Create app.py

app.py

@@ -0,0 +1,96 @@
+import streamlit as st 
+# from langchain_community.llms import HuggingFaceTextGenInference
+import os, pickle
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.schema import StrOutputParser
+
+from custom_llm import CustomLLM, custom_chain_with_history, custom_combined_chain, custom_dataframe_chain, format_df,custom_unique_df_chain
+
+
+API_TOKEN = os.getenv('HF_INFER_API')
+
+
+from typing import Optional
+
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_community.chat_models import ChatAnthropic
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain.memory import ConversationBufferMemory
+from langchain_core.runnables.history import RunnableWithMessageHistory
+
+
+@st.cache_data
+def get_df():
+    return pickle.load("ebesha_ticket_df.pkl")
+
+@st.cache_data
+def get_unique_values():
+    exec(custom_unique_df_chain(llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"]), df=st.session_state.df).invoke({"df_example":format_df(st.session_state.df.head(4))}))
+    return response
+
+
+@st.cache_resource
+def get_llm_chain():
+    dataframe_chain = custom_dataframe_chain(llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"]), df=st.session_state.df, unique_values=st.session_state.unique_values)
+    memory_chain = custom_chain_with_history(llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"]), memory=st.session_state.memory)
+    return custom_unique_df_chain(llm=llm, dataframe_chain=dataframe_chain, memory_chain=memory_chain)
+
+if 'memory' not in st.session_state:
+    st.session_state['memory'] = ConversationBufferMemory(return_messages=True)
+    st.session_state.memory.chat_memory.add_ai_message("Hello there! I'm AI assistant of Lintas Media Danawa. How can I help you today?")
+
+if 'df' not in st.session_state:
+    st.session_state['df'] = get_df()
+
+
+if 'unique_values' not in st.session_state:
+    
+    st.session_state.unique_values = get_unique_values()
+
+
+
+if 'chain' not in st.session_state:
+    # st.session_state['chain'] = custom_chain_with_history(llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"]), memory=st.session_state.memory)
+    st.session_state['chain'] = get_llm_chain()
+    # st.session_state['chain'] = custom_chain_with_history(llm=InferenceClient("https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1", headers = {"Authorization": f"Bearer {API_TOKEN}"}, stream=True, max_new_tokens=512, temperature=0.01), memory=st.session_state.memory)
+st.title("LMD Chatbot V3)
+st.subheader("Combination of Ticket Submission and WI/User Guide Knowledge")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = [{"role":"assistant", "content":"Hello there! I'm AI assistant of Lintas Media Danawa. How can I help you today?"}]
+
+# 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"])
+
+
+# React to user input
+if prompt := st.chat_input("Ask me anything.."):
+    # Display user message in chat message container
+    st.chat_message("User").markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "User", "content": prompt})
+    
+    # full_response = st.session_state.chain.invoke(prompt).split("\n<|")[0]
+    full_response = st.session_state.chain.invoke({"question":prompt, "memory":st.session_state.memory, "df_example":format_df(st.session_state.df.head(4))}).split("\n<|")[0]
+    
+
+    with st.chat_message("assistant"):
+        st.markdown(full_response)
+
+    # Display assistant response in chat message container
+    # with st.chat_message("assistant"):
+    #     message_placeholder = st.empty()
+    #     full_response = ""
+    #     for chunk in st.session_state.chain.stream(prompt):
+    #         full_response += chunk + " "
+    #         message_placeholder.markdown(full_response + " ")
+    #         if full_response[-4:] == "\n<|":
+    #             break
+        # st.markdown(full_response)
+    st.session_state.memory.save_context({"question":prompt}, {"output":full_response})
+    st.session_state.memory.chat_memory.messages = st.session_state.memory.chat_memory.messages[-15:]
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": full_response})