Create app.py

app.py

@@ -0,0 +1,122 @@
+import torch
+import subprocess
+import streamlit as st
+from run_localGPT import load_model
+from langchain.vectorstores import Chroma
+from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.chains import RetrievalQA
+from streamlit_extras.add_vertical_space import add_vertical_space
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferMemory
+
+
+def model_memory():
+    # Adding history to the model.
+    template = """Use the following pieces of context to answer the question at the end. If you don't know the answer,\
+    just say that you don't know, don't try to make up an answer.
+
+    {context}
+
+    {history}
+    Question: {question}
+    Helpful Answer:"""
+
+    prompt = PromptTemplate(input_variables=["history", "context", "question"], template=template)
+    memory = ConversationBufferMemory(input_key="question", memory_key="history")
+
+    return prompt, memory
+
+
+# Sidebar contents
+with st.sidebar:
+    st.title("🤗💬 Converse with your Data")
+    st.markdown(
+        """
+    ## About
+    This app is an LLM-powered chatbot built using:
+    - [Streamlit](https://streamlit.io/)
+    - [LangChain](https://python.langchain.com/)
+    - [LocalGPT](https://github.com/PromtEngineer/localGPT) 
+ 
+    """
+    )
+    add_vertical_space(5)
+    st.write("Made with ❤️ by [Prompt Engineer](https://youtube.com/@engineerprompt)")
+
+
+if torch.backends.mps.is_available():
+    DEVICE_TYPE = "mps"
+elif torch.cuda.is_available():
+    DEVICE_TYPE = "cuda"
+else:
+    DEVICE_TYPE = "cpu"
+
+
+# if "result" not in st.session_state:
+#     # Run the document ingestion process.
+#     run_langest_commands = ["python", "ingest.py"]
+#     run_langest_commands.append("--device_type")
+#     run_langest_commands.append(DEVICE_TYPE)
+
+#     result = subprocess.run(run_langest_commands, capture_output=True)
+#     st.session_state.result = result
+
+# Define the retreiver
+# load the vectorstore
+if "EMBEDDINGS" not in st.session_state:
+    EMBEDDINGS = HuggingFaceInstructEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs={"device": DEVICE_TYPE})
+    st.session_state.EMBEDDINGS = EMBEDDINGS
+
+if "DB" not in st.session_state:
+    DB = Chroma(
+        persist_directory=PERSIST_DIRECTORY,
+        embedding_function=st.session_state.EMBEDDINGS,
+        client_settings=CHROMA_SETTINGS,
+    )
+    st.session_state.DB = DB
+
+if "RETRIEVER" not in st.session_state:
+    RETRIEVER = DB.as_retriever()
+    st.session_state.RETRIEVER = RETRIEVER
+
+if "LLM" not in st.session_state:
+    LLM = load_model(device_type=DEVICE_TYPE, model_id=MODEL_ID, model_basename=MODEL_BASENAME)
+    st.session_state["LLM"] = LLM
+
+
+if "QA" not in st.session_state:
+    prompt, memory = model_memory()
+
+    QA = RetrievalQA.from_chain_type(
+        llm=LLM,
+        chain_type="stuff",
+        retriever=RETRIEVER,
+        return_source_documents=True,
+        chain_type_kwargs={"prompt": prompt, "memory": memory},
+    )
+    st.session_state["QA"] = QA
+
+st.title("LocalGPT App 💬")
+# Create a text input box for the user
+prompt = st.text_input("Input your prompt here")
+# while True:
+
+# If the user hits enter
+if prompt:
+    # Then pass the prompt to the LLM
+    response = st.session_state["QA"](prompt)
+    answer, docs = response["result"], response["source_documents"]
+    # ...and write it out to the screen
+    st.write(answer)
+
+    # With a streamlit expander
+    with st.expander("Document Similarity Search"):
+        # Find the relevant pages
+        search = st.session_state.DB.similarity_search_with_score(prompt)
+        # Write out the first
+        for i, doc in enumerate(search):
+            # print(doc)
+            st.write(f"Source Document # {i+1} : {doc[0].metadata['source'].split('/')[-1]}")
+            st.write(doc[0].page_content)
+            st.write("--------------------------------")