Update app.py

app.py

@@ -1,8 +1,46 @@
 import pandas as pd
 import gradio
+from clean_data import text_normalizer
+import pprint
+from sentence_transformers import SentenceTransformer, CrossEncoder, util
 
-def predict():
-    return
+# read data
+df = pd.read_csv('./assets/final_combined.csv')to_dict(orient='records')
+doc_embeddings = np.load('./assets/final_combined_embed.npy', allow_pickel=True)
+
+def semantic_search(normalized_query):
+    '''
+    function to perform semantic search given a search query
+    '''
+    query_embedding = bi_encoder.encode(query)
+    hits = util.semantic_search(query_embedding, doc_embeddings, top_k=50)
+    return hits[0]
+    
+def re_ranker(normalized_query, hits):
+    '''
+    function to re-rank semantic search results using cross encoding
+    '''
+    cross_inp = [[query, doc_embeddings[hit['corpus_id']]] for hit in hit]
+    cross_scores = cross_encoder.predict(cross_inp)
+
+    for idx in range(len(cross_scores)):
+        hits[idx]['cross-score'] = cross_scores[idx]
+    return sorted(hits, key=lambda x: x['cross-score'], reverse=True)
+
+
+def print_results(hits, k_items):
+    results = ""
+    for hit in hits[:k_items]:
+        results += pprint.pformat(df[hit['corpus_id']], indent=4)
+    return results
+        
+def predict(query):
+    normalized_query = text_normalizer(query)
+    
+    bi_hits = semantic_search(normalized_query)
+    reranked_hits = re_ranker(bi_hits)
+    
+    return print_results(reranked_hits, k_items = 10)
 
 app = gr.Interface(
         fn = predict,