Update app.py

app.py

@@ -1,4 +1,97 @@
+from langchain.llms import OpenAI
+from langchain.agents import AgentType, initialize_agent, load_tools
+from langchain.callbacks import StreamlitCallbackHandler
 import streamlit as st
+from langchain_community.llms import LlamaCpp
+from langchain_community.tools import HumanInputRun
+from langchain_community.llms import Ollama
+from langchain.agents import AgentExecutor, create_react_agent
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+#from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext  #Vector store index is for indexing the vector
+#from llama_index.llms.huggingface import HuggingFaceLLM
+from langchain_huggingface import HuggingFaceEmbeddings
+# from langchain_huggingface import HuggingFaceEmbeddings
+# from langchain_community.embeddings import HuggingFaceInstructEmbeddings,HuggingFaceEmbeddings
+#from llama_index.core import ServiceContext,Settings
+# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+#from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+import streamlit as st
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.llms import OpenAI
+from langchain.chains import RetrievalQA
+
+from langchain.chat_models import ChatOpenAI
+from langchain.llms import OpenAI
+from langchain.agents import load_tools, initialize_agent, Tool
+from langchain.tools import HumanInputRun
+from langchain.agents import AgentType
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain_ollama.llms import OllamaLLM
+from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler
+from huggingface_hub import snapshot_download
+from langchain import hub
+import os 
+def get_input() -> str:
+    if prompt := st.chat_input():
+        return prompt
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+download = True
+for file_name in os.listdir("/home/user/app"):
+    if "llama-2-7b-chat.Q5_K_S.gguf" in file_name:
+        download=False
+if download:
+    snapshot_download(repo_id="TheBloke/Llama-2-7B-Chat-GGUF", allow_patterns="*.Q5_K_S.gguf",local_dir="/home/user/app")
+
+llm = LlamaCpp(
+            model_path="/home/user/app/llama-2-7b-chat.Q5_K_S.gguf",
+            n_gpu_layers=-1,
+            n_batch=512,
+            n_ctx=4096,
+            callback_manager=callback_manager,
+            verbose=True,  # Verbose is required to pass to the callback manager
+        )
+# llm = OpenAI(temperature=0, streaming=True)
+embeddings= HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5")
+documents = PyPDFDirectoryLoader("/home/user/app").load()
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+texts = text_splitter.split_documents(documents)
+
+
+db_pdf = FAISS.from_documents(texts, embeddings)
+db_pdf.save_local("db_pdf")
+print("whats happenings ")
+
+# Creating retrieval QA chains
+
+db_pdf_retriever = RetrievalQA.from_chain_type(
+    llm=llm, chain_type="stuff", retriever=db_pdf.as_retriever()
+)
+db_pdf_tool = Tool(
+    name="intellify hr policies tool",
+    func=db_pdf_retriever.run,
+    description="useful for when you want to answer any questions on the intellify hr policies.",
+    return_direct=True
+)
+
+human_input = HumanInputRun(input_func=get_input)
+
+tools = [
+    db_pdf_tool,
+    human_input
+]
+prompt  = hub.pull("hwchase17/react")
+agent = create_react_agent(llm, tools, prompt)
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True,handle_parsing_errors=True)
+
+# tools = load_tools(["human", ], llm=llm, input_func=get_input)
+# agent = initialize_agent(
+#     tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
+# )
+
+with st.chat_message("assistant"):
+    st_callback = StreamlitCallbackHandler(st.container())
+    response = agent_executor.invoke(callbacks=[st_callback])
+    st.write(response)