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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load LLaMA 3.2 model and tokenizer
model_name = "meta-llama/LLaMA-3.2" # Replace this with the correct model ID if necessary
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Define the inference function
def generate_text(prompt, max_length=100, temperature=0.7):
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(inputs['input_ids'], max_length=max_length, temperature=temperature)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Create the Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs=[
gr.inputs.Textbox(label="Enter your prompt", placeholder="Start typing..."),
gr.inputs.Slider(minimum=50, maximum=200, default=100, label="Max Length"),
gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.7, label="Temperature"),
],
outputs="text",
title="LLaMA 3.2 Text Generator",
description="Enter a prompt to generate text using the LLaMA 3.2 model.",
theme="compact",
)
# Launch the Gradio app
iface.launch()
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