Update app.py

app.py

@@ -6,71 +6,74 @@ from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain_core.prompts import ChatPromptTemplate
 from langchain.chains import create_retrieval_chain
 from langchain_community.vectorstores import FAISS
-from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain_community.document_loaders import PyPDFLoader
 from langchain_google_genai import GoogleGenerativeAIEmbeddings
 from dotenv import load_dotenv
-import os
+import time
+
 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")
+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():
+def vector_embedding(uploaded_files):
 
     if "vectors" not in st.session_state:
 
-        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 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})
-    print("Response time :",time.process_time()-start)
+        st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+        # Load documents from the uploaded PDF files
+        documents = []
+        for uploaded_file in uploaded_files:
+            loader = PyPDFLoader(uploaded_file)
+            documents.extend(loader.load())
+
+        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        st.session_state.final_documents = st.session_state.text_splitter.split_documents(documents)
+        
+        if st.session_state.final_documents:
+            st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
+            st.write("Vector Store DB Is Ready")
+        else:
+            st.write("No documents were loaded or processed. Please check your files.")
+
+prompt1 = st.text_input("Enter Your Question From Documents")
+
+uploaded_files = st.file_uploader("Upload your PDF files", accept_multiple_files=True, type=["pdf"])
+
+if st.button("Documents Embedding") and uploaded_files:
+    vector_embedding(uploaded_files)
+
+if prompt1 and "vectors" in st.session_state:
+    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:.2f} seconds")
     st.write(response['answer'])
 
-    # With a streamlit expander
+    # With a Streamlit expander
     with st.expander("Document Similarity Search"):
         # Find the relevant chunks
         for i, doc in enumerate(response["context"]):
             st.write(doc.page_content)
-            st.write("--------------------------------")
+            st.write("--------------------------------")
+else:
+    st.write("Please upload your documents and click on 'Documents Embedding' first.")