adding new files

app.py

@@ -5,14 +5,14 @@ from sklearn.neighbors import NearestNeighbors
 import gradio as gr
 
 # Load the embeddings from the file
-with open('embeddings.pkl', 'rb') as f:
+with open('embeddings_quran.pkl', 'rb') as f:
     embeddings = pickle.load(f)
 
 # Initialize the Nearest Neighbors model with cosine similarity
 nbrs = NearestNeighbors(n_neighbors=10, metric='cosine').fit(embeddings)
 
 # Load the dataset
-df = pd.read_csv('quran_hadith.csv')
+df = pd.read_csv('quran_data.csv', delimiter='\t')
 
 # Initialize the SentenceTransformer model
 model = SentenceTransformer('all-MiniLM-L6-v2')
@@ -25,7 +25,7 @@ def semantic_search(query, model, embeddings, nbrs):
     distances, indices = nbrs.kneighbors([query_embedding])
 
     # Return the k most similar sentences and their distances
-    similar_sentences = [(df['text'].iloc[idx], dist) for idx, dist in zip(indices[0], distances[0])]
+    similar_sentences = [(df['ayat'].iloc[idx], dist) for idx, dist in zip(indices[0], distances[0])]
     return similar_sentences
 
 def search_interface(query):

embeddings.pkl → embeddings_quran.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bea0ad0ae5e5cf9a73dad7706c32f651e6596cd5b025a5abd440ca5bde7e006a
-size 40502947
+oid sha256:06857222e1efcae222c6e50269f15df7daa4bc892c49d43d24e3187342cfdb6c
+size 9277603

quran_hadith.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6beddedddf73d2e8327e284a0eeed55820a246e6f99e19445c812027b5744cc5
-size 10748559