Update app.py

app.py

@@ -9,20 +9,20 @@ from langchain_community.vectorstores import FAISS
 from langchain_community.document_loaders import PyPDFDirectoryLoader
 from langchain_google_genai import GoogleGenerativeAIEmbeddings
 from dotenv import load_dotenv
-
+import os
 load_dotenv()
 
-# Load API keys
-groq_api_key = os.getenv('groqapi')
-os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
+## load the GROQ And OpenAI API 
+
+groq_api_key=os.getenv('groqapi')
+os.environ["GOOGLE_API_KEY"]=os.getenv("GOOGLE_API_KEY")
 
 st.title("Legal Assistant")
 
-# Initialize LLM
-llm = ChatGroq(groq_api_key=groq_api_key, model_name="Llama3-8b-8192")
+llm=ChatGroq(groq_api_key=groq_api_key,
+             model_name="Llama3-8b-8192")
 
-# Define prompt
-prompt = ChatPromptTemplate.from_template(
+prompt=ChatPromptTemplate.from_template(
 """
 Answer the questions based on the provided context only.
 Please provide the most accurate response based on the question
@@ -34,49 +34,37 @@ Questions:{input}
 """
 )
 
-# Vector embedding function
 def vector_embedding():
+
     if "vectors" not in st.session_state:
-        st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-        st.session_state.loader = PyPDFDirectoryLoader("./new")  # Data ingestion
-        st.session_state.docs = st.session_state.loader.load()  # Document loading
-        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)  # Chunk creation
-        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs[:20])  # Splitting
-        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)  # Vector embedding
-
-# Initialize embeddings and vectors
+
+        st.session_state.embeddings=GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+        st.session_state.loader=PyPDFDirectoryLoader("./new") ## Data Ingestion
+        st.session_state.docs=st.session_state.loader.load() ## Document Loading
+        st.session_state.text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200) ## Chunk Creation
+        st.session_state.final_documents=st.session_state.text_splitter.split_documents(st.session_state.docs[:20]) #splitting
+        st.session_state.vectors=FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings) #vector OpenAI embeddings
+
 vector_embedding()
 
-# Input box for questions
-prompt1 = st.text_input("Enter Your Question From Documents")
 
-# Initialize session state for history if not already present
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
 
-# Sidebar for chat history and clear history button
-with st.sidebar:
-    st.title("Chat History")
-    if st.button("Clear Chat History"):
-        st.session_state.chat_history = []
-    if st.session_state.chat_history:
-        for idx, entry in enumerate(st.session_state.chat_history):
-            if st.button(f"Q{idx + 1}: {entry['question']}"):
-                st.session_state.selected_question = entry['question']
+prompt1=st.text_input("Enter Your Question From Doduments")
 
-# Display selected question from history
-if "selected_question" in st.session_state:
-    st.write(f"**Selected Question:** {st.session_state.selected_question}")
 
-# Process question and generate response
-if prompt1:
-    document_chain = create_stuff_documents_chain(llm, prompt)
-    retriever = st.session_state.vectors.as_retriever()
-    retrieval_chain = create_retrieval_chain(retriever, document_chain)
-    response = retrieval_chain.invoke({'input': prompt1})
 
-    # Save to history
-    st.session_state.chat_history.append({"question": prompt1, "answer": response['answer']})
 
-    # Display response
+import time
+
+
+
+if prompt1:
+    document_chain=create_stuff_documents_chain(llm,prompt)
+    retriever=st.session_state.vectors.as_retriever()
+    retrieval_chain=create_retrieval_chain(retriever,document_chain)
+    start=time.process_time()
+    response=retrieval_chain.invoke({'input':prompt1})
+    print("Response time :",time.process_time()-start)
     st.write(response['answer'])
+
+