Update app.py

app.py

@@ -12,7 +12,7 @@ import pickle
 df = None
 
 # Open the file in binary mode 
-with open('df.pkl', 'rb') as file: 
+with open('df_classifcation.pkl', 'rb') as file: 
       
     # Call load method to deserialze 
     df = pickle.load(file)
@@ -22,11 +22,33 @@ model = SentenceTransformer('all-MiniLM-L6-v2')
 cities = df['locality'].unique()
 
 
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+cities = df['locality'].unique()
+def extract_city(query,cities):
+    city = None
+    doc = nlp(query)
+    for ent in doc.ents:
+        if ent.label_ == "GPE":  # Geo-Political Entity
+            # Assuming the entity is a city
+            return ent.text
+    print(cities)
+    if city in cities:
+        
+        print(f"City found: {city}")
+    else:
+        print("No city found.")
+    return city
+
+    
+
+
 
 def filter_and_rank_by_similarity_sentiment_ranking(query, df, model,cities, k):
     city=None
     try:
-        city = [word for word in query.split() if word in cities][0]
+        city = extract_city(qurey,cities)
     except:
         pass
     cities = df['locality'].unique()