Update app.py

app.py

@@ -91,33 +91,14 @@ class SemanticSearch:
 
 
 
-#def load_recommender(path, start_page=1):
-#    global recommender
-#   texts = pdf_to_text(path, start_page=start_page)
-#   chunks = text_to_chunks(texts, start_page=start_page)
-#    recommender.fit(chunks)
-#    return 'Corpus Loaded.'
-
-# The modified function generates embeddings based on PDF file name and page number and checks if the embeddings file exists before loading or generating it.	
 def load_recommender(path, start_page=1):
     global recommender
-    pdf_file = os.path.basename(path)
-    embeddings_file = f"{pdf_file}_{start_page}.npy"
-    
-    if os.path.isfile(embeddings_file):
-        embeddings = np.load(embeddings_file)
-        recommender.embeddings = embeddings
-        recommender.fitted = True
-        return "Embeddings loaded from file"
-    
     texts = pdf_to_text(path, start_page=start_page)
     chunks = text_to_chunks(texts, start_page=start_page)
     recommender.fit(chunks)
-    np.save(embeddings_file, recommender.embeddings)
     return 'Corpus Loaded.'
 
 
-
 def generate_text(openAI_key,prompt, engine="text-davinci-003"):
     openai.api_key = openAI_key
     completions = openai.Completion.create(
@@ -235,4 +216,4 @@ demo.launch()
 # # Define the button action
 # if btn:
 #     answer_value = question_answer(url, file, question)
-#     answer.value = answer_value
+#     answer.value = answer_value