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# app.py
import streamlit as st
from function import Head_Agent
def init_chatbot():
with open('open_ai_key.txt', 'r', encoding='utf-8') as file:
openai_key = file.readline().strip()
with open('pinecone_api.txt', 'r', encoding='utf-8') as file:
pinecone_key = file.readline().strip()
pinecone_index_name = 'machine-learning-index'
return Head_Agent(openai_key, pinecone_key, pinecone_index_name)
# ------------- Streamlit -------------------
st.title("My Streamlit Chatbot with Greetings")
if "chatbot" not in st.session_state:
st.session_state["chatbot"] = init_chatbot()
chatbot = st.session_state["chatbot"]
user_query = st.text_input("Please enter your question:")
greeting_keywords = {
"hi", "hello", "hey", "how are you", "how r u", "yo", "good morning", "good evening", "good afternoon"
}
if st.button("Sent"):
if not user_query.strip():
st.warning("Please enter valid content.")
else:
# ---
normalized_input = user_query.lower().strip()
if normalized_input in greeting_keywords:
greet_response = "Hello there! How can I assist you today?"
st.write("Robot: ", greet_response)
chatbot.conv_history.append(f"User: {user_query}")
chatbot.conv_history.append(f"Robot: {greet_response}")
else:
if chatbot.obnoxious_agent.check_query(user_query):
st.write("Robot: Your question is inappropriate, please try another one.")
else:
docs = chatbot.query_agent.query_vector_store(user_query)
matches = docs["matches"]
response = chatbot.answering_agent.generate_response(
user_query,
matches,
chatbot.conv_history
)
chatbot.conv_history.append(f"User: {user_query}")
chatbot.conv_history.append(f"Robot: {response}")
# 3.
conversation_context = (
f"User query: {user_query}\n"
f"Retrieve document summaries: {response}"
)
relevance = chatbot.relevant_agent.get_relevance(conversation_context)
if relevance.strip().lower() == "no":
st.write("【Robot: generated answer, but not sure if it's relevant:】", response)
else:
st.write("Robot:", response)
st.write("---")
st.subheader("Conversation History")
for msg in chatbot.conv_history:
st.write(msg)
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