Update app.py

app.py

@@ -108,103 +108,13 @@ def build_experimental_ui():
             
         button_query = st.button('Submit', disabled=False)
         
-        
         if button_query:
 
             for question in  questions_df['question']:
                 instruction = f'{prompt}.Question:{question}'
-        
-            print('---- run query ----')
-            print(f'model: {selected_model}  embeddings: {selected_embeddings}')
-            if selected_embeddings!=st.session_state['selected_embeddings']:
-                st.session_state['selected_embeddings'] = selected_embeddings
-                texts = load_pdf_document(pdf_docs)
-                st.session_state['retriever'] = get_retriever_from_text(texts, embeddings[selected_embeddings])
-    #        qa = RetrievalQA.from_chain_type(llm=models[selected_model], chain_type="stuff", 
-    #                                         retriever=st.session_state['retriever'], return_source_documents=True)
-            st.session_state['docs'] = st.session_state['retriever'].get_relevant_documents(st.session_state.query)
-            context = '\n\n'.join([doc.page_content for doc in st.session_state['docs']])
-            st.session_state['context'] = context
-            source_files = get_pdf_file_names(st.session_state['pdf_file'])
-            #st.session_state['conversation']= get_conversation_chain(st.session_state['retriever'])
-
-            if strategy=='Without Chain-of-Thought':
-                user_token = model_configs[selected_model]['USER_TOKEN']
-                end_token = model_configs[selected_model]['END_TOKEN']
-                assistant_token = model_configs[selected_model]['ASSISTANT_TOKEN']
-                prompt_pattern, prompt = create_prompt(user_token, instruction, st.session_state.query, end_token, assistant_token, context)
-                updated_context = truncate_context(prompt_pattern, context, 
-                                                   max_token_len=model_configs[selected_model]['MAX_TOKENS'],
-                                                   max_new_token_length=model_configs[selected_model]['MAX_NEW_TOKEN_LENGTH'])
-                updated_prompt = prompt_pattern.replace('{context}', updated_context)
-                print(updated_prompt)
-
-                
-                with st.spinner():
-                    answer = models[selected_model].generate([updated_prompt]).generations[0][0].text.strip()
-                st.write(answer)
-                chat_content['answer'] = answer
-                chat_content['source'] = source_files
-                chat_content['context']=st.session_state['context']
-                chat_content['time']=datetime.now().strftime("%d-%m-%Y %H:%M:%S")
-                if st.session_state['chat_history']: 
-                    st.session_state['chat_history'].append(chat_content)
-                else:
-                    st.session_state['chat_history']=[chat_content]
-                print('------chat history-----',st.session_state['chat_history'])
-                if updated_prompt!=prompt:
-                    st.caption(f"Note: The context has been truncated to fit model max tokens of {model_configs[selected_model]['MAX_TOKENS']}. Original context contains {len(context.split())} words. Truncated context contains {len(updated_context.split())} words.")
 
-            else:
-                chain = PDSCoverageChain()
-                with st.spinner():
-                    answer = chain.generate(models[selected_model], model_configs[selected_model], st.session_state.query, context)
-                st.write(answer)
-                chat_content['answer'] = answer
-                chat_content['source'] = source_files
-                chat_content['context']=st.session_state['context']
-                chat_content['time']=datetime.now().strftime("%d-%m-%Y %H:%M:%S")
-                if st.session_state['chat_history']: 
-                    st.session_state['chat_history'].append(chat_content)
-                else:
-                    st.session_state['chat_history']=[chat_content]
-                print('------chat history-----',st.session_state['chat_history'])
-               
-        
-        if st.session_state['docs']:
-
-            docs = st.session_state['docs']
+            return
             
-            col3, col4, col5, col6 = st.columns([0.2,0.35, 0.65, 3.8])
-            if st.session_state.query is None:
-                disable_query = True
-            else:
-                disable_query = False
-            chat_history = st.session_state['chat_history']
-            with col3:
-                st.button(":thumbsup:",  on_click = get_feedback,disabled=disable_query,
-                            kwargs=dict(upvote=True, downvote=False,
-                                        button='upvote'))
-            with col4:   
-                st.button(":thumbsdown:",  on_click = get_feedback,disabled=disable_query,
-                            kwargs=dict(upvote=False, downvote=True,
-                                        button='downvote'))
-        
-        
-            with st.expander("References"):
-                for doc in docs:
-                    print('-------',doc)
-                    #st.markdown('###### Page {}'.format(doc.metadata['page']))
-                    st.write(doc.page_content.replace('\n','\n\n').replace('$','\$').replace('**',''))
-        
-                
-            
-            st.button("End Chat",  on_click = get_feedback,
-                    kwargs=dict(button='end-chat',
-                                chat_history=chat_history))
-            
-
-
     
     else:
         st.info("Under Development")