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| from fastapi import FastAPI | |
| from fastapi.responses import HTMLResponse | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from fastbook import * | |
| from pydantic import BaseModel, Field | |
| from pathlib import * | |
| from pathlib import Path | |
| import urllib.request | |
| import base64 | |
| from src.modelsetup import setup_model | |
| app = FastAPI() | |
| ## Full documentation on https://thiagoh-simple-predictor.hf.space/docs | |
| ## Usage | |
| # curl -X GET 'https://thiagoh-simple-predictor.hf.space/?name=uia' -H "Content-Type: application/json" | |
| origins = ["http://thiagoh.github.io", "https://thiagoh.github.io"] | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=origins, | |
| allow_origin_regex="https?://thiagoh\.github\.io(/?.*)?", | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| fingers_model, bear_model = setup_model() | |
| def read_root(): | |
| routes = app.routes | |
| print(f'Routes: {routes}') | |
| content = f""" | |
| <html> | |
| <head> | |
| <title>Some HTML in here</title> | |
| </head> | |
| <body> | |
| <h1>Here's the available routes in this app</h1> | |
| <ul>{''.join(map(lambda e: f'<li><a href="https://thiagoh-simple-predictor.hf.space{e.path}">{e.path}</a></li>', routes))}</ul> | |
| <h3>Navigation details</h3> | |
| <p>Navigate to <a href="https://thiagoh-simple-predictor.hf.space">https://thiagoh-simple-predictor.hf.space</a> to hit the application. You can also <code>curl</code> it</p> | |
| </body> | |
| </html>""" | |
| return HTMLResponse(content=content, status_code=200) | |
| class Prediction(BaseModel): | |
| prediction: str | |
| probability: float | |
| imageEncodedBytes: Union[str, None] = None | |
| class InputItem(BaseModel): | |
| imageUrl: Union[str, None] = Field(default=None, title="Url of an image", max_length=2400) | |
| imageEncodedBytes: Union[str, None] = None | |
| def predict_finger(item: InputItem): | |
| if item.imageUrl: | |
| with urllib.request.urlopen(item.imageUrl) as f: | |
| decoded_bytes = f.read() | |
| encoded_bytes = base64.b64encode(decoded_bytes) | |
| else: | |
| decoded_bytes = base64.b64decode(item.imageEncodedBytes) | |
| encoded_bytes = item.imageEncodedBytes | |
| prediction, prediction_idx, probabilities = fingers_model.predict(PILImage.create(decoded_bytes)) | |
| probability: int = probabilities[prediction_idx] | |
| print(f'Prediction: {prediction}; Probability: {probability:.04f}') | |
| return Prediction(prediction=prediction, probability=probability, imageEncodedBytes=encoded_bytes) | |