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import streamlit as st
from sentence_transformers import SentenceTransformer
import faiss
import pandas as pd
import numpy as np
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
df = pd.read_csv('course_data.csv',index_col=0)
courses = df.to_dict('records')
descriptions = [course['Content'] for course in courses]
embeddings = model.encode(descriptions)
index = faiss.IndexFlatL2(embeddings.shape[1]) # L2 distance index
index.add(np.array(embeddings))
# Function to simulate chatbot response (replace with your AI model logic)
def generate_response(query,k=5):
# print(query)
# Placeholder response logic (you can replace this with your model/API call)
query_embedding = model.encode([query[-1]]) # Encode the user query
# Search in FAISS index for the closest matches
D, I = index.search(np.array(query_embedding), k=k) # k is the number of top results
# Retrieve course titles based on the search results
results = []
desc = []
for idx in I[0]:
course_title = courses[idx]['Course_Name'] # Get the course title
desc.append(courses[idx]['Content'])
results.append(course_title)
# output=''
# for i,j in enumerate(list(set(results))):
# output+=str(i+1)+j+'\n'
return list(set(results))
# Define session state variables
if 'messages' not in st.session_state:
st.session_state.messages = []
if 'mess' not in st.session_state:
st.session_state.mess=[]
if st.sidebar.button("RESET"):
st.session_state.messages=[]
st.session_state.mess=[]
# User input
st.title('Analytics Vidhya Course Finder')
user_input = st.chat_input('Write your message here...')
if user_input:
# Append user input to messages
st.session_state.messages.append({"role": "user", "content": user_input})
st.session_state.mess+=[user_input]
# Generate chatbot response
bot_response = generate_response(st.session_state.mess)
st.session_state.messages.append({"role": "bot", "content": bot_response})
# Display chat messages in correct order
for message in st.session_state.messages:
if message["role"] == "user":
with st.chat_message("human"):
st.write(message['content'])
else:
with st.chat_message("ai"):
for i in message['content']:
st.write('* '+i)
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