修改流程顺序，看看效果

app.py

@@ -210,7 +210,7 @@ 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=5, max_value=20, value=10, step=1)
+        num_lessons = st.slider('How many lessons do you want this course to have?', min_value=5, max_value=19, value=10, step=1)
         language = 'English'
         Chinese = st.checkbox('Output in Chinese')
         if Chinese:
@@ -218,87 +218,88 @@ def app():
         btn = st.button('submit')
 
     
-    col1, col2 = st.columns([0.6,0.4], gap='large')
+    col1, col2 = st.columns([0.6,0.4])
         
     if btn:
         temp_file_paths = []
-        col1.file_proc_state = st.text("Processing file...")
+        file_proc_state = st.text("Processing file...")
         for added_file in added_files:
             with tempfile.NamedTemporaryFile(delete=False, suffix=".md") as tmp:
                 tmp.write(added_file.getvalue())
                 tmp_path = tmp.name
                 temp_file_paths.append(tmp_path)
-        col1.file_proc_state.text("Processing file...Done")
+        file_proc_state.text("Processing file...Done")
 
-        col1.outline_generating_state = st.text("Generating Course Oueline...")
-        course_outline_list = courseOutlineGenerating(temp_file_paths, num_lessons, language)
-        col1.outline_generating_state.text("Generating Course Oueline...Done")
-        
-        #把课程大纲打印出来
-        course_outline_string = ''
-        lessons_count = 0
-        for outline in course_outline_list:
-            lessons_count += 1
-            course_outline_string += f"{lessons_count}." + outline[0] + '\n'
-            course_outline_string += '\n' + outline[1] + '\n\n'
-            #time.sleep(1)
-        with col1.st.expander("Check the course outline", expanded=False):
-                    st.write(course_outline_string)
-
-        col1.vdb_state = st.text("Constructing vector database from provided materials...")
+        vdb_state = st.text("Constructing vector database from provided materials...")
         embeddings_df, faiss_index = constructVDB(temp_file_paths)
-        col1.vdb_state.text("Constructing vector database from provided materials...Done")
+        vdb_state.text("Constructing vector database from provided materials...Done")
         
-        count_generating_content = 0
-        for lesson in course_outline_list:
-            count_generating_content += 1
-            content_generating_state = st.text(f"Writing content for lesson {count_generating_content}...")
-            retrievedChunksList = searchVDB(lesson, embeddings_df, faiss_index)
-            courseContent = generateCourse(lesson, retrievedChunksList, language)
-            content_generating_state.text(f"Writing content for lesson {count_generating_content}...Done")
-            #st.text_area("Course Content", value=courseContent)
-            with col1.st.expander(f"Learn the lesson {count_generating_content} ", expanded=False):
-                st.markdown(courseContent)
-
-    user_question = st.chat_input("Enter your questions when learning...")
-
-    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
-        for message in st.session_state.messages:
-            with st.chat_message(message["role"]):
-                st.markdown(message["content"])
-        #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, embeddings_df, faiss_index)
-            prompt = decorate_user_question(user_question, retrieved_chunks_for_user)
-            st.session_state.messages.append({"role": "user", "content": prompt})
-            with st.chat_message("user"):
-                st.markdown(user_question)
-            # Display assistant response in chat message container
+        outline_generating_state = st.text("Generating Course Oueline...")
+        course_outline_list = courseOutlineGenerating(temp_file_paths, num_lessons, language)
+        outline_generating_state.text("Generating Course Oueline...Done")
+        
+        with col1:
+            #把课程大纲打印出来
+            course_outline_string = ''
+            lessons_count = 0
+            for outline in course_outline_list:
+                lessons_count += 1
+                course_outline_string += f"{lessons_count}." + outline[0] + '\n'
+                course_outline_string += '\n' + outline[1] + '\n\n'
+                #time.sleep(1)
+            with st.expander("Check the course outline", expanded=False):
+                        st.write(course_outline_string)
+
+            count_generating_content = 0
+            for lesson in course_outline_list:
+                count_generating_content += 1
+                content_generating_state = st.text(f"Writing content for lesson {count_generating_content}...")
+                retrievedChunksList = searchVDB(lesson, embeddings_df, faiss_index)
+                courseContent = generateCourse(lesson, retrievedChunksList, language)
+                content_generating_state.text(f"Writing content for lesson {count_generating_content}...Done")
+                #st.text_area("Course Content", value=courseContent)
+                with st.expander(f"Learn the lesson {count_generating_content} ", expanded=False):
+                    st.markdown(courseContent)
+
+        user_question = st.chat_input("Enter your questions when learning...")
+
+        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"):
-                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"]} 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})
+                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
+            for message in st.session_state.messages:
+                with st.chat_message(message["role"]):
+                    st.markdown(message["content"])
+            #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, embeddings_df, faiss_index)
+                prompt = decorate_user_question(user_question, retrieved_chunks_for_user)
+                st.session_state.messages.append({"role": "user", "content": 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"]} 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})
 
     
 if __name__ == "__main__":