Update app.py

app.py

@@ -1,114 +1,113 @@
-from langchain_nvidia import ChatNVIDIA
-from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain_community.document_loaders import PyPDFDirectoryLoader,PyPDFLoader
-from langchain_core.prompts import ChatMessagePromptTemplate,ChatPromptTemplate
-from langchain.prompts import PromptTemplate
-from dotenv import load_dotenv
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain.chains.history_aware_retriever import create_history_aware_retriever
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain.chains import create_retrieval_chain
-from langchain.chains import LLMChain
-import streamlit as st
-import os
-
-load_dotenv()
-os.environ["NVIDIA_API_KEY"] = os.getenv("NVIDIA_API_KEY")
-os.environ['HF_TOKEN'] = os.getenv("HF_TOKEN")
-
-
-def file_process(file_uploader):
-
-    all_docs = []
-    if file_uploader is not None:
-
-        for file_uploader in file_uploader:
-            with open(f"./temp/{file_uploader.name}",'wb') as f:
-                f.write(file_uploader.getbuffer())
-            loader = PyPDFLoader(f"./temp/{file_uploader.name}")
-            docs = loader.load()
-            all_docs.extend(docs)
-    
-    return all_docs
-
-def vectore_function(docs):
-    if "vectore" not in st.session_state:
-        
-        st.session_state.embedding = NVIDIAEmbeddings()
-        #st.session_state.loader = PyPDFDirectoryLoader("./pdf")
-        #st.session_state.pdf = st.session_state.loader.load()
-        st.session_state.text_spliter = RecursiveCharacterTextSplitter(chunk_size=700,chunk_overlap=70)
-        st.session_state.spliter = st.session_state.text_spliter.split_documents(docs[:30])
-        st.session_state.vectore = FAISS.from_documents(st.session_state.spliter,st.session_state.embedding)
-
-
-template_prompt = ChatPromptTemplate.from_template(
-
-    """
-        your work to give the answer to the user question from using 
-        context. please try to use attractive emoji or give the most accurcate and  
-        similar answer from the context if answer is seems 
-        similar so write the Sorry we don't have a answer yet!.
-
-        <context>
-        {context}
-        <context>
-
-        Question:{input}
-
-    """
-)
-
-
-llm = ChatNVIDIA(model="nvidia/llama-3.1-nemotron-70b-instruct")
-
-st.set_page_config(page_title="Langchain: Question/Answering Chat bot",page_icon="🦜")
-st.title('🦜LangChain')
-st.title("Title: Question/Answering Chat bot")
-st.subheader("How to use:")
-
-st.write("Step1: Upload your document. Please ensure that the document is in PDF format.")
-
-st.write("""Step2: Click on the 'Embed Document' button and wait. During this step,the entire document will be embedded and stored in the database.
-           Wait until the database setup is complete.""")  
-    
-st.write("""Step3: Once the database is ready, a text box will appear where you can
-            ask question based on the stored document.""")
-
-st.write("Step4: Enter your question in the text box and press Enter to receive answers.")
-
-st.write("⚠️Step 1: Upload the document then go to another steps")
-file_uploader = st.file_uploader("Upload the only pdf",type='pdf',accept_multiple_files=True)
-
-if file_uploader is not None:
-    #st.write(f"file name:{file_uploader.name}")
-    os.makedirs(f"./temp",exist_ok=True)
-    upload_docs = file_process(file_uploader)
-    st.write(f"length of the uploaded document: {len(upload_docs)}")
-
-st.write("⚠️Step2: Click on the 'Embed document' button and wait for the database is successfully ready")
-if st.button("Embed the document"):
-    vectore_function(upload_docs)
-    st.write("Sucessfully database is ready")
-
-st.write("⚠️After completing all the requirements then you ask your question")
-user_input = st.text_area("Ask you any question from the documentation")
-
-if st.button("👉Generate the answer"):
-    if user_input:
-        chain_stuff = create_stuff_documents_chain(llm=llm,prompt=template_prompt)
-        retriever = st.session_state.vectore.as_retriever()
-        chain_retriever = create_retrieval_chain(retriever,chain_stuff)
-        response = chain_retriever.invoke({"input":user_input})
-        st.write(response['answer'])
-
-        if "context" in response:
-            with st.expander("Similar document:"):
-                for i,doc in enumerate(response['context']):
-                    st.write(doc.page_content)
-                    st.write("---------------------")   
-
-        else:
+from langchain_nvidia import ChatNVIDIA
+from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFDirectoryLoader,PyPDFLoader
+from langchain_core.prompts import ChatMessagePromptTemplate,ChatPromptTemplate
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+from langchain.chains.history_aware_retriever import create_history_aware_retriever
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains import create_retrieval_chain
+from langchain.chains import LLMChain
+import streamlit as st
+import os
+
+load_dotenv()
+os.environ["NVIDIA_API_KEY"] = os.getenv("NVIDIA_API_KEY")
+os.environ['HF_TOKEN'] = os.getenv("HF_TOKEN")
+
+
+def file_process(file_uploader):
+
+    all_docs = []
+    if file_uploader is not None:
+
+        for file_uploader in file_uploader:
+            with open(f"./temp/{file_uploader.name}",'wb') as f:
+                f.write(file_uploader.getbuffer())
+            loader = PyPDFLoader(f"./temp/{file_uploader.name}")
+            docs = loader.load()
+            all_docs.extend(docs)
+    
+    return all_docs
+
+def vectore_function(docs):
+    if "vectore" not in st.session_state:
+        
+        st.session_state.embedding = NVIDIAEmbeddings()
+        #st.session_state.loader = PyPDFDirectoryLoader("./pdf")
+        #st.session_state.pdf = st.session_state.loader.load()
+        st.session_state.text_spliter = RecursiveCharacterTextSplitter(chunk_size=700,chunk_overlap=70)
+        st.session_state.spliter = st.session_state.text_spliter.split_documents(docs[:30])
+        st.session_state.vectore = FAISS.from_documents(st.session_state.spliter,st.session_state.embedding)
+
+
+template_prompt = ChatPromptTemplate.from_template(
+
+    """
+        your work to give the answer to the user question from using 
+        context. please try to use attractive emoji or give the most accurcate and  
+        similar answer from the context if answer is seems 
+        similar so write the Sorry we don't have a answer yet!.
+
+        <context>
+        {context}
+        <context>
+
+        Question:{input}
+
+    """
+)
+
+
+llm = ChatNVIDIA(model="nvidia/llama-3.1-nemotron-70b-instruct")
+
+st.set_page_config(page_title="Langchain: Question/Answering Chat bot",page_icon="🦜")
+st.title('🦜LangChain')
+st.title("Title: Question/Answering Chat bot")
+st.subheader("How to use:")
+
+st.write("Step1: Upload your document. Please ensure that the document is in PDF format.")
+
+st.write("""Step2: Click on the 'Embed Document' button and wait. During this step,the entire document will be embedded and stored in the database.
+           Wait until the database setup is complete.""")  
+    
+st.write("""Step3: Once the database is ready, a text box will appear where you can
+            ask question based on the stored document.""")
+
+st.write("Step4: Enter your question in the text box and press Enter to receive answers.")
+
+st.write("⚠️Step 1: Upload the document then go to another steps")
+file_uploader = st.file_uploader("Upload the only pdf",type='pdf',accept_multiple_files=True)
+
+if file_uploader is not None:
+    #st.write(f"file name:{file_uploader.name}")
+    os.makedirs(f"./temp",exist_ok=True)
+    upload_docs = file_process(file_uploader)
+    st.write(f"length of the uploaded document: {len(upload_docs)}")
+
+st.write("⚠️Step2: Click on the 'Embed document' button and wait for the database is successfully ready")
+if st.button("Embed the document"):
+    vectore_function(upload_docs)
+    st.write("Sucessfully database is ready")
+
+st.write("⚠️After completing all the requirements then you ask your question")
+user_input = st.text_area("Ask you any question from the documentation")
+
+if st.button("👉Generate the answer"):
+    if user_input:
+        chain_stuff = create_stuff_documents_chain(llm=llm,prompt=template_prompt)
+        retriever = st.session_state.vectore.as_retriever()
+        chain_retriever = create_retrieval_chain(retriever,chain_stuff)
+        response = chain_retriever.invoke({"input":user_input})
+        st.write(response['answer'])
+
+        if "context" in response:
+            with st.expander("Similar document:"):
+                for i,doc in enumerate(response['context']):
+                    st.write(doc.page_content)
+                    st.write("---------------------")   
+
+        else:
             st.write("context is not provide in the response")