Update src/PDFprocess_sample.py (#11)

- Update src/PDFprocess_sample.py (b317a4821026779107fdc976c88aad871c78cd3f)


Co-authored-by: Khan <[email protected]>

src/PDFprocess_sample.py

@@ -1,49 +1,49 @@
-import tempfile
-import streamlit as st
-import pickle
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-import faiss 
-
-
-def process_pdf(uploaded_file):
-    
-    all_documents = []
-    st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    
-    main_placeholder = st.empty()
-    # Creating  a temporary file to store the uploaded PDF's
-    main_placeholder.text("Data Loading...Started...✅✅✅")
-    for uploaded_file in uploaded_file:
-        with tempfile.NamedTemporaryFile(delete=False , suffix='.pdf') as temp_file:
-            temp_file.write(uploaded_file.read()) ## write file to temporary
-            temp_file_path = temp_file.name  # Get the temporary file path
-            
-            
-            # Load the PDF's from the temporary file path
-            
-        
-        loader = PyPDFLoader(temp_file_path) # Document loader
-        doc= loader.load() # load Document 
-        main_placeholder.text("Text Splitter...Started...✅✅✅")
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) # Recursive Character String
-        #final_documents = text_splitter.split_documents(doc)# splitting
-        final_documents = text_splitter.split_documents(doc)
-        all_documents.extend(final_documents)
-        
-        
-        if all_documents:
-            main_placeholder.text("Embedding Vector Started Building...✅✅✅")
-            st.session_state.vectors = FAISS.from_documents(all_documents,st.session_state.embeddings)
-            st.session_state.docs = all_documents 
-            
-            # Save FAISS vector store to disk
-            faiss_index = st.session_state.vectors.index  # Extract FAISS index
-            faiss.write_index(faiss_index, "faiss_index.bin")  # Save index to a binary file
-            main_placeholder.text("Vector database created!...✅✅✅")   
-            
-        else:
-            st.error("No documents found after processing the uploaded files or the pdf is corrupted / unsupported.")
-        
+import tempfile
+import streamlit as st
+import pickle
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+import faiss 
+
+
+def process_pdf(uploaded_file):
+    
+    all_documents = []
+    st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    
+    main_placeholder = st.empty()
+    # Creating  a temporary file to store the uploaded PDF's
+    main_placeholder.text("Data Loading...Started...✅✅✅")
+    for uploaded_file in uploaded_file:
+        with tempfile.NamedTemporaryFile(delete=False , suffix='.pdf') as temp_file:
+            temp_file.write(uploaded_file.read()) ## write file to temporary
+            temp_file_path = temp_file.name  # Get the temporary file path
+            
+            
+            # Load the PDF's from the temporary file path
+            
+        
+        loader = PyPDFLoader(temp_file_path) # Document loader
+        doc= loader.load() # load Document 
+        main_placeholder.text("Text Splitter...Started...✅✅✅")
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) # Recursive Character String
+        #final_documents = text_splitter.split_documents(doc)# splitting
+        final_documents = text_splitter.split_documents(doc)
+        all_documents.extend(final_documents)
+        
+        
+        if all_documents:
+            main_placeholder.text("Embedding Vector Started Building...✅✅✅")
+            st.session_state.vectors = FAISS.from_documents(all_documents,st.session_state.embeddings)
+            st.session_state.docs = all_documents 
+            
+            # Save FAISS vector store to disk
+            faiss_index = st.session_state.vectors.index  # Extract FAISS index
+            faiss.write_index(faiss_index, "faiss_index.bin")  # Save index to a binary file
+            main_placeholder.text("Vector database created!...✅✅✅")   
+            
+        else:
+            st.error("No documents found after processing the uploaded files or the pdf is corrupted / unsupported.")
+