Spaces:
Running
Running
File size: 7,317 Bytes
b296661 09fe857 b296661 fa714bc b296661 fa714bc b296661 16f0715 b296661 16f0715 b296661 09fe857 b296661 09fe857 b296661 09fe857 b296661 fa714bc b296661 16f0715 b296661 fa714bc b296661 fa714bc b296661 8274e73 b296661 fa714bc b296661 8274e73 b296661 2329708 b296661 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
import streamlit as st
import os
from typing import List, Tuple, Optional
from pinecone import Pinecone
from langchain_pinecone import PineconeVectorStore
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
from dotenv import load_dotenv
from RAG import RAG
import logging
from image_scraper import DigitalCommonwealthScraper
import shutil
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Page configuration
st.set_page_config(
page_title="Boston Public Library Chatbot",
page_icon="🤖",
layout="wide"
)
def initialize_models() -> Tuple[Optional[ChatOpenAI], HuggingFaceEmbeddings]:
"""Initialize the language model and embeddings."""
try:
load_dotenv()
if "llm" not in st.session_state:
# Initialize OpenAI model
st.session_state.llm = ChatOpenAI(
model="gpt-4", # Changed from gpt-4o-mini which appears to be a typo
temperature=0,
timeout=60, # Added reasonable timeout
max_retries=2
)
if "embeddings" not in st.session_state:
# Initialize embeddings
st.session_state.embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
if "pinecone" not in st.session_state:
pinecone_api_key = os.getenv("PINECONE_API_KEY")
INDEX_NAME = 'bpl-rag'
#initialize vectorstore
pc = Pinecone(api_key=pinecone_api_key)
index = pc.Index(INDEX_NAME)
st.session_state.pinecone = PineconeVectorStore(index=index, embedding=st.session_state.embeddings)
except Exception as e:
logger.error(f"Error initializing models: {str(e)}")
st.error(f"Failed to initialize models: {str(e)}")
return None, None
def process_message(
query: str,
llm: ChatOpenAI,
vectorstore: PineconeVectorStore,
) -> Tuple[str, List]:
"""Process the user message using the RAG system."""
try:
response, sources = RAG(
query=query,
llm=llm,
vectorstore=vectorstore,
)
return response, sources
except Exception as e:
logger.error(f"Error in process_message: {str(e)}")
return f"Error processing message: {str(e)}", []
def display_sources(sources: List) -> None:
"""Display sources in expandable sections with proper formatting."""
if not sources:
st.info("No sources available for this response.")
return
st.subheader("Sources")
for i, doc in enumerate(sources, 1):
try:
with st.expander(f"Source {i}"):
if hasattr(doc, 'page_content'):
st.markdown(f"**Content:** {doc.page_content[0:100] + ' ...'}")
if hasattr(doc, 'metadata'):
for key, value in doc.metadata.items():
st.markdown(f"**{key.title()}:** {value}")
# Web Scraper to display images of sources
# Especially helpful if the sources are images themselves
# or are OCR'd text files
scraper = DigitalCommonwealthScraper()
images = scraper.extract_images(doc.metadata["URL"])
images = images[:1]
# If there are no images then don't display them
if not images:
st.warning("No images found on the page.")
return
# Download the images
# Delete the directory if it already exists
# to clear the existing cache of images for each listed source
output_dir = 'downloaded_images'
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
# Download the main image to a local directory
downloaded_files = scraper.download_images(images)
# Display the image using st.image
# Display the title of the image using img.get
st.image(downloaded_files, width=400, caption=[
img.get('alt', f'Image {i+1}') for i, img in enumerate(images)
])
else:
st.markdown(f"**Content:** {str(doc)}")
except Exception as e:
logger.error(f"Error displaying source {i}: {str(e)}")
st.error(f"Error displaying source {i}")
def main():
st.title("Digital Commonwealth RAG")
INDEX_NAME = 'bpl-rag'
# Initialize session state
if "messages" not in st.session_state:
st.session_state.messages = []
# Initialize models
initialize_models()
# Display chat history
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Chat input
user_input = st.chat_input("Type your query here...")
if user_input:
# Display user message
with st.chat_message("user"):
st.markdown(user_input)
st.session_state.messages.append({"role": "user", "content": user_input})
# Process and display assistant response
with st.chat_message("assistant"):
with st.spinner("Thinking... Please be patient, I'm a little slow right now..."):
response, sources = process_message(
query=user_input,
llm=st.session_state.llm,
vectorstore=st.session_state.pinecone
)
if isinstance(response, str):
st.markdown(response)
st.session_state.messages.append({
"role": "assistant",
"content": response
})
# Display sources
display_sources(sources)
else:
st.error("Received an invalid response format")
# Footer
st.markdown("---")
st.markdown(
"Built with Langchain + Streamlit + Pinecone",
help="Natural Language Querying for Digital Commonwealth"
)
st.markdown("The Digital Commonwealth site provides access to photographs, manuscripts, books, audio recordings, and other materials of historical interest that have been\ndigitized and made available by members of Digital Commonwealth, a statewide consortium of libraries, museums, archives, and historical societies from across Massachusetts.")
if __name__ == "__main__":
main() |