import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("TuringsSolutions/Gemma2LegalEdition", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TuringsSolutions/Gemma2LegalEdition", trust_remote_code=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def predict(prompt, temperature, max_tokens):
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=max_tokens,
        temperature=temperature
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Create Gradio interface
iface = gr.Interface(
    fn=predict, 
    inputs=[
        gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
        gr.Slider(minimum=10, maximum=200, value=50, step=10, label="Number of Output Tokens")
    ], 
    outputs="text",
    title="Phi3 Law Case Management Model",
    description="A model to assist with law case management. Adjust the temperature and number of output tokens as needed."
)

# Launch the Gradio app
iface.launch()