LTW-1181203031 commited on
Commit
ddb7963
1 Parent(s): ca01b05

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +3 -8
app.py CHANGED
@@ -1,18 +1,14 @@
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  from haystack.document_stores.memory import InMemoryDocumentStore
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  from haystack.nodes import TfidfRetriever, FARMReader
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- import google.colab
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- google.colab.drive.mount('/content/drive')
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  import pickle
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- pickle_file = '/content/drive/MyDrive/Group13_NLP_Project/knowledge_graph.pickle'
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  # Load the knowledge graph from the pickle file
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  with open(pickle_file, 'rb') as f:
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  knowledge_graph = pickle.load(f)
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- print("Knowledge graph loaded from ", pickle_file)
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-
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  document_store = InMemoryDocumentStore()
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  node_sentences = {}
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  documents = []
@@ -47,9 +43,11 @@ document_store.write_documents(documents)
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  #Initialize the retriever
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  retriever = TfidfRetriever(document_store=document_store)
 
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  #Initialize the reader
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  model_name = "primasr/multilingualbert-for-eqa-finetuned"
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  reader = FARMReader(model_name_or_path=model_name, use_gpu=False)
 
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  #Create pipeline with the component of retriever and reader
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  from haystack.pipelines import Pipeline
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  pipeline = Pipeline()
@@ -96,11 +94,8 @@ def checkReiterateQuery(query,lang):
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  import gradio as gr
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  from langdetect import detect
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- import warnings
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- warnings.filterwarnings('ignore')
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  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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-
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  chat_history = []
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  answer_counter = 0
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  def chatbot_interface(message):
 
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  from haystack.document_stores.memory import InMemoryDocumentStore
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  from haystack.nodes import TfidfRetriever, FARMReader
 
 
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  import pickle
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+ pickle_file = 'knowledge_graph.pickle'
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  # Load the knowledge graph from the pickle file
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  with open(pickle_file, 'rb') as f:
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  knowledge_graph = pickle.load(f)
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  document_store = InMemoryDocumentStore()
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  node_sentences = {}
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  documents = []
 
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  #Initialize the retriever
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  retriever = TfidfRetriever(document_store=document_store)
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+
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  #Initialize the reader
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  model_name = "primasr/multilingualbert-for-eqa-finetuned"
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  reader = FARMReader(model_name_or_path=model_name, use_gpu=False)
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+
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  #Create pipeline with the component of retriever and reader
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  from haystack.pipelines import Pipeline
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  pipeline = Pipeline()
 
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  import gradio as gr
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  from langdetect import detect
 
 
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  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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  chat_history = []
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  answer_counter = 0
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  def chatbot_interface(message):