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import gradio as gr
from transformers import pipeline
from transformers import T5Tokenizer, T5ForConditionalGeneration
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from openai_model import *
from bard_model import *
from llm_model import *
import tensorflow
import torch
import random
import time
import os
global default_model_name
default_model_name = "google/flan-t5-base"
def predict(input_text, input_prompt, input_model1, input_model2, input_model3):
if input_model1!="":
model_name = input_model1
response_text = llm_predict(input_text, model_name)
return response_text
elif input_model2!="":
model_name = input_model2
response_text = openai_predict(input_text, input_prompt, model_name)
return response_text
else:
model_name = input_model3
response_text = bard_predict(input_text, model_name)
return response_text
def select_choice(choice):
if choice == "Custom Large Language Model (LLM)":
return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
elif choice == "OpenAI 'GPT-3.5-Turbo' Model":
return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)]
else:
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
input_model1 = gr.Textbox(label="Enter LLM name:")
input_model2 = gr.Textbox(label="Enter OpenAI API KEY:")
input_model3 = gr.Textbox(label="Enter Google Bard API KEY:")
with gr.Blocks(theme=gr.themes.Glass(primary_hue="blue", secondary_hue="sky", neutral_hue="stone")) as demo:
gr.Markdown(
"""
# Chatbot to interact with different Large Language Models (LLMs)
[Here](https://huggingface.co/models?pipeline_tag=text2text-generation) are some popular custom text2text large lamguage models.
For example: **"google/flan-t5-base"**
OR Your **OPEN AI API KEY** to chat with **ChatGPT**
OR Your **Google Bard API KEY** to chat with **Google Bard**
""")
input_models = gr.Radio(choices=["Custom Large Language Model (LLM)", "OpenAI 'GPT-3.5-Turbo' Model",
"Google Bard Model"], label="Please Select which model you want to use:",
value="Custom Large Language Model (LLM):")
with gr.Row(visible=True) as row:
with gr.Column(visible=True) as col:
with gr.Row(visible=True) as rowA:
input_model1.render()
with gr.Row(visible=False) as rowB:
input_model2.render()
with gr.Row(visible=False) as rowC:
input_model3.render()
chatbot = gr.Chatbot(height=300, label="A chatbot to interact with LLM", avatar_images=((os.path.join(os.path.dirname(__file__), "user.png")), (os.path.join(os.path.dirname(__file__), "bot.png"))))
user_input = gr.Textbox(label="Enter a message:")
clear = gr.ClearButton(value="Clear")
custom_prompt = gr.Textbox(label="Enter a custom prompt to use:", lines=15, max_lines=20, placeholder="In just 150 words summarize this text...")
input_models.change(fn=select_choice, inputs=input_models, outputs=[rowA, rowB, rowC], queue=False)
def user(user_message, chat_history):
return "", chat_history + [[user_message, None]]
def respond(chat_history, custom_prompt, input_model1, input_model2, input_model3):
bot_message = predict(chat_history[-1][0], custom_prompt, input_model1, input_model2, input_model3)
chat_history[-1][1] = bot_message
time.sleep(2)
return chat_history
user_input.submit(user, [user_input, chatbot], [user_input, chatbot], queue=False).then(
respond, [chatbot, custom_prompt, input_model1, input_model2, input_model3], chatbot
)
clear.click(lambda: [None, None, None, None, None, None], outputs=[user_input, chatbot, custom_prompt, input_model1, input_model2, input_model3], queue=False)
demo.queue()
demo.launch() |