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import streamlit as st
from langchain_chroma import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_google_genai import GoogleGenerativeAI
from langchain_huggingface import HuggingFaceEmbeddings
# Configuration
GOOGLE_API_KEY = "AIzaSyB-7cKMdUpA5kTccpNxd72IT5CjeSgSmkc" # Replace with your API key
CHROMA_DB_DIR = "./chroma_db_" # Directory for ChromaDB
MODEL_NAME = "flax-sentence-embeddings/all_datasets_v4_MiniLM-L6"
# Initialize Embeddings and Models
embeddings_model = HuggingFaceEmbeddings(model_name=MODEL_NAME)
db = Chroma(collection_name="vector_database", embedding_function=embeddings_model, persist_directory=CHROMA_DB_DIR)
genai_model = GoogleGenerativeAI(api_key=GOOGLE_API_KEY, model="gemini-1.5-flash")
# Streamlit UI
st.set_page_config(
page_title="Pega CDH Intelligent Assistant",
page_icon="🤖",
layout="wide",
initial_sidebar_state="expanded"
)
# Sidebar Navigation
st.sidebar.image("https://www.pega.com/sites/all/themes/pega/images/logo.svg", use_column_width=True)
st.sidebar.title("Pega CDH Assistant 🤝")
st.sidebar.markdown("Navigate to the features you need:")
menu = st.sidebar.radio(
"Menu",
["Ask an Intelligent Question", "Explore Knowledge Hub", "Give Feedback"],
index=0
)
# Style Enhancements
st.markdown("""
<style>
.main {background-color: #f7f9fc; padding: 20px;}
h1, h2, h3, h4 {color: #005b96;}
.sidebar .sidebar-content {background-color: #e3f2fd;}
.stButton>button {background-color: #0288d1; color: white; border-radius: 8px;}
</style>
""", unsafe_allow_html=True)
# Main Interface
if menu == "Ask an Intelligent Question":
st.title("Pega CDH Intelligent Question Assistant")
st.subheader("Get precise answers powered by Pega Customer Decision Hub.")
st.image("https://via.placeholder.com/800x200?text=Empower+Your+Decisions+with+Pega+AI", use_column_width=True)
# Input Section
query = st.text_input(
"What's your question today?",
placeholder="Type your question to unlock insights from Pega's knowledge base..."
)
if query:
with st.spinner("Analyzing your question and fetching an answer..."):
docs_chroma = db.similarity_search_with_score(query, k=4)
context_text = "\n\n".join([doc.page_content for doc, _score in docs_chroma])
# Generate Answer
PROMPT_TEMPLATE = """
Answer the question based only on the following context:
{context}
Answer the question based on the above context: {question}.
"""
prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
prompt = prompt_template.format(context=context_text, question=query)
response_text = genai_model.invoke(prompt)
# Chat-Like Output
st.subheader("Your Conversation")
st.markdown(f"**You:** {query}")
st.markdown(f"**Pega Assistant:** {response_text}")
elif menu == "Explore Knowledge Hub":
st.title("Pega Knowledge Hub 📚")
st.subheader("Search through the rich repository of Pega's insights.")
st.write("Use the filters below to explore relevant topics.")
# Add advanced filters and browsing options for knowledge hub
elif menu == "Give Feedback":
st.title("Your Feedback Matters 💡")
st.subheader("Help us improve by sharing your experience.")
feedback = st.text_area(
"Share your suggestions or comments below:",
placeholder="Write your feedback here..."
)
if st.button("Submit Feedback"):
st.success("Thank you for your feedback! 🙏")
# Footer
st.sidebar.markdown("---")
st.sidebar.info("**Pega Customer Decision Hub Assistant v1.0** | Powered by OpenAI and Pega Technologies.")
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