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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()
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