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import streamlit as st
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
# Function to load and preprocess the data
def load_data(file):
df = pd.read_csv(file, delimiter=";")
return df
# Function to process the input and get the most similar question
def get_most_similar_question(new_sentence, questions, answers, vectorizer, tfidf_matrix):
new_tfidf = vectorizer.transform([new_sentence])
similarities = cosine_similarity(new_tfidf, tfidf_matrix)
most_similar_index = np.argmax(similarities)
similarity_percentage = similarities[0, most_similar_index] * 100
return answers[most_similar_index], similarity_percentage
# Function to generate response
def answer_the_question(new_sentence, questions, answers, vectorizer, tfidf_matrix):
most_similar_answer, similarity_percentage = get_most_similar_question(new_sentence, questions, answers, vectorizer, tfidf_matrix)
if similarity_percentage > 70:
response = {
'answer': most_similar_answer
}
else:
response = {
'answer': 'Sorry, I am not aware of this information :('
}
return response
# Streamlit app
def main():
st.title("Q&A Chatbot")
st.write("Upload a CSV file with questions and answers.")
# Upload CSV file
uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
if uploaded_file is not None:
df = load_data(uploaded_file)
questions = df['question'].tolist()
answers = df['answer'].tolist()
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform(questions)
# Ask question
user_question = st.text_input("Ask your question here:")
if st.button("Ask"):
if user_question:
response = answer_the_question(user_question, questions, answers, vectorizer, tfidf_matrix)
st.write("Answer:", response['answer'])
if __name__ == "__main__":
main()
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