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

# Get the Hugging Face token from the environment variable
HF_TOKEN = os.environ.get("HF_TOKEN")

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('EleutherAI/GPT-NeoX-20B', use_auth_token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained('skylersterling/SentimentGPT', use_auth_token=HF_TOKEN)
model.eval()
model.to('cpu')

# Define the function that generates text from a prompt
def generate_text(prompt, temperature=0.1, top_p=0.2, max_tokens=3):
    prompt_with_eos = prompt + " >"  # Add the "EOS" to the end of the prompt
    input_tokens = tokenizer.encode(prompt_with_eos, return_tensors='pt')

    print(prompt_with_eos)

    input_tokens = input_tokens.to('cpu')

    generated_text = prompt_with_eos  # Start with the initial prompt plus "EOS"
    prompt_length = len(generated_text)

    for _ in range(max_tokens):  # Adjust the range to control the number of tokens generated
        with torch.no_grad():
            outputs = model(input_tokens)
            predictions = outputs.logits[:, -1, :] / temperature
            sorted_logits, sorted_indices = torch.sort(predictions, descending=True)
            cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
            sorted_indices_to_remove = cumulative_probs > top_p
            sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
            sorted_indices_to_remove[..., 0] = 0
            indices_to_remove = sorted_indices[sorted_indices_to_remove]
            predictions[:, indices_to_remove] = -float('Inf')
            next_token = torch.multinomial(torch.softmax(predictions, dim=-1), 1)
        
        input_tokens = torch.cat((input_tokens, next_token), dim=1)
        
        decoded_token = tokenizer.decode(next_token.item())
        generated_text += decoded_token  # Append the new token to the generated text
        if decoded_token == "<":  # Stop if the end of sequence token is generated
            break
    
    # Check if the generated text contains "0" or "1" and return the appropriate sentiment message
    if "1" in generated_text:
        return "The sentiment is positive."
    elif "0" in generated_text:
        return "The sentiment is negative."
    else:
        return "Invalid"

# Create a Gradio interface with a text input, sliders for temperature and top_p, and a text output
interface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(lines=2, placeholder="Enter your prompt here...")
    ],
    outputs=gr.Textbox(),
    live=False,
    description="SentimentGPT processes the sequence and returns a reasonably accurate guess of whether the sentiment behind the input is positive or negative."
)

interface.launch()