Update app.py

app.py

@@ -1,7 +1,7 @@
-from fastapi import FastAPI, Query
+import spacy
+from fastapi import FastAPI
 from pydantic import BaseModel
 from typing import Optional, Dict, Any
-import spacy
 
 # Load spaCy language model
 nlp = spacy.load("en_core_web_sm")
@@ -29,6 +29,8 @@ KEYWORD_MAPPINGS = {
         "popular": {"popular": True},
         "trending": {"trending": True},
         "near me": {"radius": 500},
+        "around me": {"radius": 500},
+        "nearby": {"radius": 500},
     },
 }
 
@@ -38,7 +40,6 @@ class QueryRequest(BaseModel):
     latitude: Optional[float] = None
     longitude: Optional[float] = None
 
-# Function to parse the sentence using spaCy
 def parse_sentence_to_query_ner(sentence: str, lat: Optional[float] = None, lng: Optional[float] = None) -> Dict[str, Any]:
     """
     Parse a sentence using spaCy NER and build a search query.
@@ -58,6 +59,7 @@ def parse_sentence_to_query_ner(sentence: str, lat: Optional[float] = None, lng:
         "longitude": lng,
         "radius": None,
         "merchant_category": None,
+        "business_name": None,
         "top_rated": False,
         "popular": False,
         "trending": False,
@@ -78,8 +80,14 @@ def parse_sentence_to_query_ner(sentence: str, lat: Optional[float] = None, lng:
         if phrase in sentence.lower():
             query.update(filter_dict)
 
+    # Extract potential business names using NER
+    for ent in doc.ents:
+        if ent.label_ in ["ORG", "PERSON", "GPE"]:  # Relevant entity types
+            query["business_name"] = ent.text
+            break
+
     # Use NER to extract location context
-    if "near me" in sentence.lower() or "around me" in sentence.lower():
+    if "near me" in sentence.lower() or "around me" in sentence.lower() or "nearby" in sentence.lower():
         if lat is not None and lng is not None:
             query["radius"] = 500  # Default radius for "near me"