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File size: 1,918 Bytes
386f58c 31b662b 386f58c 31b662b f291f90 386f58c 31b662b 386f58c 17a5be5 386f58c 4a6f6f4 386f58c 91e3338 386f58c 91e3338 386f58c 31b662b 9af1d81 31b662b 77d00be | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | from visual_product_search.pipeline.training_pipeline import VisualProductPipeline
from visual_product_search.pipeline.prediction_pipeline import ProductPredictionPipeline
from visual_product_search.logger import logging
from visual_product_search.exception import ExceptionHandle
from flask import Flask, render_template, request
import sys
app = Flask(__name__)
predictionPipeline = ProductPredictionPipeline()
@app.route("/", methods=["GET"])
def home():
return render_template("home.html")
@app.route("/train", methods=["GET"])
def train_page():
return render_template("train.html")
@app.route("/train_model", methods=["GET"])
def model_train():
try:
pipeline = VisualProductPipeline()
pipeline.run_pipeline()
return "Training completed successfully"
except Exception as e:
logging.critical(f"Pipeline failed: {e}")
raise ExceptionHandle(e, sys)
@app.route("/predict", methods=["POST"])
def predict():
try:
k = int(request.form.get("k", 5))
if request.form.get("text_field"):
query = request.form["text_field"]
outputs = predictionPipeline.search_with_text(query, k)
results = [item.entity['image_link'] for item in outputs[0]]
return render_template("home.html", results=results)
elif "img_field" in request.files:
img_file = request.files["img_field"]
outputs = predictionPipeline.search_with_image(img_file, k)
results = [item.entity['image_link'] for item in outputs[0]]
return render_template("home.html", results=results)
else:
return render_template("home.html", result="No input provided")
except Exception as e:
logging.critical(f"Prediction Failed: {e}")
raise ExceptionHandle(e, sys)
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860) |