fix

app.py

@@ -1,11 +1,4 @@
 # -*- coding: utf-8 -*-
-"""gradio_wangchanberta
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1Kw2k1oymhq4ZAcy4oBYOlIg4bBU-HlVr
-"""
 
 #@title scirpts
 import time
@@ -177,6 +170,17 @@ class Chatbot:
     #     score="{:.4f})".format(hit['score'])
     #     Answer = self.model_pipeline(message, context)
     #     return Answer
+    def predict_semantic_search(self, message):
+        message = message.strip()
+        query_embedding = self.embedding_model.encode([message], convert_to_tensor=True)[0]
+        corpus_embeddings = self.embedding_model.encode(self.df['Question'].tolist(), convert_to_tensor=True)
+        hits = util.semantic_search(query_embedding.unsqueeze(0), corpus_embeddings, top_k=1)
+        hit = hits[0][0]
+        context = self.df['Context'][hit['corpus_id']]
+        Answer = self.model_pipeline(message, context)
+        return Answer
+
+    
     def predict_semantic_search(self, message):
         message = message.strip()
         query_embedding = self.embedding_model.encode([message], convert_to_tensor=True)[0]  # Fix here
@@ -221,4 +225,10 @@ demoSemantic = gr.Interface(fn=bot._chatbot.predict_semantic_search, inputs="tex
 demoWithoutFiss = gr.Interface(fn=bot._chatbot.predict_without_faiss, inputs="text", outputs="text",examples=EXAMPLE_PATH, title="TH wiki (just Model)")
 
 demo = gr.TabbedInterface([demoFaiss, demoWithoutFiss, demoBert, demoSemantic], ["Faiss", "Model", "Faiss & Model", "Semantic Search & Model"])
-demo.launch()
+demo.launch()
+if __name__ == "__main__":
+    df = load_data()
+    model, tokenizer = load_model('wangchanberta-hyp')
+    embedding_model = load_embedding_model()
+    index = set_index(prepare_sentences_vector(load_embeddings(EMBEDDINGS_PATH)))
+    interface.launch()