Update src/PDFprocess_sample.py

src/PDFprocess_sample.py

@@ -8,87 +8,87 @@ from langchain_community.vectorstores import FAISS
 import faiss 
 
 
-# def process_pdf(uploaded_file):
+def process_pdf(uploaded_file):
     
-#     all_documents = []
-#     # st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    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:
+    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
+        # 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
 
-#         temp_file_path = os.path.join("/tmp", uploaded_file.name)
-#         with open(temp_file_path, "wb") as f:
-#             f.write(uploaded_file.read())
+        temp_file_path = os.path.join("/tmp", uploaded_file.name)
+        with open(temp_file_path, "wb") as f:
+            f.write(uploaded_file.read())
 
-#         st.write(f"Uploaded files: {[file.name for file in uploaded_file]}")
+        st.write(f"Uploaded files: {[file.name for file in uploaded_file]}")
 
             
             
-#             # Load the PDF's from 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)
+        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 
+        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!...✅✅✅")   
+            # 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.")
+        else:
+            st.error("No documents found after processing the uploaded files or the pdf is corrupted / unsupported.")
 
 
 
-def process_pdf(uploaded_files):
-    all_documents = []
-    main_placeholder = st.empty()
-    main_placeholder.text("Data Loading...Started...✅✅✅")
+# def process_pdf(uploaded_files):
+#     all_documents = []
+#     main_placeholder = st.empty()
+#     main_placeholder.text("Data Loading...Started...✅✅✅")
 
-    for uploaded_file in uploaded_files:
-        temp_file_path = os.path.join("/tmp", uploaded_file.name)
-        with open(temp_file_path, "wb") as f:
-            f.write(uploaded_file.read())
+#     for uploaded_file in uploaded_files:
+#         temp_file_path = os.path.join("/tmp", uploaded_file.name)
+#         with open(temp_file_path, "wb") as f:
+#             f.write(uploaded_file.read())
 
-        st.write(f"Uploaded files: {[file.name for file in uploaded_files]}")
+#         st.write(f"Uploaded files: {[file.name for file in uploaded_files]}")
 
-        loader = PyPDFLoader(temp_file_path)
-        doc = loader.load()
-        main_placeholder.text("Text Splitter...Started...✅✅✅")
+#         loader = PyPDFLoader(temp_file_path)
+#         doc = loader.load()
+#         main_placeholder.text("Text Splitter...Started...✅✅✅")
 
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-        final_documents = text_splitter.split_documents(doc)
-        all_documents.extend(final_documents)
+#         text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+#         final_documents = text_splitter.split_documents(doc)
+#         all_documents.extend(final_documents)
 
-    if all_documents:
-        main_placeholder.text("Embedding Vector Started Building...✅✅✅")
+#     if all_documents:
+#         main_placeholder.text("Embedding Vector Started Building...✅✅✅")
         
-        # ⏬ Move embedding initialization here
-        st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-        st.session_state.vectors = FAISS.from_documents(all_documents, st.session_state.embeddings)
-        st.session_state.docs = all_documents
+#         # ⏬ Move embedding initialization here
+#         st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+#         st.session_state.vectors = FAISS.from_documents(all_documents, st.session_state.embeddings)
+#         st.session_state.docs = all_documents
 
-        faiss_index = st.session_state.vectors.index
-        faiss.write_index(faiss_index, "faiss_index.bin")
-        main_placeholder.text("Vector database created!...✅✅✅")
+#         faiss_index = st.session_state.vectors.index
+#         faiss.write_index(faiss_index, "faiss_index.bin")
+#         main_placeholder.text("Vector database created!...✅✅✅")
 
-    else:
-        st.error("No documents found or the PDF is corrupted.")
+#     else:
+#         st.error("No documents found or the PDF is corrupted.")