import os
import gradio as gr
from huggingface_hub import login
from transformers import AutoModelForCausalLM, AutoTokenizer

# Fetch API token from environment variable
api_token = os.getenv("Llama_Token")

# Authenticate with Hugging Face
login(api_token)

# Load LLaMA 3.2 model and tokenizer with the API token
model_name = "meta-llama/Llama-3.2-1B"
tokenizer = AutoTokenizer.from_pretrained(model_name, token=api_token)
model = AutoModelForCausalLM.from_pretrained(model_name, token=api_token)

# Define the inference function
def generate_text(prompt, max_length, temperature):
    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.Textbox(label="Enter your prompt", placeholder="Start typing..."),
        gr.Slider(minimum=50, maximum=200, label="Max Length", value=100),
        gr.Slider(minimum=0.1, maximum=1.0, label="Temperature", value=0.7),
    ],
    outputs="text",
    title="LLaMA 3.2 Text Generator",
    description="Enter a prompt to generate text using the LLaMA 3.2 model.",
)

# Launch the Gradio app
iface.launch()