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import gradio as gr
from transformers import (
AutoTokenizer,
AutoModel,
AutoModelForSeq2SeqLM,
AutoModelForCausalLM
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
preset_examples = [
('Instruction: given a dialog context, you need to response empathically.',
'', 'Does money buy happiness?', 'Chitchat'),
]
def generate(instruction, knowledge, dialog, top_p, min_length, max_length):
if knowledge != '':
knowledge = '[KNOWLEDGE] ' + knowledge
dialog = ' EOS '.join(dialog)
query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids
outputs = model.generate(input_ids, min_length=int(
min_length), max_length=int(max_length), top_p=top_p, do_sample=True)
output = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(query)
print(output)
return output
def api_call_generation(instruction, knowledge, query, top_p, min_length, max_length):
dialog = [
query
]
response = generate(instruction, knowledge, dialog,
top_p, min_length, max_length)
return response
def change_example(choice):
choice_idx = int(choice.split()[-1]) - 1
instruction, knowledge, query, instruction_type = preset_examples[choice_idx]
return [gr.update(lines=1, visible=True, value=instruction), gr.update(visible=True, value=knowledge), gr.update(lines=1, visible=True, value=query), gr.update(visible=True, value=instruction_type)]
def change_textbox(choice):
if choice == "Chitchat":
return gr.update(lines=1, visible=True, value="Instruction: given a dialog context, you need to response empathically.")
elif choice == "Grounded Response Generation":
return gr.update(lines=1, visible=True, value="Instruction: given a dialog context and related knowledge, you need to response safely based on the knowledge.")
else:
return gr.update(lines=1, visible=True, value="Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge.")
with gr.Blocks() as demo:
gr.Markdown("# The broken God")
gr.Markdown('''All hail Mekhane. Reject flesh. Embrace metal''')
dropdown = gr.Dropdown(
[f"Example {i+1}" for i in range(1)], label='Examples')
radio = gr.Radio(
["Conversational Question Answering", "Chitchat", "Grounded Response Generation"], label="Instruction Type", value='Conversational Question Answering'
)
instruction = gr.Textbox(lines=1, interactive=True, label="Instruction",
value="Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge.")
radio.change(fn=change_textbox, inputs=radio, outputs=instruction)
knowledge = gr.Textbox(lines=6, label="Knowledge")
query = gr.Textbox(lines=1, label="User Query")
dropdown.change(change_example, dropdown, [instruction, knowledge, query, radio])
with gr.Row():
with gr.Column(scale=1):
response = gr.Textbox(label="Response", lines=2)
with gr.Column(scale=1):
top_p = gr.Slider(0, 1, value=0.9, label='top_p')
min_length = gr.Number(8, label='min_length')
max_length = gr.Number(
64, label='max_length (should be larger than min_length)')
greet_btn = gr.Button("Generate")
greet_btn.click(fn=api_call_generation, inputs=[
instruction, knowledge, query, top_p, min_length, max_length], outputs=response)
demo.launch() |