import gradio as gr
import logging
import os
import torch
import transformers
from transformers import AutoTokenizer

print(os.environ["HF_TOKEN"][:5])
logging.info(os.environ["HF_TOKEN"][:5])

pipe_flan = transformers.pipeline("text2text-generation", model="google/flan-t5-small")
def google_flan(input_text):
    return pipe_flan(input_text)

demo = gr.Interface(fn=google_flan, inputs="text", outputs="text")

# model = "meta-llama/Llama-2-7b-chat-hf"
# tokenizer = AutoTokenizer.from_pretrained(
#     model,
#     token=os.environ["HF_TOKEN"],
# )
# pipeline = transformers.pipeline(
#     "text-generation",
#     model=model,
#     torch_dtype=torch.float16,
#     device_map="auto",
#     token=os.environ["HF_TOKEN"],
#     low_cpu_mem_usage=True,
# )

# def llama2(input_text):
#     sequences = pipeline(
#         input_text,
#         do_sample=True,
#         top_k=10,
#         num_return_sequences=1,
#         eos_token_id=tokenizer.eos_token_id,
#         max_length=200,
#     )
#     output_text = ""
#     for seq in sequences:
#         output_text += seq["generated_text"] + "\n"
#     return output_text

# demo = gr.Interface(fn=llama2, inputs="text", outputs="text")
    
demo.launch(server_name="0.0.0.0", server_port=7860)