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import gradio as gr | |
from transformers import pipeline | |
path = 'HamadML/bloomz-560m_p' | |
pipe = pipeline('text-generation', model=path, tokenizer=path) | |
def generate_poetry(prompt, top_p, top_k, temperature, max_length): | |
# Add instruction for the model | |
instruction = "Generate poetry based on the given prompt." | |
model_input = generate_prompt(instruction, prompt) | |
# Generate poetry | |
output = pipe(model_input, | |
max_length=max_length, | |
do_sample=True, | |
top_k=top_k, | |
top_p=top_p, | |
temperature=temperature | |
) | |
return output[0]['generated_text'] | |
def generate_prompt(instruction, input=None): | |
if input: | |
return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. | |
### Instruction: | |
{instruction} | |
### Input: | |
{input} | |
### Response:""" | |
else: | |
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. | |
### Instruction: | |
{instruction} | |
### Response:""" | |
# Create a Gradio interface | |
inputs = [ | |
gr.inputs.Textbox(label="Enter a prompt", placeholder="Enter a prompt", lines=3), | |
gr.inputs.Slider(label="Top-p value", minimum=0.0, maximum=1.0, default=0.9, step=0.1), | |
gr.inputs.Slider(label="Top-k value", minimum=1, maximum=1000, default=400, step=1), | |
gr.inputs.Slider(label="Temperature value", minimum=0.0, maximum=1.0, default=0.9, step=0.1), | |
gr.inputs.Slider(label="Max length", minimum=1, maximum=300, default=200, step=1), | |
] | |
outputs = gr.outputs.Textbox(label="Generated Poetry") | |
examples = [ | |
["چرته چې هم د مينې سپکه وشي", 0.9, 400, 0.7, 200] | |
] | |
iface = gr.Interface( | |
fn=generate_poetry, | |
inputs=inputs, | |
outputs=outputs, | |
examples=examples, | |
title="Pashto Poetry Generator", | |
description="Unleash the beauty of Pashto poetry with the power of deep learning", | |
theme="default" | |
) | |
iface.launch() | |