app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd
import torch
#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"
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-large-finetuned-wtq"
#model_name = "microsoft/tapex-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)

bot_input_ids = None

tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")


def chatbot_response(user_message):
    # 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)
    response = None
    
    # Let's chat for 5 lines
    for step in range(1):
        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
    
        # append the new user input tokens to the chat history
        bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
    
        # generated a response while limiting the total chat history to 1000 tokens, 
        chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
    
        # pretty print last ouput tokens from bot
        #print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

        response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
        
    return response

def sql_response(user_query):
    
    #inputs = tokenizer.encode("User: " + user_query, return_tensors="pt")
    inputs = user_query
    encoding = sql_tokenizer(table=table, query=inputs, return_tensors="pt")
    outputs = sql_model.generate(**encoding)
    response = sql_tokenizer.batch_decode(outputs, 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.",
)

# Define the chatbot interface using Gradio
sql_interface = gr.Interface(
    fn=sql_response,
    inputs=gr.Textbox(prompt="Enter your SQL Qus:"),
    outputs=gr.Textbox(),
    live=True,
    capture_session=True,
    title="ST SQL Chatbot",
    description="Type your message in the box above, and the chatbot will respond.",
)

# Launch the Gradio interface
if __name__ == "__main__":
    chatbot_interface.launch()

sql_interface.launch()