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# -*- coding: utf-8 -*- | |
"""gradio_app_0 | |
Automatically generated by Colaboratory. | |
Original file is located at | |
https://colab.research.google.com/drive/lalalalalal_HAHAHHAH!@#$%^& | |
""" | |
from EMO_AI.all import * | |
""" | |
# should add kwargs in get_model(), time to update our lib | |
from pathlib import Path | |
# return a dict of options | |
def load_weight(f="emo_0.pt"): | |
p = Path(f) | |
if p.is_file(): | |
return {"PATH": f, "inference_only": False} | |
return {"pretrained":False} | |
""" | |
model = get_model("emo_0.pt", inference_only=False) | |
# stable: model = get_model(pretrained=False) | |
import gradio as gr | |
"""# TODO | |
## model | |
### put our model (and pretrained weight) to huggingface space | |
## App | |
### store users result there (and pass to firebase?) | |
### enable to send request from the mobile to space, and send result back from space to mobile | |
### use socket? | |
""" | |
"for creating filename that won't collide as much" | |
import hashlib | |
from pathlib import Path | |
"add write data function" | |
def get_filename(text): | |
return hashlib.md5(text.encode("utf-8")).hexdigest() | |
def write_result(text, content): | |
# decide the filename: use hash function? | |
# write up the result | |
filename = Path(get_filename(text)) | |
if not filename.is_file(): | |
filename.write_text(content) | |
# return the file name (try to integrate with socket?) | |
return filename | |
def fn2(text, model=model): | |
out = get_output(text,model) | |
filename = write_result(text, out) | |
return out | |
interface = gr.Interface( | |
fn = fn2, | |
inputs="text", | |
outputs="text" | |
) | |
interface.launch() | |