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Update app.py
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app.py
CHANGED
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@@ -201,8 +201,8 @@ from langchain_nomic.embeddings import NomicEmbeddings
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import os
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llm_groq = ChatGroq(
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-
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# Initialize anonymizer
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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)
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@@ -285,7 +285,6 @@ async def on_chat_start():
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while files is None:
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files = await cl.AskFileMessage(
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content="Please upload a pdf file to begin!",
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# accept=["application/pdf"],
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accept=["application/pdf", "image/jpeg", "image/png", "application/vnd.openxmlformats-officedocument.wordprocessingml.document"],
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max_size_mb=100,
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timeout=180,
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@@ -313,7 +312,6 @@ async def on_chat_start():
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docsearch = await cl.make_async(Chroma.from_texts)(
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[anonymized_text], embeddings, metadatas=[{"source": "0-pl"}]
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)
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# }
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# Initialize message history for conversation
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message_history = ChatMessageHistory()
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@@ -340,6 +338,7 @@ async def on_chat_start():
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await msg.update()
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# Store the chain in user session
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cl.user_session.set("chain", chain)
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cl.user_session.set("file_path", file.path) # Store the file path in session
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@@ -363,12 +362,12 @@ async def main(message: cl.Message):
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@cl.on_chat_end
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async def on_chat_end():
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-
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file_path = cl.user_session.get("file_path")
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if
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# Clear the vector store
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if file_path and os.path.exists(file_path):
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# Remove the uploaded file
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@@ -378,4 +377,3 @@ async def on_chat_end():
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cl.user_session.clear()
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logging.info("User session ended, data cleared.")
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-
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import os
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llm_groq = ChatGroq(
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model_name='llama3-70b-8192'
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)
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# Initialize anonymizer
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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)
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while files is None:
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files = await cl.AskFileMessage(
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content="Please upload a pdf file to begin!",
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accept=["application/pdf", "image/jpeg", "image/png", "application/vnd.openxmlformats-officedocument.wordprocessingml.document"],
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max_size_mb=100,
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timeout=180,
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docsearch = await cl.make_async(Chroma.from_texts)(
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[anonymized_text], embeddings, metadatas=[{"source": "0-pl"}]
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)
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# Initialize message history for conversation
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message_history = ChatMessageHistory()
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await msg.update()
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# Store the chain in user session
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cl.user_session.set("chain", chain)
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cl.user_session.set("docsearch", docsearch) # Store the docsearch in session
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cl.user_session.set("file_path", file.path) # Store the file path in session
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@cl.on_chat_end
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async def on_chat_end():
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docsearch = cl.user_session.get("docsearch")
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file_path = cl.user_session.get("file_path")
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if docsearch:
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# Clear the vector store
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docsearch.delete()
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if file_path and os.path.exists(file_path):
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# Remove the uploaded file
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cl.user_session.clear()
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logging.info("User session ended, data cleared.")
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