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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd
import torch
import numpy as np
import time
import os
#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}")
'''
# Load the chatbot model
chatbot_model_name = "microsoft/DialoGPT-medium" #"gpt2"
chatbot_tokenizer = AutoTokenizer.from_pretrained(chatbot_model_name)
chatbot_model = AutoModelForCausalLM.from_pretrained(chatbot_model_name)
# Load the SQL Model
#wikisql take longer to process
#model_name = "microsoft/tapex-large-finetuned-wikisql" # You can change this to any other model from the list above
#model_name = "microsoft/tapex-base-finetuned-wikisql"
#model_name = "microsoft/tapex-base-finetuned-wtq"
model_name = "microsoft/tapex-large-finetuned-wtq"
#model_name = "google/tapas-base-finetuned-wtq"
sql_tokenizer = TapexTokenizer.from_pretrained(model_name)
sql_model = BartForConditionalGeneration.from_pretrained(model_name)
data = {
"year": [1896, 1900, 1904, 2004, 2008, 2012],
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
}
table = pd.DataFrame.from_dict(data)
chat_history_ids = None
bot_input_ids = None
def chatbot_response(user_message):
global chat_history_ids
global bot_input_ids
# Check if the user input is a question
is_question = "?" in user_message
if is_question:
# If the user input is a question, use TAPEx for question-answering
#inputs = user_query
encoding = sql_tokenizer(table=table, query=user_message, return_tensors="pt")
outputs = sql_model.generate(**encoding)
response = sql_tokenizer.batch_decode(outputs, skip_special_tokens=True)
else:
# Generate chatbot response using the chatbot model
'''
inputs = chatbot_tokenizer.encode("User: " + user_message, return_tensors="pt")
outputs = chatbot_model.generate(inputs, max_length=100, num_return_sequences=1)
response = chatbot_tokenizer.decode(outputs[0], skip_special_tokens=True)
'''
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = chatbot_tokenizer.encode(user_message + chatbot_tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
if chat_history_ids is not None:
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1)
else:
bot_input_ids = new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = chatbot_model.generate(bot_input_ids, max_length=1000, pad_token_id=chatbot_tokenizer.eos_token_id)
response = chatbot_tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
return response
# Define the chatbot and SQL execution interfaces using Gradio
chatbot_interface = gr.Interface(
fn=chatbot_response,
inputs=gr.Textbox(prompt="You:"),
outputs=gr.Textbox(),
live=True,
capture_session=True,
title="ST Chatbot",
description="Type your message in the box above, and the chatbot will respond.",
)
# Launch the Gradio interface
if __name__ == "__main__":
chatbot_interface.launch()
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