "what's up","hey",)
welcome_response = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad! You are talking to me"]
def welcome(user_response):
for word in user_response.split():
if word.lower() in welcome_input:
return random.choice(welcome_response)
def generate_response(user_response):
user_response = user_response.lower()
if(user_response not in ['bye','shutdown','exit', 'quit']):
if(user_response=='thanks' or user_response=='thank you'):
flag=False
return "You are welcome.."
else:
if(welcome(user_response)!=None):
return welcome(user_response)
else:
return COVIDbot(user_response)
# title
st.header("AskCovidDr : Retrieval Based Chatbot")
st.markdown(
"""
This is a simple retrieval based chatbot. It utilizes TF-IDF Vectorizer to find & return sentence most similar to user prompt.
"""
)
# get input text
with st.form('input'):
input_text = st.text_area("Question here")
# return response
submit_button = st.form_submit_button(label="Ask Bot")
if submit_button:
response = generate_response(input_text)
st.success(f"{response}")