add ½ cup parmesan cheese

faiss_store.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:94ca139af5ef2797f275619c44847537ca63620ff0e3533357b5bd68b74f1de1
+size 4113269

main.py

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+"""Python file to serve as the frontend"""
+import streamlit as st
+from streamlit_chat import message
+import faiss
+from langchain import OpenAI
+from langchain.chains import VectorDBQAWithSourcesChain
+import pickle
+
+# Load the LangChain.
+index = faiss.read_index("docs.index")
+
+with open("faiss_store.pkl", "rb") as f:
+    store = pickle.load(f)
+
+store.index = index
+chain = VectorDBQAWithSourcesChain.from_llm(llm=OpenAI(temperature=0), vectorstore=store)
+
+
+# From here down is all the StreamLit UI.
+st.set_page_config(page_title="Mekanism and Create Mod QA Bot", page_icon=":robot:")
+st.header("Mekanism and Create Mod QA Bot")
+
+if "generated" not in st.session_state:
+    st.session_state["generated"] = []
+
+if "past" not in st.session_state:
+    st.session_state["past"] = []
+
+
+def get_text():
+    input_text = st.text_input("You: ", "Hello, how are you?", key="input")
+    return input_text
+
+
+user_input = get_text()
+
+if user_input:
+    result = chain({"question": user_input})
+    output = f"Answer: {result['answer']}\nSources: {result['sources']}"
+
+    st.session_state.past.append(user_input)
+    st.session_state.generated.append(output)
+
+if st.session_state["generated"]:
+
+    for i in range(len(st.session_state["generated"]) - 1, -1, -1):
+        message(st.session_state["generated"][i], key=str(i))
+        message(st.session_state["past"][i], is_user=True, key=str(i) + "_user")