Upload app.py

app.py

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+import os 
+import streamlit as st  
+import re 
+from tempfile import NamedTemporaryFile
+import time
+import pathlib
+#from PyPDF2 import PdfReader
+
+from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+from langchain_community.llms import LlamaCpp
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.chains import RetrievalQA
+from langchain_community.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
+from langchain_community.document_loaders import TextLoader
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory.chat_message_histories.streamlit import StreamlitChatMessageHistory
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.llms import HuggingFaceHub
+
+
+# sidebar contents
+with st.sidebar:
+	st.title('DOC-QA DEMO ')
+	st.markdown('''
+	## About 	
+	Detail this application:
+	- LLM model: Phi-2-4bit 
+	- Hardware resource : Huggingface space 8 vCPU 32 GB 
+	''')
+	
+def split_docs(documents,chunk_size=1000):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=200)
+    sp_docs = text_splitter.split_documents(documents)
+    return sp_docs
+
+@st.cache_resource
+def load_llama2_llamaCpp():
+    core_model_name = "phi-2.Q4_K_M.gguf"
+    #n_gpu_layers = 32
+    n_batch = 512
+    callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+    llm = LlamaCpp(
+        model_path=core_model_name,
+        #n_gpu_layers=n_gpu_layers,
+        n_batch=n_batch,
+        callback_manager=callback_manager,
+        verbose=True,n_ctx = 4096, temperature = 0.1, max_tokens = 128
+    )
+    return llm
+
+def set_custom_prompt():
+    custom_prompt_template = """ Use the following pieces of information from context to answer the user's question.
+    If you don't know the answer, don't try to make up an answer.
+    Context : {context}
+    Question : {question}
+    Please answer the questions in a concise and straightforward manner. 
+    Helpful answer:
+    """
+    prompt = PromptTemplate(template=custom_prompt_template, input_variables=['context',
+                                                                              'question',
+                                                                              ])
+    return prompt
+
+
+@st.cache_resource
+def load_embeddings():
+    embeddings = HuggingFaceEmbeddings(model_name = "thenlper/gte-base",
+                                       model_kwargs = {'device': 'cpu'})
+    return embeddings
+
+
+
+def main():
+    data = []
+    sp_docs_list = []
+    msgs = StreamlitChatMessageHistory(key="langchain_messages")
+    print(msgs)
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # repo_id = "mistralai/Mistral-7B-Instruct-v0.2" 
+    # llm = HuggingFaceHub(
+    # repo_id=repo_id, model_kwargs={"temperature": 0.1, "max_length": 128})
+
+    llm = load_llama2_llamaCpp()
+    qa_prompt = set_custom_prompt()
+    embeddings = load_embeddings()
+
+    uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
+    if uploaded_file is not None :
+        with NamedTemporaryFile(dir='PDF', suffix='.pdf', delete=False) as f:
+            f.write(uploaded_file.getbuffer())
+            print(f.name)
+            #filename = f.name
+            loader = PyPDFLoader(f.name)
+            pages = loader.load_and_split()
+            data.extend(pages)
+            #st.write(pages)
+            f.close()
+            os.unlink(f.name)
+            os.path.exists(f.name)
+    if len(data) > 0 :
+        embeddings = load_embeddings()
+        sp_docs = split_docs(documents = data)
+        st.write(f"This document have {len(sp_docs)} chunks")
+        sp_docs_list.extend(sp_docs)
+    try:
+        db = FAISS.from_documents(sp_docs_list, embeddings)
+        memory = ConversationBufferMemory(memory_key="chat_history", 
+                                  return_messages=True, 
+                                  input_key="query", 
+                                  output_key="result")
+        qa_chain = RetrievalQA.from_chain_type(
+            llm = llm,
+            chain_type = "stuff",
+            retriever = db.as_retriever(search_kwargs = {'k':3}), 
+            return_source_documents = True,
+            memory = memory,
+            chain_type_kwargs = {"prompt":qa_prompt})
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+    
+            # Accept user input
+        if query := st.chat_input("What is up?"):
+            # Display user message in chat message container
+            with st.chat_message("user"):
+                st.markdown(query)
+            # Add user message to chat history
+            st.session_state.messages.append({"role": "user", "content": query})
+    
+            start = time.time()
+    
+            response = qa_chain({'query': query})
+            
+            with st.chat_message("assistant"):
+                st.markdown(response['result'])
+            
+            end = time.time()
+            st.write("Respone time:",int(end-start),"sec")
+            print(response)
+                    
+            # Add assistant response to chat history
+            st.session_state.messages.append({"role": "assistant", "content": response['result']})
+    
+            with st.expander("See the related documents"):
+                for count, url in enumerate(response['source_documents']):
+                    st.write(str(count+1)+":", url)
+    
+        clear_button = st.button("Start new convo")
+        if clear_button :
+            st.session_state.messages = []
+            qa_chain.memory.chat_memory.clear() 
+
+    except:
+        st.write("Plaese upload your pdf file.")
+
+
+if __name__ == '__main__':
+	main()