改进session

app.py

@@ -262,21 +262,27 @@ def initialize_app(added_files, num_lessons, language):
     return embeddings_df, faiss_index, course_outline_list, course_content_list
 
 def regenerate_outline(course_outline_list):
-    course_outline_string = ''
-    lessons_count = 0
-    for outline in course_outline_list:
-        lessons_count += 1
-        course_outline_string += f"{lessons_count}." + outline[0]
-        course_outline_string += '\n\n' + outline[1] + '\n\n'
-    with st.expander("Check the course outline", expanded=False):
-        st.write(course_outline_string)
+    try:
+        course_outline_string = ''
+        lessons_count = 0
+        for outline in course_outline_list:
+            lessons_count += 1
+            course_outline_string += f"{lessons_count}." + outline[0]
+            course_outline_string += '\n\n' + outline[1] + '\n\n'
+        with st.expander("Check the course outline", expanded=False):
+            st.write(course_outline_string)
+    except Exception:
+        pass
 
 def regenerate_content(course_content_list):
-    count_generating_content = 0
-    for content in course_content_list:
-        count_generating_content += 1
-        with st.expander(f"Learn the lesson {count_generating_content} ", expanded=False):
-            st.markdown(content)
+    try:
+        count_generating_content = 0
+        for content in course_content_list:
+            count_generating_content += 1
+            with st.expander(f"Learn the lesson {count_generating_content} ", expanded=False):
+                st.markdown(content)
+    except Exception:
+        pass
 
 def app():
     st.title("OmniTutor v0.0.2")
@@ -284,7 +290,7 @@ def app():
     with st.sidebar:
         st.image("https://siyuan-harry.oss-cn-beijing.aliyuncs.com/oss://siyuan-harry/20231021212525.png")
         added_files = st.file_uploader('Upload .md file', type=['.md'], accept_multiple_files=True)
-        num_lessons = st.slider('How many lessons do you want this course to have?', min_value=3, max_value=14, value=5, step=1)
+        num_lessons = st.slider('How many lessons do you want this course to have?', min_value=3, max_value=15, value=5, step=1)
         language = 'English'
         Chinese = st.checkbox('Output in Chinese')
         if Chinese:
@@ -292,7 +298,6 @@ def app():
         btn = st.button('submit')
     
     if btn:
-
         if "embeddings_df" and "faiss_index" and "course_outline_list" and "course_content_list" not in st.session_state:
             st.session_state.embeddings_df, 
             st.session_state.faiss_index, 
@@ -300,56 +305,57 @@ def app():
             st.session_state.course_content_list = initialize_app(added_files, num_lessons, language)
         #embeddings_df, faiss_index, course_outline_list = initialize_app(added_files, num_lessons, language)
 
-        col1, col2 = st.columns([0.6,0.4])
-        
+    col1, col2 = st.columns([0.6,0.4])
+    
+    # 这里还可以设置col1 和 col2 不同时出现 - 需要拆分initialize_app()函数
+    if st.session_state.course_content_list:
         with col1:
             #把课程大纲打印出来
             regenerate_outline(st.session_state.course_outline_list)
             #把课程内容打印出来
             regenerate_content(st.session_state.course_content_list)
-    
-        with col2:
-
-                st.caption(''':blue[AI Assistant]: Ask this TA any questions related to this course and get direct answers. :sunglasses:''')
-                    # Set a default model
 
+        with col2:
+            st.caption(''':blue[AI Assistant]: Ask this TA any questions related to this course and get direct answers. :sunglasses:''')
+                # Set a default model
+
+            with st.chat_message("assistant"):
+                st.write("Hello👋, how can I help you today? 😄")
+
+            if "openai_model" not in st.session_state:
+                st.session_state["openai_model"] = "gpt-3.5-turbo"
+                # Initialize chat history
+            if "messages" not in st.session_state:
+                st.session_state.messages = []
+
+            # Display chat messages from history on app rerun - 这部分不用session，利用好rerun
+            for message in st.session_state.messages:
+                with st.chat_message(message["role"]):
+                    st.markdown(message["content"][0])
+            
+            user_question = st.chat_input("Enter your questions when learning...")
+
+            #这里的session.state就是保存了这个对话会话的一些基本信息和设置
+            if user_question:
+                retrieved_chunks_for_user = searchVDB(user_question, st.session_state.embeddings_df, st.session_state.faiss_index)
+                #retrieved_chunks_for_user = []
+                prompt = decorate_user_question(user_question, retrieved_chunks_for_user)
+                st.session_state.messages.append({"role": "user", "content": [user_question, prompt]})
+                with st.chat_message("user"):
+                    st.markdown(user_question)
+                # Display assistant response in chat message container
                 with st.chat_message("assistant"):
-                    st.write("Hello👋, how can I help you today? 😄")
-
-                if "openai_model" not in st.session_state:
-                    st.session_state["openai_model"] = "gpt-3.5-turbo"
-                    # Initialize chat history
-                if "messages" not in st.session_state:
-                    st.session_state.messages = []
-
-                # Display chat messages from history on app rerun - 这部分不用session，利用好rerun
-                for message in st.session_state.messages:
-                    with st.chat_message(message["role"]):
-                        st.markdown(message["content"][0])
-                
-                user_question = st.chat_input("Enter your questions when learning...")
-
-                #这里的session.state就是保存了这个对话会话的一些基本信息和设置
-                if user_question:
-                    retrieved_chunks_for_user = searchVDB(user_question, st.session_state.embeddings_df, st.session_state.faiss_index)
-                    #retrieved_chunks_for_user = []
-                    prompt = decorate_user_question(user_question, retrieved_chunks_for_user)
-                    st.session_state.messages.append({"role": "user", "content": [user_question, prompt]})
-                    with st.chat_message("user"):
-                        st.markdown(user_question)
-                    # Display assistant response in chat message container
-                    with st.chat_message("assistant"):
-                        message_placeholder = st.empty()
-                        full_response = ""
-                        for response in openai.ChatCompletion.create(
-                            model=st.session_state["openai_model"],
-                            messages=[{"role": m["role"], "content": m["content"][1]} for m in st.session_state.messages],
-                            stream=True,
-                        ):
-                            full_response += response.choices[0].delta.get("content", "")
-                            message_placeholder.markdown(full_response + "▌")
-                        message_placeholder.markdown(full_response)
-                    st.session_state.messages.append({"role": "assistant", "content": full_response})
+                    message_placeholder = st.empty()
+                    full_response = ""
+                    for response in openai.ChatCompletion.create(
+                        model=st.session_state["openai_model"],
+                        messages=[{"role": m["role"], "content": m["content"][1]} for m in st.session_state.messages],
+                        stream=True,
+                    ):
+                        full_response += response.choices[0].delta.get("content", "")
+                        message_placeholder.markdown(full_response + "▌")
+                    message_placeholder.markdown(full_response)
+                st.session_state.messages.append({"role": "assistant", "content": full_response})