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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd
import gradio as gr
import numpy as np
import time
import os
#import pyodbc
#import pkg_resources
'''
# Get a list of installed packages and their versions
installed_packages = {pkg.key: pkg.version for pkg in pkg_resources.working_set}
# Print the list of packages
for package, version in installed_packages.items():
print(f"{package}=={version}")
'''
'''
# Replace the connection parameters with your SQL Server information
server = 'your_server'
database = 'your_database'
username = 'your_username'
password = 'your_password'
driver = 'SQL Server' # This depends on the ODBC driver installed on your system
# Create the connection string
connection_string = f'DRIVER={{{driver}}};SERVER={server};DATABASE={database};UID={username};PWD={password}'
# Connect to the SQL Server
conn = pyodbc.connect(connection_string)
#============================================================================
# Replace "your_query" with your SQL query to fetch data from the database
query = 'SELECT * FROM your_table_name'
# Use pandas to read data from the SQL Server and store it in a DataFrame
df = pd.read_sql_query(query, conn)
# Close the SQL connection
conn.close()
'''
data = {
"year": [1896, 1900, 1904, 2004, 2008, 2012],
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
}
table = pd.DataFrame.from_dict(data)
# Load the chatbot model
chatbot_model_name = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(chatbot_model_name)
model = AutoModelForCausalLM.from_pretrained(chatbot_model_name)
# Load the SQL Model
sql_model_name = "microsoft/tapex-large-finetuned-wtq"
sql_tokenizer = TapexTokenizer.from_pretrained(sql_model_name)
sql_model = BartForConditionalGeneration.from_pretrained(sql_model_name)
#sql_response = None
conversation_history = []
def predict(input, conversation_history): #history=[]):
#global sql_response
# Check if the user input is a question
#is_question = "?" in input
'''
if is_question:
sql_encoding = sql_tokenizer(table=table, query=input + sql_tokenizer.eos_token, return_tensors="pt")
sql_outputs = sql_model.generate(**sql_encoding)
sql_response = sql_tokenizer.batch_decode(sql_outputs, skip_special_tokens=True)
else:
'''
bot_input = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")
chat_history_ids = model.generate(bot_input, max_length=1000, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(chat_history_ids[:, bot_input.shape[-1]:][0], skip_special_tokens=True)
#return response
'''
# tokenize the new input sentence
new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
# generate a response
history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
# convert the tokens to text, and then split the responses into the right format
response = tokenizer.decode(history[0]).split("<|endoftext|>")
response = [(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)] # convert to tuples of list
'''
return response #, history
def chatbot_interface(user_input):
global conversation_history
conversation_history.append(user_input)
dialog_prompt = "User: " + " ".join(conversation_history) + "\nBot:"
response = predict(dialog_prompt, conversation_history)
conversation_history.append(response)
return "Bot (DialoGPT): " + response
def sqlquery(input):
sql_encoding = sql_tokenizer(table=table, query=input + sql_tokenizer.eos_token, return_tensors="pt")
sql_outputs = sql_model.generate(**sql_encoding)
sql_response = sql_tokenizer.batch_decode(sql_outputs, skip_special_tokens=True)
return sql_response
chat_interface = gr.Interface(
fn=chatbot_interface,
inputs=["text"],
outputs=["chatbot"],
live=True,
)
sql_interface = gr.Interface(
fn=sqlquery,
theme="default",
inputs=gr.Textbox(prompt="You:"),
outputs=gr.Textbox(),
live=True,
capture_session=True,
title="ST SQL Chat",
description="Type your message in the box above, and the chatbot will respond.",
)
combine_interface = gr.TabbedInterface(
interface_list=[
chat_interface,
sql_interface
],
tab_names=['Chatbot' ,'SQL Chat'],
)
if __name__ == '__main__':
combine_interface.launch() |