加入session后重整的代码逻辑

app.py

@@ -10,7 +10,6 @@ from nltk.corpus import stopwords
 from nltk.stem import WordNetLemmatizer
 from collections import Counter
 import nltk
-import time
 
 openai.api_key = st.secrets["OPENAI_API_KEY"]
 
@@ -229,26 +228,59 @@ def initialize_app(added_files, num_lessons, language):
     course_outline_list = courseOutlineGenerating(temp_file_paths, num_lessons, language)
     outline_generating_state.text("Generating Course Outline...Done")
 
+    outline_presenting = st.empty()
+    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 outline_presenting.expander("Check the course outline", expanded=False):
+        st.write(course_outline_string)
+
+    content_presenting = st.empty()
+    content_generating_state = st.empty()
+    count_generating_content = 0
+    course_content_list = []
+    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, st.session_state.embeddings_df, st.session_state.faiss_index)
+        courseContent = generateCourse(lesson, retrievedChunksList, language)
+        course_content_list.append(courseContent)
+        content_generating_state.text(f"Writing content for lesson {count_generating_content}...Done")
+        with content_presenting.expander(f"Learn the lesson {count_generating_content} ", expanded=False):
+            st.markdown(courseContent)
+
     file_proc_state.empty()
     vdb_state.empty()
     outline_generating_state.empty()
-
-    return embeddings_df, faiss_index, course_outline_list
+    outline_presenting.empty()
+    content_presenting.empty()
+    content_generating_state.empty()
+
+    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)
+
+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)
 
 def app():
     st.title("OmniTutor v0.0.2")
 
-    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"])
-
     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)
@@ -259,75 +291,66 @@ def app():
             language = 'Chinese'
         btn = st.button('submit')
     
-    col1, col2 = st.columns([0.6,0.4])
-    
-    user_question = st.chat_input("Enter your questions when learning...")
-    
     if btn:
-        
-        if "embeddings_df" and "faiss_index" and "course_outline_list" not in st.session_state:
-            st.session_state.embeddings_df, st.session_state.faiss_index, st.session_state.course_outline_list = initialize_app(added_files, num_lessons, language)
+
+        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, 
+            st.session_state.course_outline_list,
+            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])
         
         with col1:
-            st.text("Processing file...Done")
-            st.text("Constructing vector database from provided materials...Done")
-            st.text("Generating Course Outline...Done")
-
             #把课程大纲打印出来
-            course_outline_string = ''
-            lessons_count = 0
-            for outline in st.session_state.course_outline_list:
-                lessons_count += 1
-                course_outline_string += f"{lessons_count}." + outline[0]
-                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 st.session_state.course_outline_list:
-                count_generating_content += 1
-                content_generating_state = st.text(f"Writing content for lesson {count_generating_content}...")
-                retrievedChunksList = searchVDB(lesson, st.session_state.embeddings_df, st.session_state.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)
-    
-    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? 😄")
-            
-            #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": 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})
-    
+            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 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})
+        
     
     
 if __name__ == "__main__":