Create app.py

app.py

@@ -0,0 +1,162 @@
+import os
+import streamlit as st
+import asyncio
+
+from langchain_community.utilities import SQLDatabase
+from typing_extensions import TypedDict, List
+from IPython.display import Image, display
+from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain.schema import Document
+from langgraph.graph import START, END, StateGraph
+from langchain.prompts import PromptTemplate, ChatPromptTemplate
+from sqlalchemy import create_engine
+import uuid
+from langchain_groq import ChatGroq
+from langchain_community.utilities import GoogleSerperAPIWrapper
+from langchain_chroma import Chroma
+from langchain_community.document_loaders import NewsURLLoader
+from langchain_community.retrievers.wikipedia import WikipediaRetriever
+
+from langchain.vectorstores import Chroma
+from langchain_community.document_loaders import UnstructuredURLLoader, NewsURLLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import WebBaseLoader 
+from langchain_core.output_parsers import JsonOutputParser
+from langchain_community.vectorstores.utils import filter_complex_metadata
+from langchain.schema import Document
+from langgraph.graph import START, END, StateGraph
+from langchain_community.document_loaders.directory import DirectoryLoader 
+
+from functions import *
+import pprint
+from pathlib import Path
+from sqlalchemy import create_engine
+from langchain.retrievers import ContextualCompressionRetriever
+
+from langchain.storage import InMemoryByteStore
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.retrievers.document_compressors import CrossEncoderReranker
+from langchain_community.cross_encoders import HuggingFaceCrossEncoder 
+
+import re
+from functions import *
+
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.retrievers.document_compressors import FlashrankRerank
+
+
+os.environ["LANGCHAIN_API_KEY"] = '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'
+os.environ["LANGCHAIN_TRACING_V2"]="true"
+os.environ["LANGCHAIN_ENDPOINT"]= "https://api.smith.langchain.com"
+os.environ["LANGCHAIN_PROJECT"] = "Lithuanian_law_v2_LT_Kalba"
+os.environ["GROQ_API_KEY"] = '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' 
+os.environ["SERPER_API_KEY"] = '6f80701ecd004c2466e8bd7bcebacacf89c74b84'  
+
+
+
+
+
+
+def main():
+
+    st.set_page_config(page_title="Birutė, Teisės Asistentė: ",
+                       page_icon=":books:")
+    
+
+    st.header("Birutė, Lietuviškos Civilinės teisės asistentė :" ":books:")
+
+    db = None
+    custom_graph = None
+
+    search_type = st.selectbox(
+            " ##### Pasirinkite paieškos būdą. Pasirinkimai yra: [Max marginal relevance search (MMR), Similarity search (similarity). Default value (similarity)], MMR gražina dokumentus iš platesnio spectro infromacijos, similarity is mažesnio, konkrečiau susijusio su užklausa", 
+            options=["mmr", "similarity"], 
+            index=0  
+        )
+
+    k = st.select_slider(
+            " ###Pasirinkite kieki dokumentų, kuriuos norėtumete naudoti generuojant atsakymą. Per daug dokumentų, kartais gali prikimšti asistentės atminti nereikalinga informacija ir dėl to gali atsirasti haliucinacijos. Parinktasis dokumentų kiekis: (4): ", 
+            options=list(range(2, 9)), 
+            value=3  
+        )
+
+    full_retriever = create_retriever_from_chroma(vectorstore_path="docs/chroma/", search_type=search_type, k=k, chunk_size=9500, chunk_overlap=0)
+    
+
+    
+
+    #compression_full_retriever = ContextualCompressionRetriever(
+     #       base_compressor=compressor, base_retriever=full_retriever
+      #  )
+   
+
+    # Tabs for Chat and Document Search
+    tab1, tab2 = st.tabs(["Teisės Asistentė", "Informacijos Paieška Dokumentuose"])
+
+    if "active_tab" not in st.session_state:
+        st.session_state["active_tab"] = "Teisės Asistentė "
+
+    with tab1:
+        
+        st.markdown("""
+            ###### Prisiminkite: Aš esu virtuali asistentė, ne profesionali teisininkė. Naudokite savo kritinį mąstymą priimdami sprendimus arba pasitarkite su teisininku.
+        """)
+        
+        if "messages" not in st.session_state:
+            st.session_state["messages"] = [
+            {"role": "assistant", "content": "Labas, aš virtuali teisės asistentė Birutė. Kuo galėčiau jums padėti?"}
+        ]
+
+        
+        
+        
+       
+
+
+
+        custom_graph = create_workflow(full_retriever)
+
+        user_question = st.text_input("Klauskite klausimus susijusius su civiline teise.:")
+        klausti_btn = st.button("Klausti")
+        if klausti_btn:
+            with st.spinner("Galvojama"):
+                try:
+                    handle_userinput(user_question, custom_graph)
+                except Exception as e:
+                    st.write(f"Error: {e}")
+
+    with tab2:
+        
+        st.session_state["active_tab"] = "Informacijos Paieška Dokumentuose"
+
+        if st.session_state["active_tab"] == "Informacijos Paieška Dokumentuose":
+
+       
+
+            
+            user_topic = st.text_input("Paieška dokumentuose pagal tekstinį atitikimą:")
+            ieskoti_btn = st.button("Ieškoti informacijos")
+            if ieskoti_btn:
+                
+                with st.sidebar:
+                    try:
+                        relevant_docs = full_retriever.get_relevant_documents(user_topic)
+                        pretty_output_parts = []
+                        for doc in relevant_docs:
+                            pretty_output_parts.append("\n\n**Documentų Meta duomenys**:\n")
+                            metadata_str = "Dokumento Pavadinimas: " + doc.metadata.get('original_doc_name', 'unknown')
+                            pretty_output_parts.append(metadata_str)
+                            pretty_output_parts.append("\n\n**Dokumentu skirsniai**:\n")
+                            content = doc.page_content.replace('\\n\\n\\n\\n', '\n\n').replace('\\n\\n', '\n\n')
+                            pretty_output_parts.append(content)
+
+                            pretty_output = "\n\n\n".join(pretty_output_parts)
+                            st.markdown(pretty_output)
+                    except Exception as e:
+                        st.write(f"Error: {e}")
+
+if __name__ == "__main__":
+    main()