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import gradio as gr
from transformers import AutoTokenizer
import re
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
config = PeftConfig.from_pretrained("mohamedemam/Arabic-meeting-summarization")
model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-3b")
model = PeftModel.from_pretrained(model, "mohamedemam/Arabic-meeting-summarization")
# Load the tokenizer and model
model_name ="bigscience/bloomz-3b"
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-3b")
model.eval()
import wikipediaapi
# Create a Wikipedia API instance
wiki_wiki = wikipediaapi.Wikipedia('MyProjectName ([email protected])', 'en')
page_py = wiki_wiki.page('Leo messi')
example_contexts=page_py.text.split(f"\n")
for i in range(len(example_contexts)):
example_contexts[i]=re.sub(f'\n'," ", example_contexts[i])
# Recommended words for users to choose from
# Function to generate questions and answers with configurable parameters
def generate_qa(context, temperature, top_p,num_seq,l_p, num_b):
input_text = context
input_ids = tokenizer(input_text, return_tensors='pt')
# Generate with configurable parameters
output = model.generate(
**input_ids,
temperature=temperature,
top_p=top_p,
num_return_sequences=num_seq,
max_length=100,
num_beams=num_b,
length_penalty=l_p,
do_sample=True,
)
#
generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)
formatted_output = "\n\n".join(set(generated_text))
return formatted_output
iface = gr.Interface(
fn=generate_qa,
inputs=[
gr.inputs.Dropdown(example_contexts, label="Choose an Example"),
gr.inputs.Slider(minimum=0.0, maximum=5, default=2.1, step=0.01, label="Temperature"),
gr.inputs.Slider(minimum=0.0, maximum=1, default=0.5, step=0.01, label="Top-p"),
gr.inputs.Slider(minimum=1, maximum=20, default=3, step=1, label="num of sequance"),
gr.inputs.Slider(minimum=0.01, maximum=5, default=3, step=.01, label="l_p")
,
gr.inputs.Slider(minimum=1, maximum=20, default=3, step=1, label="num of beams"),
],
outputs=gr.outputs.Textbox(label="Generated Output"),
title="Question Generation and Answering",
description="Select an example context, choose a recommended word, adjust temperature and top-p. The model will generate questions and answers.",
)
# Launch the interface
iface.launch() |