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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() |