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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): | |
| # Initialize a dictionary to hold the combined properties | |
| combined_prop = {} | |
| # Identify the keywords to process. Extend this list if needed. | |
| merge_keywords = ["allOf", "anyOf", "oneOf"] | |
| # Loop through each keyword to check if it exists in the prop | |
| for keyword in merge_keywords: | |
| if keyword in prop: | |
| for subprop in prop[keyword]: | |
| combined_prop.update(subprop) | |
| # After merging, remove the keyword to avoid confusion | |
| del prop[keyword] | |
| # If no merge_keywords were found, copy the original property | |
| if not combined_prop: | |
| combined_prop = prop.copy() | |
| # Preserve specific properties defined outside of merge_keywords, | |
| # like 'description' and 'default', by updating them in combined_prop | |
| for key in ['description', 'default']: | |
| if key in prop: | |
| combined_prop[key] = prop[key] | |
| # This returns the combined property with preserved 'description' and 'default' values. | |
| return combined_prop | |
| def create_gradio_app(api_spec, api_url): | |
| inputs = [] | |
| input_schema = api_spec["components"]["schemas"]["Input"]["properties"] | |
| for name, prop in input_schema.items(): | |
| #print(prop) | |
| prop = extract_property_info( | |
| prop | |
| ) # Extract property info correctly for 'allOf' | |
| print(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: # Assuming string type for simplicity, can add more types as needed | |
| input_field = gr.Textbox( | |
| label=prop.get("title"), | |
| info=prop.get("description"), | |
| ) | |
| inputs.append(input_field) | |
| def predict(**kwargs): | |
| payload = {"input": {}} | |
| for key, value in kwargs.items(): | |
| if isinstance( | |
| value, io.BytesIO | |
| ): # For image inputs, convert to the desired format | |
| value.seek(0) | |
| value = ( | |
| "data:image/jpeg;base64," + base64.b64encode(value.read()).decode() | |
| ) | |
| payload["input"][key] = value | |
| 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 | |
| output_component = gr.Gallery(label="Output Images") | |
| return gr.Interface(fn=predict, inputs=inputs, outputs=output_component) | |
| API_URL = "http://localhost:5000/predictions" | |
| app = create_gradio_app(api_spec, API_URL) | |
| app.launch() |