Delete new_streamlit.py

new_streamlit.py

@@ -1,188 +0,0 @@
-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
-from bpl_scraper import DigitalCommonwealthScraper
-import logging
-import json
-import shutil
-from PIL import Image
-import io
-
-# 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()
-        
-        # Initialize OpenAI model
-        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
-        )
-        
-        # Initialize embeddings
-        embeddings = HuggingFaceEmbeddings(
-            model_name="sentence-transformers/all-MiniLM-L6-v2"
-        )
-        
-        return llm, 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,
-    index_name: str,
-    embeddings: HuggingFaceEmbeddings
-) -> Tuple[str, List]:
-    """Process the user message using the RAG system."""
-    try:
-        response, sources = RAG(
-            query=query,
-            llm=llm,
-            index_name=index_name,
-            embeddings=embeddings
-        )
-        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("Boston Public Library RAG Chatbot")
-    
-    # Initialize session state
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-    
-    # Initialize models
-    llm, embeddings = initialize_models()
-    if not llm or not embeddings:
-        st.error("Failed to initialize the application. Please check the logs.")
-        return
-    
-    # Constants
-    INDEX_NAME = 'bpl-rag'
-    
-    # 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 message 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..."):
-                response, sources = process_message(
-                    query=user_input,
-                    llm=llm,
-                    index_name=INDEX_NAME,
-                    embeddings=embeddings
-                )
-                
-                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 ❤️ using Streamlit + LangChain + OpenAI",
-        help="An AI-powered chatbot with RAG capabilities"
-    )
-
-if __name__ == "__main__":
-    main()