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import gradio as gr
from transformers import pipeline
# Load the TinyLlama model for text generation
pipe = pipeline("text-generation", model="TinyLlama/TinyLlama_v1.1")
# Define the prediction function
def generate_text(prompt, max_length=128, temperature=1.0, top_p=0.95):
# You can expose more parameters as needed
result = pipe(
prompt,
max_length=max_length,
temperature=temperature,
top_p=top_p,
num_return_sequences=1,
do_sample=True
)
# The output is a list of dicts with 'generated_text'
return result[0]['generated_text']
# Create the Gradio interface
demo = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(lines=4, label="Input Prompt"),
gr.Slider(32, 512, value=128, step=8, label="Max Length"),
gr.Slider(0.1, 2.0, value=1.0, step=0.05, label="Temperature"),
gr.Slider(0.5, 1.0, value=0.95, step=0.01, label="Top-p (nucleus sampling)")
],
outputs=gr.Textbox(lines=8, label="Generated Text"),
title="TinyLlama Text Generation",
description="Enter a prompt and generate text using TinyLlama/TinyLlama_v1.1."
)
# Launch the app
demo.launch()
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