Create app.py

app.py

@@ -0,0 +1,54 @@
+from flask import Flask, request, render_template
+from transformers import pipeline
+import re
+
+app = Flask(__name__)
+
+# Load the NER pipeline
+ner_pipeline = pipeline(
+    "ner",
+    model="VoiceLab/bert-base-uncased-finetuned-ner-fashion",
+    aggregation_strategy="simple"
+)
+
+def extract_entities(query):
+    # Get entities from model
+    entities = ner_pipeline(query)
+    
+    # Initialize result dictionary
+    result = {
+        "Brand": None,
+        "Category": None,
+        "Gender": None,
+        "Price": None
+    }
+    
+    # Process entities
+    for entity in entities:
+        label = entity["entity_group"]
+        value = entity["word"].strip()
+        
+        if label == "Brand" and not result["Brand"]:
+            result["Brand"] = value
+        elif label == "Product Type" and not result["Category"]:
+            result["Category"] = value
+        elif label == "Gender" and not result["Gender"]:
+            result["Gender"] = value
+    
+    # Extract price using regex (since model doesn't handle prices)
+    price_match = re.search(r"under (\d+)\s*AED", query, re.IGNORECASE)
+    if price_match:
+        result["Price"] = f"Under {price_match.group(1)} AED"
+    
+    return result
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    if request.method == "POST":
+        query = request.form["query"]
+        result = extract_entities(query)
+        return render_template("index.html", result=result, query=query)
+    return render_template("index.html", result=None)
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000, debug=True)