Spaces:
Runtime error
Runtime error
File size: 4,070 Bytes
6972c11 0f1027f 6972c11 cabea5c 6972c11 519a5c7 6972c11 cabea5c 6972c11 cabea5c 6972c11 96e7dc5 fce4fd9 6972c11 b2e33a0 9af93b2 cabea5c 9af93b2 4e70cdb 519a5c7 cabea5c 0f1027f 4e70cdb 0f1027f 6972c11 7ab6e52 6972c11 7ab6e52 6972c11 7ab6e52 6972c11 7ab6e52 6972c11 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
import gradio as gr
import requests
import json
import os
from screenshot import (
before_prompt,
prompt_to_generation,
after_generation,
js_save,
js_load_script,
)
from spaces_info import description, examples, initial_prompt_value
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, set_seed
#API_URL = os.getenv("API_URL")
#HF_API_TOKEN = os.getenv("HF_API_TOKEN")
def inference(input_sentence, max_length, sample_or_greedy, seed=42):
#print("input_sentence", input_sentence)
if sample_or_greedy == "Sample":
parameters = {
"max_new_tokens": max_length,
"top_p": 0.9,
"do_sample": True,
#"seed": seed,
"early_stopping": False,
"length_penalty": 0.0,
"eos_token_id": None,
}
else:
parameters = {
"max_new_tokens": max_length,
"do_sample": False,
#"seed": seed,
"early_stopping": False,
"length_penalty": 0.0,
"eos_token_id": None,
}
payload = {"inputs": input_sentence, "parameters": parameters,"options" : {"use_cache": False} }
model_name = 'bigscience/bloomz-560m'
pipe = pipeline("text-generation",
model = model_name,
tokenizer = model_name,
max_new_tokens = max_length,
do_sample = False,
length_penalty = 0.0,
early_stopping = False,
eos_token_id = None
)
res = pipe(input_sentence)
#data = query(payload)
#if "error" in data:
# return (None, None, f"<span style='color:red'>ERROR: {data['error']} </span>")
#generation = data[0]["generated_text"].split(input_sentence, 1)[1]
generation = res[0]["generated_text"].split(input_sentence, 1)[1]
print(generation)
return (
before_prompt
+ input_sentence
+ prompt_to_generation
+ generation
+ after_generation,
res[0]["generated_text"],
"",
)
#return generation
if __name__ == "__main__":
demo = gr.Blocks()
with demo:
with gr.Row():
gr.Markdown(value=description)
with gr.Row():
with gr.Column():
text = gr.Textbox(
label="Input",
value=" ", # should be set to " " when plugged into a real API
)
tokens = gr.Slider(1, 64, value=32, step=1, label="Tokens to generate")
sampling = gr.Radio(
["Sample", "Greedy"], label="Sample or greedy", value="Sample"
)
'''
sampling2 = gr.Radio(
["Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5"],
value="Sample 1",
label="Sample other generations (only work in 'Sample' mode)",
type="index",
)
'''
with gr.Row():
submit = gr.Button("Submit")
load_image = gr.Button("Generate Image")
with gr.Column():
text_error = gr.Markdown(label="Log information")
text_out = gr.Textbox(label="Output")
display_out = gr.HTML(label="Image")
display_out.set_event_trigger(
"load",
fn=None,
inputs=None,
outputs=None,
no_target=True,
js=js_load_script,
)
with gr.Row():
#gr.Examples(examples=examples, inputs=[text, tokens, sampling, sampling2])
gr.Examples(examples=examples, inputs=[text, tokens, sampling])
submit.click(
inference,
#inputs=[text, tokens, sampling, sampling2],
inputs = [text, tokens, sampling],
outputs=[display_out, text_out, text_error],
)
load_image.click(fn=None, inputs=None, outputs=None, _js=js_save)
demo.launch() |