File size: 2,869 Bytes
ec9ef8b 4e86ef1 54210ca 4e86ef1 b5d991e 4e86ef1 b5d991e 4e86ef1 f24bed6 4e86ef1 d002017 d087072 d002017 436b052 54210ca e030ac0 54210ca e030ac0 54210ca 4e86ef1 54210ca 4e86ef1 54210ca e030ac0 4e86ef1 e030ac0 4e86ef1 4f6e66f e030ac0 f24bed6 4e86ef1 abad0fd 23432db 4e86ef1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd
#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 = "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-large-finetuned-wtq"
#model_name = "microsoft/tapex-base-finetuned-wtq"
tokenizer = TapexTokenizer.from_pretrained(model_name)
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)
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)
return response
def sql_response(user_query):
#inputs = tokenizer.encode("User: " + user_query, return_tensors="pt")
inputs = user_query
encoding = tokenizer(table=table, query=inputs, return_tensors="pt")
outputs = model.generate(**encoding)
response = 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="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="You:"),
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.",
)
# Combine the chatbot and SQL execution interfaces
combined_interface = gr.Interface([chatbot_interface, sql_interface], layout="horizontal")
# Launch the Gradio interface
if __name__ == "__main__":
combined_interface.launch()
|