Update app.py

app.py

@@ -12,62 +12,60 @@ from dotenv import load_dotenv
 import os
 load_dotenv()
 
-# Load the GROQ and OpenAI API KEY
-groq_api_key = os.getenv('GROQ_API_KEY')
-os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
+## load the GROQ And OpenAI API KEY 
+groq_api_key=os.getenv('GROQ_API_KEY')
+os.environ["GOOGLE_API_KEY"]=os.getenv("GOOGLE_API_KEY")
 
 st.title("Gemma Model Document Q&A")
 
-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")
 
-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.
+Please provide the most accurate response based on the question
 <context>
 {context}
 <context>
-Questions: {input}
+Questions:{input}
+
 """
 )
 
-def vector_embedding(uploaded_files):
+def vector_embedding():
+
     if "vectors" not in st.session_state:
-        st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-        
-        # Save the uploaded files and load them
-        with open("uploaded_files.zip", "wb") as f:
-            f.write(uploaded_files.getbuffer())
-        
-        # Extract the uploaded files
-        os.system("unzip -o uploaded_files.zip -d ./uploaded_data")
-        
-        st.session_state.loader = PyPDFDirectoryLoader("./uploaded_data")  # 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)  # Splitting
-        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)  # Vector OpenAI embeddings
-
-uploaded_files = st.file_uploader("Upload Your PDF Files", accept_multiple_files=True, type=["pdf"])
 
-if st.button("Documents Embedding"):
-    if uploaded_files:
-        vector_embedding(uploaded_files[0])
-        st.write("Vector Store DB Is Ready")
-    else:
-        st.write("Please upload PDF files.")
+        st.session_state.embeddings=GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+        st.session_state.loader=PyPDFDirectoryLoader("./us_census") ## 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
+
+
+
 
-prompt1 = st.text_input("Enter Your Question From Documents")
+
+prompt1=st.text_input("Enter Your Question From Doduments")
+
+
+if st.button("Documents Embedding"):
+    vector_embedding()
+    st.write("Vector Store DB Is Ready")
 
 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})
-    st.write(f"Response time: {time.process_time() - start} seconds")
+    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'])
 
     # With a streamlit expander
@@ -75,4 +73,4 @@ if prompt1:
         # Find the relevant chunks
         for i, doc in enumerate(response["context"]):
             st.write(doc.page_content)
-            st.write("--------------------------------")
+            st.write("--------------------------------")