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import sys |
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sys.path.append("./src") |
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from kidney_classification.pipeline.prediction import PredictionPipeline |
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from kidney_classification.utils.common import decodeImage |
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from flask_cors import CORS, cross_origin |
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import os |
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from flask import Flask, request, jsonify, render_template |
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os.putenv("LANG", "en_US.UTF-8") |
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os.putenv("LC_ALL", "en_US.UTF-8") |
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app = Flask(__name__) |
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CORS(app) |
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class ClientApp: |
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def __init__(self): |
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self.filename = "inputImage.jpg" |
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self.classifier = PredictionPipeline(self.filename) |
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@app.route("/", methods=["GET"]) |
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@cross_origin() |
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def home(): |
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return render_template("index.html") |
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@app.route("/train", methods=["GET", "POST"]) |
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@cross_origin() |
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def trainRoute(): |
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os.system("dvc repro") |
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return "Training done successfully!" |
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@app.route("/predict", methods=["POST"]) |
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@cross_origin() |
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def predictRoute(): |
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image = request.json["image"] |
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decodeImage(image, clApp.filename) |
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result = clApp.classifier.predict() |
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return jsonify(result) |
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if __name__ == "__main__": |
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clApp = ClientApp() |
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app.run(host="0.0.0.0", port=8080) |
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