chat with pdf app

app.py

@@ -1,79 +1,79 @@
-import streamlit as st 
-import os 
-from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-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 dotenv import load_dotenv
-import tempfile
-import time
-
-load_dotenv()
-
-# load the Nvidia API key 
-os.environ['NVIDIA_API_KEY'] = os.getenv('NVIDIA_API_KEY')
-
-llm = ChatNVIDIA(model="meta/llama3-70b-instruct")
-
-def vector_embedding(pdf_file):
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
-        tmp_file.write(pdf_file.getvalue())
-        tmp_file_path = tmp_file.name
-
-    st.session_state.embeddings = NVIDIAEmbeddings()
-    st.session_state.loader = PyPDFLoader(tmp_file_path)
-    st.session_state.docs = st.session_state.loader.load()
-    st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=700, chunk_overlap=50)
-    st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
-    st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
-
-    os.unlink(tmp_file_path)
-
-st.title("NVIDIA NIM Demo")
-
-prompt = ChatPromptTemplate.from_template(
-    """
-    Answer the questions based on the provided context only.
-    Please provide the most accurate response based on the question
-    <context>
-    {context}
-    </context>
-    Question: {input}
-    """
-)
-
-uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
-
-if uploaded_file is not None:
-    if st.button("Process PDF"):
-        with st.spinner("Processing PDF..."):
-            vector_embedding(uploaded_file)
-        st.success("FAISS Vector Store DB is ready using NvidiaEmbedding")
-
-prompt1 = st.text_input("Enter your question about the uploaded document")
-
-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)
-    
-    with st.spinner("Generating answer..."):
-        start = time.process_time()
-        response = retrieval_chain.invoke({'input': prompt1})
-        end = time.process_time()
-    
-    st.write("Answer:", response['answer'])
-    st.write(f"Response time: {end - start:.2f} seconds")
-    
-    with st.expander("Document Similarity Search"):
-        for i, doc in enumerate(response["context"]):
-            st.write(f"Chunk {i + 1}:")
-            st.write(doc.page_content)
-            st.write("------------------------------------------")
-else:
-    if prompt1:
+import streamlit as st 
+import os 
+from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+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 dotenv import load_dotenv
+import tempfile
+import time
+
+load_dotenv()
+
+# load the Nvidia API key 
+os.environ['NVIDIA_API_KEY'] = os.getenv('NVIDIA_API_KEY')
+
+llm = ChatNVIDIA(model="meta/llama3-70b-instruct")
+
+def vector_embedding(pdf_file):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
+        tmp_file.write(pdf_file.getvalue())
+        tmp_file_path = tmp_file.name
+
+    st.session_state.embeddings = NVIDIAEmbeddings()
+    st.session_state.loader = PyPDFLoader(tmp_file_path)
+    st.session_state.docs = st.session_state.loader.load()
+    st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=700, chunk_overlap=50)
+    st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
+    st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
+
+    os.unlink(tmp_file_path)
+
+st.title("Chat with PDF")
+
+prompt = ChatPromptTemplate.from_template(
+    """
+    Answer the questions based on the provided context only.
+    Please provide the most accurate response based on the question
+    <context>
+    {context}
+    </context>
+    Question: {input}
+    """
+)
+
+uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
+
+if uploaded_file is not None:
+    if st.button("Process PDF"):
+        with st.spinner("Processing PDF..."):
+            vector_embedding(uploaded_file)
+        st.success("FAISS Vector Store DB is ready using NvidiaEmbedding")
+
+prompt1 = st.text_input("Enter your question about the uploaded document")
+
+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)
+    
+    with st.spinner("Generating answer..."):
+        start = time.process_time()
+        response = retrieval_chain.invoke({'input': prompt1})
+        end = time.process_time()
+    
+    st.write("Answer:", response['answer'])
+    st.write(f"Response time: {end - start:.2f} seconds")
+    
+    with st.expander("Document Similarity Search"):
+        for i, doc in enumerate(response["context"]):
+            st.write(f"Chunk {i + 1}:")
+            st.write(doc.page_content)
+            st.write("------------------------------------------")
+else:
+    if prompt1:
         st.warning("Please upload and process a PDF document first.")