app.py

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()