Upload app.py

"made UI GPT-like, added memory"

app.py

@@ -11,28 +11,16 @@ from langchain.schema import (
 )
 from langchain.embeddings.openai import OpenAIEmbeddings
 
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import Chroma
-
 import streamlit as st
-from streamlit_chat import message
 
 load_dotenv()
 
 
 os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
-llm = ChatOpenAI(temperature=0.3, model="gpt-3.5-turbo")
+llm = ChatOpenAI(temperature=0.3)
 embeddings = OpenAIEmbeddings()
 
 
-@st.cache_data
-def load_into_chroma(docs):
-    text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-    docs = text_splitter.split_documents(text_splitter)
-    global db_chroma
-    db_chroma = Chroma.from_documents(docs, embeddings)
-
-
 def generate_content(query, knowledge_base):
     # relevant_docs = db_chroma.similarity_search(query)
     system_prompt = f"""You are a professional writer of motivational letters.\
@@ -44,50 +32,51 @@ Make the letter very personal with regards to the knowledge base.
 
 Knowledge Base: ```{knowledge_base}```
 """
-    system_message = SystemMessage(content=system_prompt)
-    human_message = HumanMessage(content=query)
-    message = [system_message, human_message]
-    response = llm(message)
+    # system_message = SystemMessage(content=system_prompt)
+    # human_message = HumanMessage(content=query[-1]['content'])
+    # message = [system_message, human_message]
+    messages = [SystemMessage(content=system_prompt)]
+    for i in range(len(query)):
+        if i % 2 == 0:
+            temp_query = HumanMessage(content=query[i]['content'])
+        else:
+            temp_query = AIMessage(content=query[i]['content'])
+        messages.append(temp_query)
+    response = llm(messages)
     return response.content
 
 
-system_session_prompt = """As a professional writer of motivational letters, \
-your task is to write a sales proposal provided to you according to \
-the required changes. You will make the recommended changes to the \
-sales proposal and return the entire proposal with thse changes. \
-Your job depends on the answers you provide so play close attention to \
-the queries you recieve.
-"""
-
-
 def main():
-    st.title("ChatGPT 🤖 Powered Chatbot")
-    st.header("Sales Proposal Generator")
+    st.title("GradGPT 🤖")
+    st.header("ChatGPT Powered Motivational Letter writer")
 
     uploaded_file = st.file_uploader("Upload a word file", type="docx")
-    if "messages" not in st.session_state:
-        st.session_state.messages = [AIMessage(content="How can I help you?")]
     if uploaded_file is not None:
         # extract text from word file
         knowledge_base = docx2txt.process(uploaded_file)
         # load_into_chroma(call_transcript)
 
-        with st.sidebar:
-            user_input = st.text_area("Enter your query: ", key="user_input")
-            st.session_state.messages.append(HumanMessage(content=user_input))
-
-            if st.button("Generate content"):
-                with st.spinner("GPT is thinking..."):
-                    response = generate_content(user_input, knowledge_base)
-                    st.session_state.messages.append(AIMessage(content=response))
-
-    # display message history
-    messages = st.session_state.get('messages', [])
-    for i in range(len(messages)):
-        if i % 2 == 0:
-            message(messages[i].content, is_user=False, key=str(i) + '_user')
-        else:
-            message(messages[i].content, is_user=True, key=str(i) + '_ai')
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    if prompt := st.chat_input("Enter your queries here."):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            content = generate_content(
+                st.session_state.messages, knowledge_base
+            )
+        st.session_state.messages.append(
+            {"role": "assistant", "content": content}
+        )
+        message_placeholder.markdown(content)
 
 
 if __name__ == '__main__':