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import gradio as gr
from urllib.parse import urlparse
import requests
import time
import os
from utils.gradio_helpers import parse_outputs, process_outputs
inputs = []
inputs.append(gr.Textbox(
label="Prompt", info='''Prompt'''
))
inputs.append(gr.Number(
label="Seed", info='''Leave blank to randomize the seed''', value=None
))
names = ['prompt', 'seed']
outputs = []
outputs.append(gr.Image())
expected_outputs = len(outputs)
def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)):
headers = {'Content-Type': 'application/json'}
payload = {"input": {}}
base_url = "http://0.0.0.0:7860"
for i, key in enumerate(names):
value = args[i]
if value and (os.path.exists(str(value))):
value = f"{base_url}/file=" + value
if value is not None and value != "":
payload["input"][key] = value
response = requests.post("http://0.0.0.0:5000/predictions", headers=headers, json=payload)
if response.status_code == 201:
follow_up_url = response.json()["urls"]["get"]
response = requests.get(follow_up_url, headers=headers)
while response.json()["status"] != "succeeded":
if response.json()["status"] == "failed":
raise gr.Error("The submission failed!")
response = requests.get(follow_up_url, headers=headers)
time.sleep(1)
if response.status_code == 200:
json_response = response.json()
#If the output component is JSON return the entire output response
if(outputs[0].get_config()["name"] == "json"):
return json_response["output"]
predict_outputs = parse_outputs(json_response["output"])
processed_outputs = process_outputs(predict_outputs)
difference_outputs = expected_outputs - len(processed_outputs)
# If less outputs than expected, hide the extra ones
if difference_outputs > 0:
extra_outputs = [gr.update(visible=False)] * difference_outputs
processed_outputs.extend(extra_outputs)
# If more outputs than expected, cap the outputs to the expected number
elif difference_outputs < 0:
processed_outputs = processed_outputs[:difference_outputs]
return tuple(processed_outputs) if len(processed_outputs) > 1 else processed_outputs[0]
else:
if(response.status_code == 409):
raise gr.Error(f"Sorry, the Cog image is still processing. Try again in a bit.")
raise gr.Error(f"The submission failed! Error: {response.status_code}")
title = "Demo for sdxl-panoramic cog image by lucataco"
model_description = "360 Panorama SDXL image with inpainted wrapping seam"
app = gr.Interface(
fn=predict,
inputs=inputs,
outputs=outputs,
title=title,
description=model_description,
allow_flagging="never",
)
app.launch(share=True)
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