add logo plus, filtred and refactoring

FindinDB.py

@@ -17,80 +17,114 @@ from langchain.embeddings import GPT4AllEmbeddings
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.document_loaders import DataFrameLoader
+from langchain.embeddings import HuggingFaceEmbeddings
 import pandas as pd
 import sqlite3
+from sentence_transformers import SentenceTransformer
+#from cleantext import clean
+import re
+model_name = 'hiiamsid/sentence_similarity_spanish_es'
+model_kwargs = {'device': 'cpu'}
+encode_kwargs = {'normalize_embeddings': True}
+hf = HuggingFaceEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs
+)
+
+
 def loadModels():
     #model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
     callback_manager = BaseCallbackManager([StreamingStdOutCallbackHandler()])
     llm = GPT4All(model="orca-mini-3b.ggmlv3.q4_0.bin",temp=0.1,streaming=True)#callback_manager=callback_manager, verbose=True,repeat_last_n=0
     embeddings = GPT4AllEmbeddings()
     return llm, embeddings
+
+
+def loadCopysAndData(pathsqlite="motor.sqlite"):
+    con = sqlite3.connect(pathsqlite)
+    copies_df = pd.read_sql_query("SELECT * from copies", con)
+    copiesT = copies_df[copies_df.copy_start =="T"]
+    copiesT=copiesT[["copy_message","id","name"]]
+    data = copiesT 
+    B=DataFrameLoader(data,page_content_column="copy_message")
+    B2=DataFrameLoader(data,page_content_column="name")
+    documents=B.load()
+    documents2=B2.load()
+    return documents,documents2
+
+def makeFaissdb(documents,folder_path,embedding):
+    try:
+        db=FAISS.load_local(folder_path=folder_path,embeddings=embedding)
+            
+    except:
+        db = FAISS.from_documents(documents, embedding)
+        FAISS.save_local(db,folder_path=folder_path)
+    return db
+
 llm,emb=loadModels()
+documents,documents2=loadCopysAndData()
 
+db=makeFaissdb(documents,"Copies",emb)
+db2=makeFaissdb(documents2,"names",emb)
+db3=makeFaissdb(documents2,"nameshf",hf)
 
-con = sqlite3.connect("motor.sqlite")
+def FinderDbs(query,dbs,filtred=False,th=1.2):
-copies_df = pd.read_sql_query("SELECT * from copies", con)
+    AllData={}
-copiesT = copies_df[copies_df.copy_start =="T"]
+    for dbt in dbs:
-copiesT=copiesT[["copy_message","id","name"]]
+        Sal = dbt.similarity_search_with_score(query,4)
-data = copiesT                
+        for output in Sal:
-B=DataFrameLoader(data,page_content_column="copy_message")
+            if output[0].metadata["id"] in AllData.keys():
-B2=DataFrameLoader(data,page_content_column="name")
+                AllData[output[0].metadata["id"]]["d"]=min([AllData[output[0].metadata["id"]]["d"]-0.1,output[1]-0.1])
-documents=B.load()
+            else:
-documents2=B2.load()
+                AllData[output[0].metadata["id"]]={"d":output[1],"page_content":output[0].page_content}
-
+    for item in AllData.items():
-try:
+        print(item)
-    db=FAISS.load_local(folder_path="Copies",embeddings=emb)
-        
-except:
-    db = FAISS.from_documents(documents, emb)
-    FAISS.save_local(db,folder_path="Copies")
-        
-try:
-    db2=FAISS.load_local(folder_path="names",embeddings=emb)
-        
-except:
-    db2 = FAISS.from_documents(documents2, emb)
-    FAISS.save_local(db2,folder_path="names")
-
-def FinderDb(query,dbs):
-    Sal = dbs.similarity_search_with_score(query,3)
-    page_content=[]
-    id=[]
-    d=[]
-    for output in Sal:
-        page_content.append(output[0].page_content)
-        id.append(output[0].metadata["id"])
-        d.append(output[1])
-    espacio="""
-
-
-########################
     
+    if filtred:
+        filtredData={}
+        for row in AllData.keys():
+            if AllData[row]["d"]<1.2:
+                filtredData[row]=AllData[row]
+        filtredData=dict(sorted(filtredData.items(), key=lambda item: item[1]["d"]))
+        return  filtredData,filtredData.keys()
 
 
-"""
+    else:
-    page_content=espacio.join(page_content)  
+        AllData=dict(sorted(AllData.items(), key=lambda item: item[1]["d"]))
-    return  page_content,d,id
+        return  AllData,AllData.keys()
 
-def QARequest(Pregunta):
+def QARequest(Pregunta,filtred=False):
     query = Pregunta
-    page_content,d,id=FinderDb(query,db)
+    AllData=FinderDbs(query,[db,db2],filtred)
-    page_content2,d2,id2=FinderDb(query,db2)
+    return AllData
-    return page_content,d,id,page_content2,d2,id2
 
 
 
 with gr.Blocks() as demo:
+    gr.Image("logo.jpg",height=100)
+    gr.Markdown("Esta es la busqueda que hace el usuario")
     Pregunta = gr.Textbox(label="Pregunta")
-    #Respuesta = gr.Textbox(label="Respuesta")
+    #Pregunta = re.sub(r"(@\[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)|^rt|http.+?", "", Pregunta)
-    #id = gr.Textbox(label="id")
+    #Pregunta=Pregunta.strip().lower()
-    #metrica=gr.Textbox(label="metrica")
+    
-    Respuesta2 = gr.Textbox(label="Respuesta2")
+    filtred=gr.Checkbox(label="filtrado")
-    id2 = gr.Textbox(label="id2")
+    
-    metrica2=gr.Textbox(label="metrica2")
+    gr.Markdown("Respuestas para orca desde los copys")
+    Respuesta = gr.Textbox(label="Respuesta")
+    id = gr.Textbox(label="id")
+    # metrica=gr.Textbox(label="metrica")
+    # gr.Markdown("Respuestas para orca desde los names")
+    # Respuesta2 = gr.Textbox(label="Respuesta2")
+    # id2 = gr.Textbox(label="id2")
+    # metrica2=gr.Textbox(label="metrica2")
+    # gr.Markdown("Respuestas para hf desde los names")
+    # Respuesta3 = gr.Textbox(label="Respuesta3")
+    # id3 = gr.Textbox(label="id3")
+    # metrica3=gr.Textbox(label="metrica3")
     Enviar_btn = gr.Button("Responder")
 
-    Enviar_btn.click(fn=QARequest, inputs=Pregunta, outputs=[Respuesta2,metrica2,id2], api_name="Respuestas") # Respuesta,metrica,id,
+    Enviar_btn.click(fn=QARequest, inputs=[Pregunta,filtred], outputs=[Respuesta,id], api_name="Angela") # 
 
 demo.launch() #