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import gradio as gr
from transformers import pipeline
# Initialize a dictionary of models for each strategy
models = {
"GPT-2": "gpt2",
"ChatGPT": "EleutherAI/gpt-neo-1.3",
"LLaMa": "model_for_LLaMa",
"Vicuna": "model_for_Vicuna",
"Alpaca": "model_for_Alpaca",
"Flan-T5": "model_for_Flan-T5",
}
# Define a function to generate text based on the selected model
def generate_text(input_instruction, selected_model):
if selected_model in models:
model_name = models[selected_model]
pipe = pipeline("text-generation", model=model_name)
generated_text = pipe(input_instruction, max_length=100, do_sample=True)[0]['generated_text']
return generated_text
else:
return "Please select a model for this strategy."
# Define example instructions for testing
instruction_examples = [
("Write a short story about a cat."),
("Explain the concept of artificial intelligence."),
("Compose a poem about nature."),
]
# Create a Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs=gr.Textbox(placeholder="Enter instruction here..."),
outputs=gr.Textbox(),
examples=instruction_examples,
live=True,
title="Text Generation with Dynamic Model Selection",
)
# Additional input section 1 - User input
with gr.Row():
user_input = gr.Textbox(placeholder="Enter your input...")
# Additional input section 2 - Strategy 1
with gr.Row():
strategy1_selector = gr.Dropdown(list(models.keys()), label="Strategy 1 - QA-Based Prompting")
# Additional input section 3 - Strategy 2
with gr.Row():
strategy2_selector = gr.Dropdown(list(models.keys()), label="Strategy 2 - Instruction-Based Prompting")
# Additional input section 4 - Strategy 3
with gr.Row():
strategy3_selector = gr.Dropdown(list(models.keys()), label="Strategy 3 - Structured Prompting")
# Create a callback function for dynamic model selection
def update_model_and_generate_text(input_instruction, selected_model):
if selected_model:
selected_model_name = strategy1_selector.value if selected_model == "Strategy 1 - QA-Based Prompting" else (
strategy2_selector.value if selected_model == "Strategy 2 - Instruction-Based Prompting" else
strategy3_selector.value)
iface.set_function(generate_text, inputs=[user_input, selected_model], outputs="outputs")
return "Selected model: " + selected_model_name
# Add a submit button to trigger dynamic model selection
submit_button = gr.Button("Submit")
# Set the function for the Gradio interface to the update_model_and_generate_text function
iface.fn = update_model_and_generate_text
# Add the submit button to the interface
iface.add(submit_button)
iface.launch()
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