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import gradio as gr
from gpt4all import GPT4All
from urllib.request import urlopen
import json
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.")
def generate_text(input_text, selected_model):
model = GPT4All(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)
with gr.Row():
input_text = gr.Textbox(lines=10, label="Input Text")
output_text = gr.Textbox(lines=10, label="Generated Text")
# 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()
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