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