| import gradio as gr | |
| from gpt4all import GPT4All | |
| from urllib.request import urlopen | |
| import json | |
| import time | |
| url = "https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models3.json" | |
| response = urlopen(url) | |
| data_json = json.loads(response.read()) | |
| def model_choices(): | |
| model_list = [data_json[i]['filename'] for i in range(len(data_json))] | |
| return model_list | |
| model_description = {model['filename']: model['description'] for model in data_json} | |
| def llm_intro(selected_model): | |
| return model_description.get(selected_model, "No description available for this model selection.") | |
| model_cache = {} # Global cache | |
| def load_model(model_name): | |
| """ | |
| This function checks the cache before loading a model. | |
| If the model is cached, it returns the cached version. | |
| Otherwise, it loads the model, caches it, and then returns it. | |
| """ | |
| if model_name not in model_cache: | |
| # Load the model. This is a placeholder; replace with actual model loading logic. | |
| model = GPT4All(model_name) # Placeholder function | |
| model_cache[model_name] = model | |
| return model_cache[model_name] | |
| def generate_text(input_text, selected_model): | |
| """ | |
| Generate text using the selected model. | |
| This function now uses the caching mechanism to load models. | |
| """ | |
| model = load_model_with_cache(selected_model) | |
| output = model.generate(input_text, max_tokens=100) | |
| return output | |
| # model_dropdown = gr.Dropdown(choices=model_choices(), | |
| # multiselect=False, | |
| # label="LLMs to choose from", | |
| # type="value", | |
| # value="orca-mini-3b-gguf2-q4_0.gguf") | |
| # explanation = gr.Textbox(label="Model Description", interactive=True, lines=10) | |
| # model_dropdown.change(fn=llm_intro, inputs=model_dropdown, outputs=explanation) | |
| # input_text = gr.Textbox(lines=5, label="Input Text") | |
| # output_text = gr.Textbox(lines=5, label="Generated Text") | |
| # gr.Interface(fn=generate_text, | |
| # inputs=[input_text, model_dropdown], | |
| # outputs=output_text, | |
| # theme = gr.themes.Soft(), | |
| # analytics_enabled=True, | |
| # title="GPT4All Text Generation Experiment").launch() | |
| # with gr.Blocks() as demo: | |
| # gr.Markdown("## GPT4All Text Generation Experiment") | |
| # with gr.Row(): | |
| # model_selection = gr.Dropdown(choices=model_choices(), | |
| # multiselect=False, | |
| # label="LLMs to choose from", | |
| # type="value", | |
| # value="orca-mini-3b-gguf2-q4_0.gguf") | |
| # explanation = gr.Textbox(label="Model Description", lines=3, interactive=False, value=llm_intro("orca-mini-3b-gguf2-q4_0.gguf")) | |
| # # Link the dropdown with the textbox to update the description based on the selected model | |
| # model_selection.change(fn=llm_intro, inputs=model_selection, outputs=explanation) | |
| # chatbot = gr.Chatbot() | |
| # input_text = gr.Textbox(lines=3, label="Press shift+Enter to submit") | |
| # # output_text = gr.Textbox(lines=10, label="Generated Text") | |
| # clear = gr.ClearButton([input_text, chatbot]) | |
| # def respond(message, chat_history, selected_model): | |
| # bot_message = generate_text(message, selected_model) | |
| # chat_history.append((message, bot_message)) | |
| # time.sleep(2) # Simulating processing delay if necessary | |
| # return bot_message, chat_history | |
| # input_text.submit(respond, [input_text, chatbot, model_selection], [chatbot]) | |
| # # # Button to generate text | |
| # # generate_btn = gr.Button("Generate") | |
| # # generate_btn.click(fn=generate_text, inputs=[input_text, model_selection], outputs=output_text) | |
| # demo.launch() | |
| # Define the chatbot function | |
| def chatbot(model_name, message, chat_history): | |
| model = load_model(model_name) | |
| response = model.generate(message, chat_history) | |
| chat_history.append((message, response)) | |
| return chat_history, response | |
| # Create the Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# GPT4All Chatbot") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| model_dropdown = gr.Dropdown( | |
| choices=model_choices(), | |
| multiselect=False, | |
| type="value", | |
| value="orca-mini-3b-gguf2-q4_0.gguf", | |
| label="LLMs to choose from" | |
| ) | |
| with gr.Column(scale=4): | |
| chatbot = gr.Chatbot(label="Conversation") | |
| message = gr.Textbox(label="Message") | |
| state = gr.State() | |
| message.submit(chatbot, inputs=[model_dropdown, message, state], outputs=[chatbot, state]) | |
| # Launch the Gradio app | |
| demo.launch() |