import gradio as gr

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch


def falcon(input_text):
    model = "tiiuae/falcon-40b"

    tokenizer = AutoTokenizer.from_pretrained(model)
    pipeline = transformers.pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        torch_dtype=torch.bfloat16,
        trust_remote_code=True,
        device_map="auto",
    )
    sequences = pipeline(
        input_text, # "Was ist das höchste Gebäude in der Welt?"
        max_length=200,
        do_sample=True,
        top_k=10,
        num_return_sequences=1,
        eos_token_id=tokenizer.eos_token_id,
    )
    for seq in sequences:
        print(f"Result: {seq['generated_text']}")

    return sequences[0]['generated_text']

iface = gr.Interface(fn=falcon, inputs="text", outputs="text")
iface.launch() # To create a public link, set `share=True`