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

hf_token = os.environ.get('HF_TOKEN')

from huggingface_hub import login
login(token=hf_token)

config = AutoConfig.from_pretrained( "mistralai/Mistral-7B-Instruct-v0.1")
config.config.max_new_tokens = 2000
config.config.context_length = 4000                                                    )

model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-Instruct-v0.1",
    token = hf_token,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
    config=config)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1", token = hf_token)

def generate_text(input_text):
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    attention_mask = torch.ones(input_ids.shape)

    output = model.generate(
        input_ids,
        attention_mask=attention_mask,
        max_length=200,
        do_sample=True,
        top_k=10,
        num_return_sequences=1,
        eos_token_id=tokenizer.eos_token_id,
        
    )

    output_text = tokenizer.decode(output[0], skip_special_tokens=True)
    print(output_text)

    # Remove Prompt Echo from Generated Text
    cleaned_output_text = output_text.replace(input_text, "")
    return cleaned_output_text


text_generation_interface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.inputs.Textbox(label="Input Text"),
    ],
    outputs=gr.inputs.Textbox(label="Generated Text")).launch()