removed duplicate

public_app.py

@@ -1,83 +0,0 @@
-import logging
-
-from langchain import PromptTemplate, LLMChain
-from langchain.chains.question_answering import load_qa_chain
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.chat_models import ChatOpenAI
-import gradio as gr
-import json
-
-from prompts import PROMPT_EXTRACT_DATE, PROMPT_FED_ANALYST
-from filterminutes import search_with_filter
-
-# --------------------------Load the sentence transformer and the vector store--------------------------#
-model_name = 'sentence-transformers/all-mpnet-base-v2'
-model_kwargs = {'device': 'cpu'}
-encode_kwargs = {'normalize_embeddings': False}
-embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs)
-vs = FAISS.load_local("MINUTES_FOMC_HISTORY", embeddings)
-
-# --------------------------Import the prompts------------------#
-PROMPT_DATE = PromptTemplate.from_template(PROMPT_EXTRACT_DATE)
-PROMPT_ANALYST = PromptTemplate.from_template(PROMPT_FED_ANALYST)
-
-
-# --------------------------define the qa chain for answering queries--------------------------#
-def load_chains(open_ai_key):
-    date_extractor = LLMChain(llm=ChatOpenAI(temperature=0, model_name='gpt-3.5-turbo', openai_api_key=open_ai_key),
-                              prompt=PROMPT_DATE)
-    fed_chain = load_qa_chain(llm=ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0, openai_api_key=open_ai_key),
-                              chain_type='stuff', prompt=PROMPT_ANALYST)
-    return date_extractor, fed_chain
-
-
-def get_chain(query, api_key):
-    """
-    Detects the date, computes similarity, and answers the query using
-    only documents corresponding to the date requested.
-    The query is first passed to the date extractor to extract the date
-    and then to the qa chain to answer the query.
-    Parameters
-    ----------
-    query : str
-        Query to be answered.
-    api_key : str
-        OpenAI API key.
-
-    Returns
-        Answer to the query.
-    """
-    date_extractor, fed_chain = load_chains(api_key)
-    logging.info('Extracting the date in numeric format..')
-    date_response = date_extractor.run(query)
-    if date_response != 'False':
-        filter_date = json.loads(date_response)
-
-        logging.info(f'Date parameters retrieved: {filter_date}')
-        logging.info('Running the qa with filtered context..')
-        filtered_context = search_with_filter(vs, query, init_k=200, step=300, target_k=7, filter_dict=filter_date)
-
-        logging.info(20 * '-' + 'Metadata for the documents to be used' + 20 * '-')
-        for doc in filtered_context:
-            logging.info(doc.metadata)
-    else:
-        logging.info('No date elements found. Running the qa without filtering can output incorrect results.')
-        filtered_context = vs.similarity_search(query, k=7)
-    return fed_chain({'input_documents': filtered_context[:7], 'question': query})['output_text']
-
-
-if __name__ == '__main__':
-    app = gr.Interface(fn=get_chain,
-                        inputs=[gr.Textbox(lines=2, placeholder="Enter your query", label='Your query'),
-                                gr.Textbox(lines=1, placeholder="Your OpenAI API key here", label='OpenAI Key')],
-                        description='Query the public database in FRED from 1936-2023',
-                        outputs=gr.Textbox(lines=1,  label='Answer'),
-                        title='Chat with the FOMC meeting minutes',
-                        examples=[['What was the economic outlook from the staff presented in the meeting '
-                                   'of April 2009 with respect to labour market developments and industrial production?'],
-                                   ['Who were the voting members present in the meeting on March 2010?'],
-                                   ['How important was the pandemic of Covid-19 in the discussions during 2020?'],
-                                   ['What was the impact of the oil crisis for the economic outlook during 1973?']],
-                        )
-    app.launch()