Update app.py
Browse files
app.py
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from user_utils import *
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import streamlit as st
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from dotenv import load_dotenv
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load_dotenv()
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st.header("Automatic Ticket Classification Tool")
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# Capture user input
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if user_input:
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try:
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#
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embeddings = create_embeddings()
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# Fetch the Pinecone index using the API key and environment info
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@@ -37,7 +43,7 @@ def main():
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# Access the document content directly
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content = getattr(doc, "page_content", "No content available.") # Safely access content
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# Apply fine-tuned extraction model
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relevant_info = fine_tune_extraction(content, user_input)
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if relevant_info:
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import streamlit as st
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from dotenv import load_dotenv
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from transformers import pipeline
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# Load environment variables
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load_dotenv()
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# Initialize the question-answering pipeline with a pre-trained model
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qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
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def fine_tune_extraction(content, query):
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# Use the pipeline to answer the question using the document content
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result = qa_pipeline(question=query, context=content)
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return result['answer']
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def main():
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st.header("Automatic Ticket Classification Tool")
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# Capture user input
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if user_input:
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try:
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# Assuming create_embeddings and pull_from_pinecone are defined elsewhere
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embeddings = create_embeddings()
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# Fetch the Pinecone index using the API key and environment info
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# Access the document content directly
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content = getattr(doc, "page_content", "No content available.") # Safely access content
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# Apply fine-tuned extraction model to extract relevant information
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relevant_info = fine_tune_extraction(content, user_input)
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if relevant_info:
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