Delete app.py

app.py

@@ -1,136 +0,0 @@
-
-import streamlit as st
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-from pinecone import Pinecone
-from langchain.vectorstores import Pinecone as LangchainPinecone
-from langchain_groq import ChatGroq
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
-from langchain.chains import create_retrieval_chain
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_community.chat_message_histories import StreamlitChatMessageHistory
-from langchain_core.chat_history import BaseChatMessageHistory
-from langchain_core.runnables.history import RunnableWithMessageHistory
-from langchain.chains import create_history_aware_retriever
-import time
-import os
-
-
-# Embedding setup
-model_name = "BAAI/bge-small-en"
-model_kwargs = {"device": "cpu"}
-encode_kwargs = {"normalize_embeddings": True}
-embeddings = HuggingFaceBgeEmbeddings(
-    model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
-)
-
-# Pinecone setup
-pc = Pinecone(api_key="pcsk_5yLpy7_7DWbGm2s2HTf1NCbo4zFB8KLEZFLT54q3poTUoEFMbf1B9ShUZqpsT7EPnE3Pjw")
-text_field = "text"
-index_name = "contentengine"
-index = pc.Index(index_name)
-vectorstore = LangchainPinecone(index, embeddings.embed_query, text_field)
-
-# Retriever setup
-retriever = vectorstore.as_retriever(
-    search_type="similarity_score_threshold",
-    search_kwargs={"k": 1, "score_threshold": 0.5},
-)
-
-
-
-llm =  ChatGroq(model="llama3-8b-8192", api_key='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', max_tokens=4096)
-
-
-# Retriever prompt setup
-retriever_prompt = """
-Given a chat history and the latest user question which might reference context in the chat history,
-formulate a standalone question which can be understood without the chat history.
-Do NOT answer the question, just reformulate it if needed and otherwise return it as is.
-
-Chat History:
-{chat_history}
-
-User Question: {input}
-
-Standalone question:
-"""
-contextualize_q_prompt = ChatPromptTemplate.from_messages([
-    ("system", retriever_prompt),
-    MessagesPlaceholder(variable_name="chat_history"),
-    ("human", "{input}"),
-])
-
-history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
-
-from langchain_core.prompts import PromptTemplate
-
-template = """
-Context: This Content Engine is designed to analyze and compare key information across multiple Form 10-K filings for major companies, specifically Alphabet, Tesla, and Uber. The system uses Retrieval-Augmented Generation (RAG) to retrieve and summarize insights, highlight differences, and answer user queries on various financial and operational topics, such as risk factors, revenue, and business models.
-
-Chat History: {chat_history}
-Context: {context}
-Human: {input}
-
-Answer:
-"""
-
-# Define the PromptTemplate with specified input variables
-custom_rag_prompt = PromptTemplate(template=template, input_variables=["chat_history", "context", "input"])
-
-question_answering_chain = create_stuff_documents_chain(llm, custom_rag_prompt)
-rag_chain = create_retrieval_chain(history_aware_retriever, question_answering_chain)
-
-# ======================================================= Streamlit UI =======================================================
-
-st.title("Chat with Content Engine")
-
-# Initialize chat history
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = StreamlitChatMessageHistory(key="chat_messages")
-
-# Message history setup
-def get_chat_history():
-    return st.session_state.chat_history
-
-# Conversational_rag_chain to use the Streamlit chat history
-conversational_rag_chain = RunnableWithMessageHistory(
-    rag_chain,
-    get_chat_history,
-    input_messages_key="input",
-    history_messages_key="chat_history",
-    output_messages_key="answer"
-)
-
-# Function to interact with the chatbot
-def chat_with_bot(query: str) -> str:
-    result = conversational_rag_chain.invoke(
-        {"input": query},
-        config={
-            "configurable": {"session_id": "streamlit_session"}
-        },
-    )
-    return result["answer"]
-
-# Display chat messages from history
-for message in st.session_state.chat_history.messages:
-    with st.chat_message(message.type):
-        st.markdown(message.content)
-
-# Accept user input
-if user_input := st.chat_input("Enter your question here..."):
-
-    # Display user message in chat message container
-    with st.chat_message("human"):
-        st.markdown(user_input)
-
-    # Display assistant response in chat message container
-    with st.chat_message("ai"):
-        with st.spinner("Thinking..."):
-            response = chat_with_bot(user_input)
-            message_placeholder = st.empty()
-            full_response = "⚠️ **_Reminder: Please double-check information._** \n\n"
-            for chunk in response:
-                full_response += chunk
-                time.sleep(0.01)
-                message_placeholder.markdown(full_response + ":white_circle:", unsafe_allow_html=True)
-