create app.py

app.py

@@ -0,0 +1,162 @@
+import streamlit as st
+from pypdf import PdfReader
+import torch
+from io import BytesIO
+from langchain.prompts import PromptTemplate
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+import textwrap
+
+from langchain.llms.huggingface_pipeline import HuggingFacePipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
+
+# load the environments
+from dotenv import load_dotenv
+load_dotenv()
+
+
+
+#DEFINE SOME VARIABLES
+
+CHUNK_SIZE = 1000
+# Using HuggingFaceEmbeddings with the chosen embedding model
+embeddings = HuggingFaceEmbeddings(
+    model_name="sentence-transformers/all-mpnet-base-v2",model_kwargs = {"device": "cuda"})
+
+
+
+# transformer model configuration
+quant_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+
+
+# CREATE A VECTOR DATABASE - FAISS
+def creat_vector_db(uploaded_pdfs) -> FAISS:
+    """Read multiple PDFs, split, embedd and store the embeddings on FAISS vector store"""
+
+    text = ""  
+    for pdf in uploaded_pdfs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE,
+                                                   chunk_overlap=100)
+    texts = text_splitter.split_text(text)
+    
+    vector_db = FAISS.from_texts(texts, embeddings) # create vector db for similarity search    
+    vector_db.save_local("faiss_index") # save the vector db to avoid repeated calls to it
+    return vector_db
+
+# LOAD LLM
+def load_llm():
+
+    model_id = "Deci/DeciLM-6b"
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(model_id, 
+                                            trust_remote_code=True, 
+                                            device_map = "auto",
+                                            quantization_config=quant_config)
+
+    pipe = pipeline("text-generation", 
+                    model=model,
+                    tokenizer=tokenizer,
+                    temperature=0.1,
+                    return_full_text = True, 
+                    max_new_tokens=40,
+                    repetition_penalty =  1.1)
+    
+    llm = HuggingFacePipeline(pipeline=pipe)
+
+    return llm
+
+
+
+# RESPONSE INSTRUCTIONS
+def set_custom_prompt():
+    """instructions, to the llm for text response generation"""
+
+
+    custom_prompt_template = """You have been given the following documents to answer the user's question.
+    If you do not have information from the information given to answer the questions just say 'I don't know the answer" and don't try to make up an answer.
+    Context: {context}
+    Question: {question}
+    Give a detailed helpful answer and nothing more.
+    Helpful answer:
+"""
+    prompt = PromptTemplate(template=custom_prompt_template, input_variables=[
+                            "context", "question"])
+    return prompt
+
+
+
+# QUESTION ANSWERING CHAIN
+def retrieval_qa_chain(prompt, vector_db):
+    """Chain to retrieve answers. the chain takes  the documents and
+    makes a call to the DeciLM-6b llm """
+
+    llm = load_llm()
+
+    qa_chain = RetrievalQA.from_chain_type(
+        llm = llm,
+        chain_type = "stuff",
+        retriever = vector_db.as_retriever(),
+        return_source_documents=True,
+        chain_type_kwargs={"prompt": prompt}
+    )
+
+    return qa_chain
+
+# QUESTION ANSWER BOT
+def qa_bot():  
+    vectore_db = FAISS.load_local("faiss_index", embeddings)  
+    conversation_prompt = set_custom_prompt()
+    conversation = retrieval_qa_chain(conversation_prompt, vectore_db)
+    return conversation
+
+# RESPONSE FROM BOT
+def bot_response(query):
+    conversation_result = qa_bot()
+    response = conversation_result({"query": query})
+    return response["result"]
+
+
+def main():
+    st.set_page_config(page_title="Multiple PDFs chat with DeciLM-6b and LangChain",
+                       page_icon=":file_folder:")
+
+    # page side panel
+    with st.sidebar:
+        st.subheader("Hello, welcome!")
+
+        pdfs = st.file_uploader(label="Upload your PDFs here and click Process!",
+                                accept_multiple_files=True)
+
+        if st.button("Process"):
+            with st.spinner("Processing file(s)..."):
+                # create a vectore store
+                creat_vector_db(pdfs)
+            st.write("Your files are Processed. You set to ask questions!")
+
+    st.header("Chat with Multiple PDFs using DeciLM-6b-instruct LLM")
+
+    # Query side
+    query = st.text_input(label="Type your question based on the PDFs",
+                          placeholder="Type question...")
+
+    if query:
+        st.write(f"Query: {query}")
+        st.text(textwrap.fill(bot_response(query), width=80))
+
+
+if __name__ == "__main__":
+    main()