Conexión base de datos y feedback.

app.py

@@ -5,6 +5,8 @@ import os
 import pandas as pd
 import time
 import random
+from datetime import datetime
+import pytz
 
 import openai
 from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, ServiceContext
@@ -19,11 +21,19 @@ from langchain.text_splitter import CharacterTextSplitter
 from openai.embeddings_utils import get_embedding
 from openai.embeddings_utils import cosine_similarity
 
+from pymongo import MongoClient
+
 
 # API KEY OPENAI
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
 
+# DATABASE CONNECTION
+CONNECTION = os.getenv("CONNECTION")
+DATABASE = os.getenv("DATABASE")
+COLLECTION = os.getenv("COLLECTION")
+
+
 # CONSTANTES
 DATASET_JSON = "demo-inmobiliaria.json"
 
@@ -31,6 +41,8 @@ DATASET_JSON = "demo-inmobiliaria.json"
 carpeta_actual = os.getcwd()
 print(f"Nombre de la carpeta actual: {carpeta_actual}")
 PATH_FILE = f"{os.getcwd()}/{DATASET_JSON}"
+print(f"Ubicación del archivo: {PATH_FILE}")
+
 
 class ChatBotInmobiliaria():
     def __init__(self):
@@ -95,39 +107,152 @@ examples = [["¿Cuánto está una casa en San Isidro?"],["Hay precios más barat
 
 gpt_bot = ChatBotInmobiliaria()
 gpt_bot.load_dataset(PATH_FILE)
-chat_history = []
 
-def chat(pregunta):
-    bot_message = str(gpt_bot.ask(question=pregunta))
-    chat_history.append((pregunta, bot_message))
-    time.sleep(1)
-    return chat_history
 
+# Conexión a la base de datos MongoDB
+client = MongoClient(CONNECTION)
+db = client[DATABASE]
+collection = db[COLLECTION]
+
+
+def get_datetime():
+    # Obtener la hora actual
+    hora_actual = datetime.now()
+    # Obtener la zona horaria de la hora actual
+    zona_horaria_actual = pytz.timezone('America/Argentina/Buenos_Aires')
+    # Aplicar la zona horaria a la hora actual
+    hora_actual_con_zona_horaria = hora_actual.astimezone(zona_horaria_actual)
+    return hora_actual_con_zona_horaria
+
+
+def insert_chat(data):
+    return collection.insert_one({"conversacion": data})
+
+
+def update_chat(id, data):
+    collection.update_one({"_id": id}, {"$set": {"conversacion": data}})
+
+
+def add_chat_history(chat_history, message, answer, calificacion=None):
+    global json_chat_history
+    global id_chat
+
+    json_chat = {"message": message, 
+                 "answer": answer, 
+                 "datetime": get_datetime(), 
+                 "calificacion": calificacion}
+    if len(chat_history) > 0:
+        # Si chat_history no está vacía, significa que es una continuación de la conversación anterior
+        json_chat_history.append(json_chat)
+        # chat_history.append([message, answer])
+
+        update_chat(id_chat, json_chat_history)
+    else:
+        # Si chat_history está vacía, es una nueva conversación
+        json_chat_history = []
+        json_chat_history.append(json_chat)
+        # chat_history.append([message, answer])
+
+        # Almacenar la nueva conversación en la base de datos
+        db_result = insert_chat(json_chat_history)
+        id_chat = db_result.inserted_id
 
-with gr.Blocks() as demo:
+
+with gr.Blocks() as demo:    
     gr.Markdown(f"""
     {title}
     {description}
     """)
 
     out1 = gr.Chatbot(label="Respuesta").style(height=300)
+    
     with gr.Row():
         in2 = gr.Textbox(label="Pregunta")
         enter = gr.Button("Enviar mensaje")
     
-    clear = gr.Button("Nuevo chat")
+    with gr.Row():
+        upvote_btn = gr.Button(value="👍  Conforme", interactive=True)
+        downvote_btn = gr.Button(value="👎  No conforme", interactive=True)
+        flag_btn = gr.Button(value="⚠️  Alertar", interactive=True)
+        # regenerate_btn = gr.Button(value="🔄  Regenerar", interactive=False)
+    clear_btn = gr.Button(value="🗑️  Nuevo chat", interactive=True)
     
     gr.Markdown(article)
 
+
     def respond(message, chat_history):
-        bot_message = str(gpt_bot.ask(question=message))
-        chat_history.append((message, bot_message))
+        answer = str(gpt_bot.ask(question=message))
+        add_chat_history(chat_history=chat_history, 
+                    message=message, 
+                    answer=answer)
+        chat_history.append([message, answer])
         time.sleep(1)
         return "", chat_history
 
+
     enter.click(fn=respond, inputs=[in2, out1], outputs=[in2, out1])
     in2.submit(respond, [in2, out1], [in2, out1])
-    clear.click(lambda: None, None, out1, queue=False)
+
+
+    def upvote_last_response(message, chat_history):
+        """
+        Obtener el último objeto JSON de la lista
+        Actualizar el valor del atributo "calificacion"
+        """
+        if len(json_chat_history) > 0:
+            json_chat_history[-1]["calificacion"] = "Conforme"
+            update_chat(id_chat, json_chat_history)
+        
+        return message, chat_history
+
+
+    def downvote_last_response(message, chat_history):
+        """
+        Obtener el último objeto JSON de la lista
+        Actualizar el valor del atributo "calificacion"
+        """
+        if len(json_chat_history) > 0:
+            json_chat_history[-1]["calificacion"] = "No conforme"
+            update_chat(id_chat, json_chat_history)
+        
+        return message, chat_history
+    
+    def flag_last_response(message, chat_history):
+        """
+        Obtener el último objeto JSON de la lista
+        Actualizar el valor del atributo "calificacion"
+        """
+        if len(json_chat_history) > 0:
+            json_chat_history[-1]["calificacion"] = "Alertar"
+            update_chat(id_chat, json_chat_history)
+        
+        return message, chat_history
+
+
+    # def regenerate_answer(message, chat_history):
+    #     """
+    #     Obtener el último objeto JSON de la lista
+    #     Actualizar el valor del atributo "calificacion"
+    #     """
+    #     if len(json_chat_history) > 0:
+    #         pregunta = json_chat_history[-1]["message"]
+    #         answer = str(gpt_bot.ask(question=pregunta))
+    #         add_chat_history(chat_history=chat_history, 
+    #                          message=pregunta, 
+    #                          answer=answer,
+    #                          calificacion="Regenerado")
+    #         chat_history.pop(-1)
+    #         chat_history.append([message, answer])
+    #         time.sleep(1)        
+    #     return message, chat_history
+
+
+    upvote_btn.click(upvote_last_response, inputs=[in2, out1], outputs=[in2, out1])
+    downvote_btn.click(downvote_last_response, inputs=[in2, out1], outputs=[in2, out1])
+    flag_btn.click(flag_last_response, inputs=[in2, out1], outputs=[in2, out1])
+    # regenerate_btn.click(regenerate_answer, inputs=[in2, out1], outputs=[in2, out1])
+    clear_btn.click(lambda: None, None, out1, queue=False)
+
     
 # in1 = gr.inputs.Textbox(label="Pregunta")
 # out1 = gr.outputs.Chatbot(label="Respuesta").style(height=350)

requirements.txt

@@ -5,3 +5,4 @@ PyPDF2
 openai
 langchain
 llama-index==0.5.25
+pymongo