Update app.py

app.py

@@ -8,9 +8,6 @@ from sklearn.metrics.pairwise import cosine_similarity
 import numpy as np
 import pandas as pd
 from transformers import pipeline
-import subprocess
-subprocess.run(["pythom","-m", "spacy","download","en_core_web_sm"])
-import spacy
 
 
 df = pd.read_csv("df_end.csv")
@@ -19,22 +16,9 @@ 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 :
-        df_to_test = df_to_test.loc[df_to_test['locality'] == city]
-        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 = [word for word in query.split() if word in cities][0]
     city = extract_city(query, cities)
     cities = df['locality'].unique()
     if city in cities:
@@ -43,7 +27,6 @@ def filter_and_rank_by_similarity_sentiment_ranking(query, df, model,cities, k):
     else:
         print("No city found.")
 
-
     query_embedding = model.encode(query)
     embeddings_matrix = np.stack(df['embedding'].values)
     similarities = cosine_similarity([query_embedding], embeddings_matrix).flatten()