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import gradio as gr | |
from transformers import pipeline | |
# Load the Meta Llma 3.1 Instruct model for the initial argument | |
model1 = gr.load("models/microsoft/GRIN-MoE") | |
# Load a different model for the counter-argument | |
# We'll use GRIN as an example, but you can replace this with another suitable model | |
model2 = gr.load("models/microsoft/GRIN-MoE") | |
def generate_initial_argument(query): | |
prompt = f"Provide a logical explanation for the following topic: {query}" | |
response = model1(prompt) | |
return response | |
def generate_counter_argument(query, initial_argument): | |
prompt = f"Given the topic '{query}' and the argument '{initial_argument}', provide a well-reasoned counter-argument:" | |
response = model2(prompt, max_length=200, num_return_sequences=1, temperature=0.7)[0]['generated_text'] | |
# Extract the counter-argument from the generated text | |
counter_argument = response.split(prompt)[-1].strip() | |
return counter_argument | |
def debate(query): | |
initial_argument = generate_initial_argument(query) | |
counter_argument = generate_counter_argument(query, initial_argument) | |
return initial_argument, counter_argument | |
# Define the Gradio interface | |
iface = gr.Interface( | |
fn=debate, | |
inputs=gr.Textbox(lines=2, placeholder="Enter your question or topic for debate here..."), | |
outputs=[ | |
gr.Textbox(label="Initial Argument (Meta Llma 3.1 Instruct)"), | |
gr.Textbox(label="Counter-Argument (GRIN-MoE)") | |
], | |
title="Two-Model Debate System", | |
description="Enter a question or topic. Meta Llma 3.1 Instruct will provide an initial argument, and GRIN-MoE will generate a counter-argument.", | |
examples=[ | |
["What are the long-term implications of artificial intelligence on employment?"], | |
["Should governments prioritize space exploration or addressing climate change?"], | |
["Is genetic engineering in humans ethical for disease prevention?"] | |
] | |
) | |
# Launch the interface | |
iface.launch() |