Upload app.py

app.py

@@ -0,0 +1,76 @@
+from Functions.write_stream import user_data
+import streamlit as st
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, ServiceContext
+from llama_index.llms.llama_cpp import LlamaCPP
+from llama_index.llms.llama_cpp.llama_utils import messages_to_prompt, completion_to_prompt
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+
+
+directory = "Knowledge Base/"
+
+
+documents = SimpleDirectoryReader(directory).load_data()
+
+llm = LlamaCPP(
+# You can pass in the URL to a GGML model to download it automatically
+model_url='https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf',
+# optionally, you can set the path to a pre-downloaded model instead of model_url
+model_path=None,
+temperature=0.75,
+max_new_tokens=256,
+# llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
+context_window=3900,
+messages_to_prompt=messages_to_prompt,
+completion_to_prompt=completion_to_prompt,
+verbose=True,
+)
+print("working -3")
+
+embed_model = HuggingFaceEmbeddings(model_name="thenlper/gte-large")
+
+print("working -2")
+
+service_context = ServiceContext.from_defaults(
+    chunk_size= 256,
+    llm=llm,
+    embed_model=embed_model
+)
+print("working -1")
+
+index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)
+print("working 0")
+
+query_engine = index.as_query_engine()
+
+
+
+###############=============    USER INTERFACE (UI    )###############=============
+
+
+st.title("Wiki Bot")
+
+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"])
+
+
+prompt = st.chat_input("Enter Your Question:")
+
+
+if prompt:
+
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    st.session_state.messages.append({"role":"user","content":prompt})
+    
+    reply= query_engine.query(prompt)
+    response = user_data(function_name=reply)
+
+    with st.chat_message("assistant"):
+        st.write_stream(response)
+    print("working!!")
+    st.session_state.messages.append({"role":"assistant","content":reply})