Update app.py

app.py

@@ -1,26 +1,20 @@
-from langchain_community.document_loaders import PyPDFLoader,DirectoryLoader
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.embeddings.openai import OpenAIEmbeddings
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
 import os
+import streamlit as st
+import time
+from openai import OpenAI
 from langchain.prompts import PromptTemplate 
-from langchain_together import Together
 from langchain.chat_models import ChatOpenAI
-from langchain.embeddings import openai
 from langchain.memory import ConversationBufferWindowMemory
 from langchain.chains import ConversationalRetrievalChain
-import streamlit as st
-import time
+from langchain.embeddings import OpenAIEmbeddings
+from langchain_community.vectorstores import FAISS
 
-from openai import OpenAI
+# Set up OpenAI API key
 api_key = os.getenv("OPENAI_API_KEY")
 client = OpenAI(api_key=api_key)
 
-
-# creating custom template to guide llm model
-custom_template ="""<s>[INST]You will start the conversation by greeting the user and introducing yourself as qanoon-bot,
+# Custom template to guide the LLM model
+custom_template = """<s>[INST]You will start the conversation by greeting the user and introducing yourself as qanoon-bot,
 stating your availability for legal assistance. Your next step will depend on the user's response.
 If the user expresses a need for legal assistance in Pakistan, you will ask them to describe their case or problem.
 After receiving the case or problem details from the user, you will provide the solutions and procedures according to the knowledge base and also give related penal codes and procedures.
@@ -35,17 +29,15 @@ ANSWER:
 </s>[INST]
 """
 
-embeddings=OpenAIEmbeddings()
-#embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"})
-#vectordb = Chroma.from_documents(texts, embedding=embeddings, persist_directory="./data")
-#db_retriever =vectordb.as_retriever(search_type="similarity",search_kwargs={'k':4})
+embeddings = OpenAIEmbeddings()
 
+# Load vector database
 db = FAISS.load_local("vectordb", embeddings, allow_dangerous_deserialization=True)
-db_retriever = db.as_retriever(search_type="similarity",search_kwargs={"k": 4})
-
+db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 4})
 
+# Streamlit page configuration
 st.set_page_config(page_title="Qanoon-Bot")
-col1, col2, col3 = st.columns([1,4,1])
+col1, col2, col3 = st.columns([1, 4, 1])
 with col2:
     st.image("https://s3.ap-south-1.amazonaws.com/makerobosfastcdn/cms-assets/Legal_AI_Chatbot.png")
 
@@ -58,44 +50,43 @@ div.stButton > button:first-child {
 div.stButton > button:active {
     background-color: #ff6262;
 }
-   div[data-testid="stStatusWidget"] div button {
-        display: none;
-        }
-    
-    .reportview-container {
-            margin-top: -2em;
-        }
-        #MainMenu {visibility: hidden;}
-        .stDeployButton {display:none;}
-        footer {visibility: hidden;}
-        #stDecoration {display:none;}
-    button[title="View fullscreen"]{
-    visibility: hidden;}
-        </style>
+div[data-testid="stStatusWidget"] div button {
+    display: none;
+}
+.reportview-container {
+    margin-top: -2em;
+}
+#MainMenu {visibility: hidden;}
+.stDeployButton {display:none;}
+footer {visibility: hidden;}
+#stDecoration {display:none;}
+button[title="View fullscreen"] {
+    visibility: hidden;
+}
+    </style>
 """,
     unsafe_allow_html=True,
 )
 
+# Function to reset conversation
 def reset_conversation():
-  st.session_state.messages = []
-  st.session_state.memory.clear()
+    st.session_state.messages = []
+    st.session_state.memory.clear()
 
+# Initialize session state
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
 if "memory" not in st.session_state:
-    st.session_state.memory = ConversationBufferWindowMemory(k=5, memory_key="chat_history",return_messages=True,output_key='answer') 
-
-#embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"})
-#db=FAISS.load_local("/content/ipc_vector_db", embeddings, allow_dangerous_deserialization=True)
+    st.session_state.memory = ConversationBufferWindowMemory(k=5, memory_key="chat_history", return_messages=True, output_key='answer')
 
+# Initialize the prompt
+prompt = PromptTemplate(template=custom_template, input_variables=['context', 'chat_history', 'question'])
 
-prompt = PromptTemplate(template=custom_template)
+# Initialize the LLM
+llm = ChatOpenAI(temperature=0.2, model_name='gpt-3.5-turbo-0125')
 
-# You can also use other LLMs options from https://python.langchain.com/docs/integrations/llms. Here I have used TogetherAI API
-
-from config import together_api
-llm=ChatOpenAI(temperature=0.2,model_name='gpt-3.5-turbo-0125')
+# Create the ConversationalRetrievalChain
 qa = ConversationalRetrievalChain.from_llm(
     llm=llm,
     memory=st.session_state.memory,
@@ -103,30 +94,31 @@ qa = ConversationalRetrievalChain.from_llm(
     combine_docs_chain_kwargs={'prompt': prompt}
 )
 
+# Display chat history
 for message in st.session_state.messages:
     with st.chat_message(message.get("role")):
         st.write(message.get("content"))
 
+# Handle user input
 input_prompt = st.chat_input("Say something")
 
 if input_prompt:
     with st.chat_message("user"):
         st.write(input_prompt)
 
-    st.session_state.messages.append({"role":"user","content":input_prompt})
+    st.session_state.messages.append({"role": "user", "content": input_prompt})
 
     with st.chat_message("assistant"):
-        with st.status("Thinking 💡...",expanded=True):
+        with st.status("Thinking 💡...", expanded=True):
             result = qa.invoke(input=input_prompt)
 
             message_placeholder = st.empty()
 
-            full_response = "**_Note: Information provided by Qanoon-Bot may be inaccurate. ** \n\n\n"
-        for chunk in result["answer"]:
-            full_response+=chunk
-            time.sleep(0.02)
-            
-            message_placeholder.markdown(full_response+" ▌")
-        st.button('Reset All Chat 🗑️', on_click=reset_conversation)
-
-    st.session_state.messages.append({"role":"assistant","content":result["answer"]})
+            full_response = "**_Note: Information provided by Qanoon-Bot may be inaccurate._** \n\n\n"
+            for chunk in result["answer"]:
+                full_response += chunk
+                time.sleep(0.02)
+                message_placeholder.markdown(full_response + " ▌")
+                
+    st.session_state.messages.append({"role": "assistant", "content": result["answer"]})
+    st.button('Reset All Chat 🗑️', on_click=reset_conversation)