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import streamlit as st
import re
import os
# Sidebar Style with Multicolored Background
sidebar_bg_style = """
<style>
[data-testid="stSidebar"] {
background: linear-gradient(135deg, #ffafbd, #ffc3a0, #2193b0, #6dd5ed);
}
</style>
"""
st.markdown(sidebar_bg_style, unsafe_allow_html=True)
# Main Content Style with Multicolored Background
main_bg_style = """
<style>
.main .block-container {
background: linear-gradient(135deg, #ff9a9e, #fad0c4, #fbc2eb, #a18cd1);
padding: 2rem;
}
.css-18e3th9 {
background: linear-gradient(135deg, #ff9a9e, #fad0c4, #fbc2eb, #a18cd1);
}
</style>
"""
st.markdown(main_bg_style, unsafe_allow_html=True)
# Sidebar: Input for URL and API keys
st.sidebar.title("Settings")
# Input field for entering URL dynamically with placeholder and help text
url_input = st.sidebar.text_input("Enter Blog Post URL", placeholder="e.g., https://example.com/blog", help="Paste the full URL of the blog post you want to retrieve data from")
# Validate the URL and show a success message when correct
if url_input:
if re.match(r"https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+", url_input):
st.sidebar.markdown('<p style="color:green; font-weight:bold;">URL is correctly entered</p>', unsafe_allow_html=True)
else:
st.sidebar.markdown('<p style="color:red; font-weight:bold;">Invalid URL, please enter a valid one</p>', unsafe_allow_html=True)
# Option to use pre-provided API keys
use_preprovided_keys = st.sidebar.checkbox("Use pre-provided API keys")
# Input fields for API keys with placeholders and helper text
if not use_preprovided_keys:
api_key_1 = st.sidebar.text_input("Enter LangChain API Key", type="password", placeholder="Enter your LangChain API Key", help="Please enter a valid LangChain API key here")
api_key_2 = st.sidebar.text_input("Enter Groq API Key", type="password", placeholder="Enter your Groq API Key", help="Please enter your Groq API key here")
else:
api_key_1 = "your-preprovided-langchain-api-key" # Replace with your actual pre-provided key
api_key_2 = "your-preprovided-groq-api-key" # Replace with your actual pre-provided key
st.sidebar.markdown('<p style="color:blue; font-weight:bold;">Using pre-provided API keys</p>', unsafe_allow_html=True)
# Submit button for API keys with a success/warning message
if st.sidebar.button("Submit API Keys"):
if use_preprovided_keys or (api_key_1 and api_key_2):
os.environ["LANGCHAIN_API_KEY"] = api_key_1
os.environ["GROQ_API_KEY"] = api_key_2
st.sidebar.markdown('<p style="color:green; font-weight:bold;">API keys are set</p>', unsafe_allow_html=True)
else:
st.sidebar.markdown('<p style="color:red; font-weight:bold;">Please fill in both API keys or select the option to use pre-provided keys</p>', unsafe_allow_html=True)
# Title of the chatbot
st.markdown('<h1 style="color:#4CAF50; font-weight:bold;">🤖 Chatbot with URL-based Document Retrieval</h1>', unsafe_allow_html=True)
# Chat query input field with placeholder and help text
query = st.text_input("Ask a question based on the blog post", placeholder="Type your question here...", help="Enter a question related to the content of the blog post")
# Placeholder to display responses
if 'chat_history' not in st.session_state:
st.session_state['chat_history'] = []
# Submit button for chat
if st.button("Submit Query"):
if query and url_input:
# Blog loading logic based on user input URL
loader = WebBaseLoader(
web_paths=(url_input,), # Use the user-input URL
bs_kwargs=dict(
parse_only=bs4.SoupStrainer() # Adjust based on the user's URL structure
),
)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
# Define the embedding class
class SentenceTransformerEmbedding:
def __init__(self, model_name):
self.model = SentenceTransformer(model_name)
def embed_documents(self, texts):
embeddings = self.model.encode(texts, convert_to_tensor=True)
if isinstance(embeddings, torch.Tensor):
return embeddings.cpu().detach().numpy().tolist() # Convert tensor to list
return embeddings
def embed_query(self, query):
embedding = self.model.encode([query], convert_to_tensor=True)
if isinstance(embedding, torch.Tensor):
return embedding.cpu().detach().numpy().tolist()[0] # Convert tensor to list
return embedding[0]
# Initialize the embedding model
embedding_model = SentenceTransformerEmbedding('all-MiniLM-L6-v2')
# Initialize Chroma with the embedding class
vectorstore = Chroma.from_documents(documents=splits, embedding=embedding_model)
# Retrieve and generate using the relevant snippets of the blog
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| ChatGroq(model="llama3-8b-8192") # Replace `llm` with an appropriate language model
| StrOutputParser()
)
# Generate the answer using the user's query
result = rag_chain.invoke(query)
# Store query and response in session for chat history
st.session_state['chat_history'].append((query, result))
# Display chat history
for q, r in st.session_state['chat_history']:
st.write(f"**User:** {q}")
st.write(f"**Bot:** {r}") |