import gradio as gr from transformers import pipeline from transformers import BloomTokenizerFast, BloomForCausalLM import re description = """ When in legal doubt, you better call BLOOM! Ask BLOOM any legal question. \n ***Advice here is for informational purposes only and should not be considered final or official legal advice. See a local attorney for the best answer to your questions.*** """ title = "Better Call Bloom!" tokenizer = BloomTokenizerFast.from_pretrained("tomrb/bettercallbloom-3b") model = BloomForCausalLM.from_pretrained("tomrb/bettercallbloom-3b",low_cpu_mem_usage=True) generator = pipeline('text-generation', model=model, tokenizer=tokenizer,do_sample=False) def preprocess(text): #We add 'Question :' and 'Answer #1:' at the start and end of the prompt return "\nQuestion: " + text + "\nAnswer #1:" def generate(text): preprocessed_text = preprocess(text) result = generator(preprocessed_text, max_length=128) output = re.split(r'\nQuestion:|Answer #1:|Answer #|Title:',result[0]['generated_text'])[2] return output examples = [ ["I started a company with a friend. What types of legal documents should we fill in to clarify the ownership of the company?"], ["[CA] I got a parking ticket in Toronto. How can I contest it?"], ] with gr.Blocks() as demo: gr.Markdown("

Better Call Bloom!

") gr.Markdown("""
When in legal doubt, you better call BLOOM! Ask BLOOM any legal question:
""") gr.Markdown("""
***THIS IS NOT LEGAL ADVICE. Advice here is for informational purposes only and should not be considered final or official legal advice. See a local attorney for the best answer to your questions.***
""") input_text = gr.Textbox(label="Input", lines=6) buton = gr.Button("Submit ") output_text = gr.Textbox(lines=6, label="Output") buton.click(generate, inputs=[input_text], outputs=output_text) gr.HTML("""

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""") demo.launch(enable_queue=True, debug=True)