Update app.py

app.py

@@ -1,4 +1,186 @@
-import re 
+# import re 
+# import PyPDF2
+# from langchain_community.embeddings import OllamaEmbeddings
+# from langchain.text_splitter import RecursiveCharacterTextSplitter
+# from langchain_community.vectorstores import Chroma
+# from langchain.chains import ConversationalRetrievalChain
+# from langchain_community.chat_models import ChatOllama
+# from langchain_groq import ChatGroq
+# from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+# import chainlit as cl
+# from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer
+# import logging
+# import pypandoc
+# import pdfkit
+# from paddleocr import PaddleOCR
+# import fitz  
+# import asyncio
+# from langchain_nomic.embeddings import NomicEmbeddings
+
+# llm_groq = ChatGroq(
+#             model_name='llama3-70b-8192'
+#     )
+
+# # Initialize anonymizer
+# anonymizer = PresidioReversibleAnonymizer(analyzed_fields=['PERSON', 'EMAIL_ADDRESS', 'PHONE_NUMBER', 'IBAN_CODE', 'CREDIT_CARD', 'CRYPTO', 'IP_ADDRESS', 'LOCATION', 'DATE_TIME', 'NRP', 'MEDICAL_LICENSE', 'URL'], faker_seed=18)
+
+# def extract_text_from_pdf(file_path):
+#     pdf = PyPDF2.PdfReader(file_path)
+#     pdf_text = ""
+#     for page in pdf.pages:
+#         pdf_text += page.extract_text()
+#     return pdf_text
+
+# def has_sufficient_selectable_text(page, threshold=50):
+#     text = page.extract_text()
+#     if len(text.strip()) > threshold:
+#         return True
+#     return False
+
+# async def get_text(file_path):
+#     text = ""
+#     try:
+#         logging.info("Starting OCR process for file: %s", file_path)
+#         extension = file_path.split(".")[-1].lower()
+#         allowed_extension = ["jpg", "jpeg", "png", "pdf", "docx"]
+#         if extension not in allowed_extension:
+#             error = "Not a valid File. Allowed Format are jpg, jpeg, png, pdf, docx"
+#             logging.error(error)
+#             return {"error": error}
+        
+#         if extension == "docx":
+#             file_path = convert_docx_to_pdf(file_path)
+        
+#         ocr = PaddleOCR(use_angle_cls=True, lang='en')
+#         result = ocr.ocr(file_path, cls=True)
+#         for idx in range(len(result)):
+#             res = result[idx]
+#             for line in res:
+#                 text += line[1][0] + " "
+#         logging.info("OCR process completed successfully for file: %s", file_path)
+#     except Exception as e:
+#         logging.error("Error occurred during OCR process for file %s: %s", file_path, e)
+#         text = "Error occurred during OCR process."
+#     logging.info("Extracted text: %s", text)
+#     return text
+
+# def convert_docx_to_pdf(input_path):
+#     html_path = input_path.replace('.docx', '.html')
+#     output_path = ".".join(input_path.split(".")[:-1]) + ".pdf"
+#     pypandoc.convert_file(input_path, 'html', outputfile=html_path)
+#     pdfkit.from_file(html_path, output_path)
+#     logging.info("DOCX Format Handled")
+#     return output_path
+
+# async def extract_text_from_mixed_pdf(file_path):
+#     pdf = PyPDF2.PdfReader(file_path)
+#     ocr = PaddleOCR(use_angle_cls=True, lang='en')
+#     pdf_text = ""
+#     for i, page in enumerate(pdf.pages):
+#         text = page.extract_text()
+#         if not has_sufficient_selectable_text(page):
+#             logging.info(f"Page {i+1} has insufficient selectable text, performing OCR.")
+#             pdf_document = fitz.open(file_path)
+#             pdf_page = pdf_document.load_page(i)
+#             pix = pdf_page.get_pixmap()
+#             image_path = f"page_{i+1}.png"
+#             pix.save(image_path)
+#             result = ocr.ocr(image_path, cls=True)
+#             for idx in range(len(result)):
+#                 res = result[idx]
+#                 for line in res:
+#                     text += line[1][0] + " "
+#         pdf_text += text
+#     return pdf_text
+
+# @cl.on_chat_start
+# async def on_chat_start():
+    
+#     files = None # Initialize variable to store uploaded files
+
+#     # Wait for the user to upload a file
+#     while files is None:
+#         files = await cl.AskFileMessage(
+#             content="Please upload a pdf file to begin!",
+#             # accept=["application/pdf"],
+#             accept=["application/pdf", "image/jpeg", "image/png", "application/vnd.openxmlformats-officedocument.wordprocessingml.document"],
+#             max_size_mb=100,
+#             timeout=180, 
+#         ).send()
+
+#     file = files[0] # Get the first uploaded file
+    
+#     # Inform the user that processing has started
+#     msg = cl.Message(content=f"Processing `{file.name}`...")
+#     await msg.send()
+
+#     # Extract text from PDF, checking for selectable and handwritten text
+#     if file.name.endswith('.pdf'):
+#         pdf_text = await extract_text_from_mixed_pdf(file.path)
+#     else:
+#         pdf_text = await get_text(file.path)
+
+#     # Anonymize the text
+#     anonymized_text = anonymizer.anonymize(
+#         pdf_text
+#     )
+    
+#     embeddings = NomicEmbeddings(model="nomic-embed-text-v1.5")
+    
+#     docsearch = await cl.make_async(Chroma.from_texts)(
+#         [anonymized_text], embeddings, metadatas=[{"source": "0-pl"}]
+#     )
+#     # }
+    
+#     # Initialize message history for conversation
+#     message_history = ChatMessageHistory()
+    
+#     # Memory for conversational context
+#     memory = ConversationBufferMemory(
+#         memory_key="chat_history",
+#         output_key="answer",
+#         chat_memory=message_history,
+#         return_messages=True,
+#     )
+
+#     # Create a chain that uses the Chroma vector store
+#     chain = ConversationalRetrievalChain.from_llm(
+#         llm = llm_groq,
+#         chain_type="stuff",
+#         retriever=docsearch.as_retriever(),
+#         memory=memory,
+#         return_source_documents=True,
+#     )
+
+#     # Let the user know that the system is ready
+#     msg.content = f"Processing `{file.name}` done. You can now ask questions!"
+#     await msg.update()
+#     # Store the chain in user session
+#     cl.user_session.set("chain", chain)
+
+
+# @cl.on_message
+# async def main(message: cl.Message):
+        
+#     # Retrieve the chain from user session
+#     chain = cl.user_session.get("chain") 
+#     # Callbacks happen asynchronously/parallel 
+#     cb = cl.AsyncLangchainCallbackHandler()
+    
+#     # Call the chain with user's message content
+#     res = await chain.ainvoke(message.content, callbacks=[cb])
+#     answer = anonymizer.deanonymize(
+#         res["answer"]
+#     )  
+#     text_elements = [] 
+            
+#     # Return results
+#     await cl.Message(content=answer, elements=text_elements).send()
+
+
+
+# v2
+import re
 import PyPDF2
 from langchain_community.embeddings import OllamaEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
@@ -13,9 +195,10 @@ import logging
 import pypandoc
 import pdfkit
 from paddleocr import PaddleOCR
-import fitz  
+import fitz
 import asyncio
 from langchain_nomic.embeddings import NomicEmbeddings
+import os
 
 llm_groq = ChatGroq(
             model_name='llama3-70b-8192'
@@ -157,6 +340,7 @@ async def on_chat_start():
     await msg.update()
     # Store the chain in user session
     cl.user_session.set("chain", chain)
+    cl.user_session.set("file_path", file.path)  # Store the file path in session
 
 
 @cl.on_message
@@ -176,3 +360,22 @@ async def main(message: cl.Message):
             
     # Return results
     await cl.Message(content=answer, elements=text_elements).send()
+
+@cl.on_chat_end
+async def on_chat_end():
+    chain = cl.user_session.get("chain")
+    file_path = cl.user_session.get("file_path")
+
+    if chain:
+        # Clear the vector store
+        chain.retriever.store.clear()
+
+    if file_path and os.path.exists(file_path):
+        # Remove the uploaded file
+        os.remove(file_path)
+        
+    # Clear the user session data
+    cl.user_session.clear()
+
+    logging.info("User session ended, data cleared.")
+