evazion
07b84d73fe
Do batch prediction inside the `predict` method instead of calling `predict` once for each image.
46 lines
1.4 KiB
Python
Executable File
46 lines
1.4 KiB
Python
Executable File
#!/usr/bin/env python
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from os import getenv
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from dotenv import load_dotenv
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from autotagger import Autotagger
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from base64 import b64encode
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from flask import Flask, request, render_template, jsonify
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from fastai.vision.core import PILImage
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load_dotenv()
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model_path = getenv("MODEL_PATH", "models/model.pth")
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autotagger = Autotagger(model_path)
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app = Flask(__name__)
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app.config["JSON_SORT_KEYS"] = False
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app.config["JSON_PRETTYPRINT_REGULAR"] = True
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@app.route("/", methods=["GET"])
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def index():
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return render_template("index.html")
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@app.route("/evaluate", methods=["POST"])
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def evaluate():
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files = request.files.getlist("file")
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images = [PILImage.create(file) for file in files]
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threshold = float(request.form.get("threshold", 0.1))
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output = request.form.get("format", "html")
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limit = int(request.form.get("limit", 50))
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predictions = autotagger.predict(images, threshold=threshold, limit=limit)
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if output == "html":
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for file in files:
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file.seek(0)
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base64data = [b64encode(file.read()).decode() for file in files]
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return render_template("evaluate.html", predictions=zip(base64data, predictions))
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elif output == "json":
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predictions = [{ "filename": file.filename, "tags": tags } for file, tags in zip(files, predictions)]
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return jsonify(predictions)
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else:
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return 400
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if __name__ == "__main__":
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app.run(host="0.0.0.0")
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