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| import requests | |
| import base64 | |
| import io | |
| from PIL import Image | |
| import gradio as gr | |
| import json | |
| # API and Schema URLs | |
| API_URL = "http://localhost:5000/predictions" | |
| SCHEMA_URL = "http://localhost:5000/openapi.json" | |
| def fetch_api_spec(url): | |
| response = requests.get(url) | |
| return response.json() | |
| def create_gradio_app_from_api_spec(api_spec): | |
| input_properties = api_spec['components']['schemas']['Input']['properties'] | |
| inputs = [] | |
| for prop, details in input_properties.items(): | |
| if 'enum' in details: | |
| choices = details['enum'] | |
| inputs.append(gr.Dropdown(choices=choices, label=prop, value=details.get('default'))) | |
| elif details['type'] == 'integer': | |
| inputs.append(gr.Number(label=prop, value=details.get('default'), minimum=details.get('minimum'), maximum=details.get('maximum'))) | |
| elif details['type'] == 'number': | |
| inputs.append(gr.Slider(minimum=details.get('minimum'), maximum=details.get('maximum'), value=details.get('default'), label=prop)) | |
| elif details['type'] == 'string' and 'format' in details and details['format'] == 'uri': | |
| inputs.append(gr.Image(label=prop)) | |
| elif details['type'] == 'string': | |
| inputs.append(gr.Textbox(label=prop, value=details.get('default'))) | |
| elif details['type'] == 'boolean': | |
| inputs.append(gr.Checkbox(label=prop, value=details.get('default'))) | |
| def predict_function(**kwargs): | |
| # Adjust the input kwargs for image inputs to convert them to the expected format by the API if needed | |
| payload = { | |
| "input": kwargs | |
| } | |
| 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_spec = api_spec['components']['schemas']['Output'] | |
| if output_spec['items']['type'] == 'string' and output_spec['items']['format'] == 'uri': | |
| outputs = [] | |
| for uri in json_response["output"]: | |
| if uri.startswith("data:image"): | |
| base64_image = uri.split(",")[1] # Strip the prefix part | |
| image_data = base64.b64decode(base64_image) | |
| image_stream = io.BytesIO(image_data) | |
| image = Image.open(image_stream) | |
| outputs.append(image) | |
| else: | |
| outputs.append(uri) | |
| return outputs | |
| else: | |
| return json_response["output"] | |
| iface = gr.Interface(fn=predict_function, inputs=inputs, outputs=gr.outputs.Image(type="pil"), title=api_spec['info']['title']) | |
| return iface | |
| # Fetch API Specification | |
| api_spec = fetch_api_spec(SCHEMA_URL) | |
| # Create and Launch Gradio App | |
| gradio_app = create_gradio_app_from_api_spec(api_spec) | |
| gradio_app.launch() |