refactor en multiples archivos

app.py

@@ -1,93 +1,19 @@
-from haystack.nodes import BM25Retriever, FARMReader
-from haystack.document_stores import ElasticsearchDocumentStore
-from haystack.pipelines import ExtractiveQAPipeline
 from abc import ABC, abstractmethod
-import certifi
 import gradio as gr
 import examples
+from document_quieries import ExtractiveProposalQueries
+from config import Config
+import certifi
 
-ca_certs=certifi.where()
-
-
-class Config():
-    es_host = "ask2democracy.es.us-central1.gcp.cloud.es.io"
-    es_user = "elastic"
-    es_password = "siKAHmmk2flwEaKNqQVZwp49"
-    proposals_index = "petrolfo"
-    #reader_model_name_or_path = "deepset/roberta-base-squad2"
-    reader_model_name_or_path = "deepset/xlm-roberta-large-squad2"
-    use_gpu = True
-    
-class DocumentQueries(ABC):
-    
-    @abstractmethod
-    def search_by_query(self, query : str, retriever_top_k: int, reader_top_k: int, es_index: str):
-        pass
-
-class ExtractiveProposalQueries(DocumentQueries):
-    
-    def __init__(self, es_host: str, es_index: str, es_user, es_password, reader_name_or_path: str, use_gpu = True) -> None:
-        reader = FARMReader(model_name_or_path = reader_name_or_path, use_gpu = use_gpu, num_processes=1)
-        self._initialize_pipeline(es_host, es_index, es_user, es_password, reader = reader)
-        
-    
-    def _initialize_pipeline(self, es_host, es_index, es_user, es_password, reader = None):
-        if reader is not None:
-            self.reader = reader
-        self.es_host = es_host
-        self.es_user = es_user
-        self.es_password = es_password
-        self.document_store = ElasticsearchDocumentStore(host = es_host, username=es_user, password=es_password, index = es_index, port = 443, scheme='https', verify_certs=True, ca_certs=ca_certs)        
-        self.retriever = BM25Retriever(document_store = self.document_store)
-        self.pipe = ExtractiveQAPipeline(self.reader, self.retriever)
-
-    def search_by_query(self, query : str, retriever_top_k: int, reader_top_k: int, es_index: str = None) :
-        if es_index is not None:
-            self._initialize_pipeline(self.es_host, es_index, self.es_user, self.es_password)
-        params = {"Retriever": {"top_k": retriever_top_k}, "Reader": {"top_k": reader_top_k}}
-        prediction = self.pipe.run( query = query, params = params)      
-        return prediction["answers"]
-    
-query = ExtractiveProposalQueries(es_host = Config.es_host, es_index = Config.proposals_index,
-                                  es_user = Config.es_user, es_password = Config.es_password,
-                                  reader_name_or_path = Config.reader_model_name_or_path,
-                                  use_gpu = Config.use_gpu)
-
-def update(query):
-    return f"{query}", f"{query}", f"{query}", f"{query}"
-
-def search(question):
-  p1_result = query.search_by_query(query = question, retriever_top_k = 5, reader_top_k = 1, es_index = "petro")
-  p2_result = query.search_by_query(query = question, retriever_top_k = 5, reader_top_k = 1, es_index = "rodolfo")
-
-  return [p1_result[0].answer, 
-          p1_result[0].context,
-          p2_result[0].answer,
-          p2_result[0].context]
-
-def update(query):
-    return f"{query}", f"{query}", f"{query}", f"{query}"
-
-def search(question):
-  p1_result = query.search_by_query(query = question, retriever_top_k = 5, reader_top_k = 1, es_index = "petro")
-  p2_result = query.search_by_query(query = question, retriever_top_k = 5, reader_top_k = 1, es_index = "rodolfo")
-
-  return [p1_result[0].answer, 
-          p1_result[0].context,
-          p2_result[0].answer,
-          p2_result[0].context]
-
+ca_certs=certifi.where()        
 query = ExtractiveProposalQueries(es_host = Config.es_host, es_index = Config.proposals_index,
                                   es_user = Config.es_user, es_password = Config.es_password,
                                   reader_name_or_path = Config.reader_model_name_or_path,
                                   use_gpu = Config.use_gpu)
 
-def update(query):
-    return f"{query}", f"{query}", f"{query}", f"{query}"
-
 def search(question):
-  p1_result = query.search_by_query(query = question, retriever_top_k = 5, reader_top_k = 3, es_index = "petro")
-  p2_result = query.search_by_query(query = question, retriever_top_k = 5, reader_top_k = 3, es_index = "rodolfo")
+  p1_result = query.search_by_query(query = question, retriever_top_k = 2, reader_top_k = 1, es_index = "petro")
+  p2_result = query.search_by_query(query = question, retriever_top_k = 2, reader_top_k = 1, es_index = "rodolfo")
 
   return [p1_result[0].answer, 
           p1_result[0].context,
@@ -161,7 +87,7 @@ examples = [
 ]
 
 iface = gr.Interface(fn=search,
-                      title= "Ask2Democracy 🇨🇴 - Elecciones precidenciales Colombia 2022",
+                      title= "Pregúntale a la democracia 🇨🇴 - Elecciones precidenciales Colombia 2022",
                       description = description,
                       inputs=[
                               gr.inputs.Textbox(lines=2, label="hazle una pregunta a los candidatos [Abajo algunos ejemplos]👇", placeholder='que va a hacer con la deuda del ICETEX?')

config.py

@@ -1,3 +1,4 @@
+
 class Config():
     es_host = "ask2democracy.es.us-central1.gcp.cloud.es.io"
     es_user = "elastic"

document_quieries → document_quieries.py

@@ -1,12 +1,8 @@
+from abc import ABC, abstractmethod
 from haystack.nodes import BM25Retriever, FARMReader
 from haystack.document_stores import ElasticsearchDocumentStore
 from haystack.pipelines import ExtractiveQAPipeline
 
-import certifi
-ca_certs=certifi.where()
-
-from abc import ABC, abstractmethod
-
 class DocumentQueries(ABC):
     
     @abstractmethod
@@ -15,11 +11,10 @@ class DocumentQueries(ABC):
 
 class ExtractiveProposalQueries(DocumentQueries):
     
-    def __init__(self, es_host: str, es_index: str, es_user, es_password, reader_name_or_path: str, use_gpu = False) -> None:
+    def __init__(self, es_host: str, es_index: str, es_user, es_password, reader_name_or_path: str, use_gpu = True) -> None:
         reader = FARMReader(model_name_or_path = reader_name_or_path, use_gpu = use_gpu, num_processes=1)
         self._initialize_pipeline(es_host, es_index, es_user, es_password, reader = reader)
         
-    
     def _initialize_pipeline(self, es_host, es_index, es_user, es_password, reader = None):
         if reader is not None:
             self.reader = reader
@@ -36,4 +31,3 @@ class ExtractiveProposalQueries(DocumentQueries):
         params = {"Retriever": {"top_k": retriever_top_k}, "Reader": {"top_k": reader_top_k}}
         prediction = self.pipe.run( query = query, params = params)      
         return prediction["answers"]
-