import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x22B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-v0.1", device_map="auto")

# Function to generate text using the model
def generate_text(prompt, max_length=500, temperature=0.7, top_k=50, top_p=0.95, num_return_sequences=1):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    output = model.generate(
        input_ids,
        max_length=max_length,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        num_return_sequences=num_return_sequences,
    )
    generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
    return generated_text

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.inputs.Textbox(lines=5, label="Input Prompt"),
        gr.inputs.Slider(minimum=100, maximum=1000, default=500, step=50, label="Max Length"),
        gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.7, step=0.1, label="Temperature"),
        gr.inputs.Slider(minimum=1, maximum=100, default=50, step=1, label="Top K"),
        gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.95, step=0.05, label="Top P"),
        gr.inputs.Slider(minimum=1, maximum=10, default=1, step=1, label="Num Return Sequences"),
    ],
    outputs=gr.outputs.Textbox(label="Generated Text"),
    title="MixTRAL 8x22B Text Generation",
    description="Use this interface to generate text using the MixTRAL 8x22B language model.",
)

# Launch the Gradio interface
iface.launch()