Update app.py

app.py

@@ -190,10 +190,31 @@ RESPUESTA: """
         st.session_state.messages.append({"role": "user", "content": prompt})
         with st.chat_message("assistant"):
             if 'GPT' not in genre:
-                llm_response = qa_chain(prompt)['result']
+                if prompt.lower() == 'resume':
+                    llm_response = utils.summarise_doc(TRANSCRIPTION_PATH, model='llama')
+                    st.markdown(llm_response)
+                else:
+                    llm_response = qa_chain(prompt)['result']
+                    llm_response = utils.process_llm_response(llm_response)
+                    st.markdown(llm_response)
+        
+                    start_time_str_list = []; start_time_seconds_list = []; end_time_seconds_list = []
+                    for response in llm_response.split('\n'):
+                        if re.search(r'(\d{2}:\d{2}:\d{2}(.\d{6})?)', response) != None:
+                            start_time_str, start_time_seconds, _, end_time_seconds = utils.add_hyperlink_and_convert_to_seconds(response)
+                            start_time_str_list.append(start_time_str)
+                            start_time_seconds_list.append(start_time_seconds)
+                            end_time_seconds_list.append(end_time_seconds)
+        
+                    if start_time_str_list:
+                        for start_time_seconds, start_time_str, end_time_seconds in zip(start_time_seconds_list, start_time_str_list, end_time_seconds_list):
+                            st.markdown("__Fragmento: " + start_time_str + "__")
+                            _, container, _ = st.columns([SIDE, WIDTH, SIDE])
+                            with container:
+                                st_player(youtube_video_url.replace("?enablejsapi=1", "") + f'?start={start_time_seconds}&end={end_time_seconds}')
             else:
                 if prompt.lower() == 'resume':
-                    llm_response = utils.summarise_doc(TRANSCRIPTION_PATH)
+                    llm_response = utils.summarise_doc(TRANSCRIPTION_PATH, model='gpt')
                     st.markdown(llm_response)
                 else:
                     llm_response = utils.get_gpt_response(TRANSCRIPTION_PATH, prompt, logger)