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| import gradio as gr | |
| import requests | |
| import json | |
| import io | |
| import os | |
| from PIL import Image | |
| import base64 | |
| from prance import ResolvingParser | |
| SCHEMA_URL = "http://localhost:5000/openapi.json" | |
| FILENAME = "openapi.json" | |
| schema_response = requests.get(SCHEMA_URL) | |
| openapi_spec = schema_response | |
| r = requests.get(SCHEMA_URL) | |
| print(r.content) | |
| with open(FILENAME, "wb") as f: | |
| f.write(r.content) | |
| parser = ResolvingParser(FILENAME) | |
| api_spec = parser.specification | |
| print(parser.specification) | |
| def extract_property_info(prop): | |
| combined_prop = {} | |
| merge_keywords = ["allOf", "anyOf", "oneOf"] | |
| for keyword in merge_keywords: | |
| if keyword in prop: | |
| for subprop in prop[keyword]: | |
| combined_prop.update(subprop) | |
| del prop[keyword] | |
| if not combined_prop: | |
| combined_prop = prop.copy() | |
| for key in ['description', 'default']: | |
| if key in prop: | |
| combined_prop[key] = prop[key] | |
| return combined_prop | |
| def sort_properties_by_order(properties): | |
| ordered_properties = sorted(properties.items(), key=lambda x: x[1].get('x-order', float('inf'))) | |
| return ordered_properties | |
| def create_gradio_app(api_spec, api_url): | |
| inputs = [] | |
| outputs = [] | |
| input_schema = api_spec["components"]["schemas"]["Input"]["properties"] | |
| output_schema = api_spec["components"]["schemas"]["Output"] | |
| ordered_input_schema = sort_properties_by_order(input_schema) | |
| names = [] | |
| for name, prop in ordered_input_schema: | |
| prop = extract_property_info(prop) | |
| if "enum" in prop: | |
| input_field = gr.Dropdown( | |
| choices=prop["enum"], label=prop.get("title"), info=prop.get("description"), value=prop.get("default") | |
| ) | |
| elif prop["type"] == "integer": | |
| input_field = gr.Slider( | |
| label=prop.get("title"), info=prop.get("description"), value=prop.get("default"), | |
| minimum=prop.get("minimum"), maximum=prop.get("maximum"), step=1, | |
| ) | |
| elif prop["type"] == "number": | |
| input_field = gr.Slider( | |
| label=prop.get("title"), info=prop.get("description"), value=prop.get("default"), | |
| minimum=prop.get("minimum"), maximum=prop.get("maximum"), | |
| ) | |
| elif prop["type"] == "boolean": | |
| input_field = gr.Checkbox(label=prop.get("title"), info=prop.get("description"), value=prop.get("default")) | |
| elif prop["type"] == "string" and prop.get("format") == "uri": | |
| input_field = gr.File(label=prop.get("title")) | |
| else: | |
| input_field = gr.Textbox(label=prop.get("title"), info=prop.get("description")) | |
| inputs.append(input_field) | |
| names.append(name) | |
| print(names) | |
| data_field = gr.State(value=names) | |
| inputs.append(data_field) | |
| if output_schema["type"] == "string": | |
| output_component = gr.Gallery(label="Output Image") | |
| outputs.append(output_component) | |
| outputs.append(data_field) | |
| #else if there's multiple outputs | |
| def predict(*args): | |
| print(args) | |
| keys = args[-1] | |
| payload = {"input": {}} | |
| for i, key in enumerate(keys): | |
| value = args[i] | |
| if(os.path.exists(value)): | |
| value = "http://localhost:7860/file=" + value | |
| payload["input"][key] = value | |
| print(payload) | |
| response = requests.post(api_url, headers={"Content-Type": "application/json"}, json=payload) | |
| json_response = response.json() | |
| if "status" in json_response and json_response["status"] == "failed": | |
| raise gr.Error("Failed to generate image") | |
| output_images = [] | |
| for output_uri in json_response["output"]: | |
| base64_image = output_uri.replace("data:image/png;base64,", "") | |
| image_data = base64.b64decode(base64_image) | |
| image_stream = io.BytesIO(image_data) | |
| output_images.append(Image.open(image_stream)) | |
| return output_images | |
| return gr.Interface(fn=predict, inputs=inputs, outputs=outputs if outputs else "label") | |
| API_URL = "http://localhost:5000/predictions" | |
| app = create_gradio_app(api_spec, API_URL) | |
| app.launch(share=True) |