Update app.py

app.py

@@ -121,8 +121,8 @@ with st.sidebar:
     with st.spinner("Preparing website materials for you..."):
         try:
             url="https://www.usinoip.com"
-            loader = WebBaseLoader(url)
-            raw_text = loader.load()
+            url_loader = WebBaseLoader(url)
+            raw_text = url_loader.load()
             page_content = raw_text[0].page_content
             page_content = str(page_content)
             temp_texts = text_splitter.split_text(page_content)
@@ -134,40 +134,30 @@ with st.sidebar:
             print("Please enter a valide URL.")
             st.stop()  
           
-if user_question.strip().isspace() or user_question.isspace():
-    st.write("Query Empty. Please enter a valid query first.")
-    st.stop()
-elif user_question == "exit":
-    st.stop()
-elif user_question == "":
-    print("Query Empty. Please enter a valid query first.")
-    st.stop()
-elif user_question != "":     
-    #st.write("Your query: "+user_question)
-    print("Your query: "+user_question)
-    
-with st.spinner("AI Thinking...Please wait a while to Cheers!"):
-    q_embedding=get_embeddings(user_question)
-    final_q_embedding = torch.FloatTensor(q_embedding)  
-    hits = semantic_search(final_q_embedding, db_embeddings, top_k=5)
-    page_contents = []
-    for i in range(len(hits[0])):
-        page_content = texts[hits[0][i]['corpus_id']]
-        page_contents.append(page_content)
-    temp_page_contents=str(page_contents)
-    final_page_contents = temp_page_contents.replace('\\n', '')     
-    random_string = generate_random_string(20)
-    i_file_path = random_string + ".txt"
-    with open(i_file_path, "w", encoding="utf-8") as file:
-        file.write(final_page_contents)
-    loader = TextLoader(i_file_path, encoding="utf-8")
-    loaded_documents = loader.load()
-    temp_ai_response=chain({"input_documents": loaded_documents, "question": user_question}, return_only_outputs=False)
-    initial_ai_response=temp_ai_response['output_text']
-    cleaned_initial_ai_response = remove_context(initial_ai_response)
-    final_ai_response = cleaned_initial_ai_response.split('<|end|>\n<|system|>\n<|end|>\n<|user|>')[0].strip().replace('\n\n', '\n').replace('<|end|>', '').replace('<|user|>', '').replace('<|system|>', '').replace('<|assistant|>', '')
-    #temp_ai_response = temp_ai_response['output_text']    
-    #final_ai_response=temp_ai_response.partition('<|end|>')[0]
-    #i_final_ai_response = final_ai_response.replace('\n', '')
-    st.write("AI Response:")
-    st.write(final_ai_response)
+if st.button('Get AI Response'):
+    if user_question !="" and not user_question.strip().isspace() and not user_question == "" and not user_question.strip() == "" and not user_question.isspace():
+        with st.spinner("AI Thinking...Please wait a while to Cheers!"):
+            q_embedding=get_embeddings(user_question)
+            final_q_embedding = torch.FloatTensor(q_embedding)  
+            hits = semantic_search(final_q_embedding, db_embeddings, top_k=5)
+            page_contents = []
+            for i in range(len(hits[0])):
+                page_content = texts[hits[0][i]['corpus_id']]
+                page_contents.append(page_content)
+            temp_page_contents=str(page_contents)
+            final_page_contents = temp_page_contents.replace('\\n', '')     
+            random_string = generate_random_string(20)
+            i_file_path = random_string + ".txt"
+            with open(i_file_path, "w", encoding="utf-8") as file:
+                file.write(final_page_contents)
+            text_loader = TextLoader(i_file_path, encoding="utf-8")
+            loaded_documents = text_loader.load()
+            temp_ai_response=chain({"input_documents": loaded_documents, "question": user_question}, return_only_outputs=False)
+            initial_ai_response=temp_ai_response['output_text']
+            cleaned_initial_ai_response = remove_context(initial_ai_response)
+            final_ai_response = cleaned_initial_ai_response.split('<|end|>\n<|system|>\n<|end|>\n<|user|>')[0].strip().replace('\n\n', '\n').replace('<|end|>', '').replace('<|user|>', '').replace('<|system|>', '').replace('<|assistant|>', '')
+            #temp_ai_response = temp_ai_response['output_text']    
+            #final_ai_response=temp_ai_response.partition('<|end|>')[0]
+            #i_final_ai_response = final_ai_response.replace('\n', '')
+            st.write("AI Response:")
+            st.write(final_ai_response)