Added app.py

app.py

@@ -0,0 +1,109 @@
+import streamlit as st
+# import fitz  # PyMuPDF for extracting text from PDFs
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.docstore.document import Document
+from langchain.llms import HuggingFacePipeline
+from langchain.chains import RetrievalQA
+from transformers import AutoConfig, AutoTokenizer, pipeline, AutoModelForCausalLM
+import torch
+import re
+import transformers
+from torch import bfloat16
+from langchain_community.document_loaders import DirectoryLoader
+
+# Initialize embeddings and ChromaDB
+model_name = "sentence-transformers/all-mpnet-base-v2"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_kwargs = {"device": device}
+embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
+
+loader = DirectoryLoader('./pdf', glob="**/*.pdf", use_multithreading=True)
+docs = loader.load()
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+all_splits = text_splitter.split_documents(docs)
+vectordb = Chroma.from_documents(documents=all_splits, embedding=embeddings, persist_directory="pdf_db")
+books_db = Chroma(persist_directory="./pdf_db", embedding_function=embeddings)
+
+books_db_client = books_db.as_retriever()
+
+# Initialize the model and tokenizer
+model_name = "stabilityai/stablelm-zephyr-3b"
+
+bnb_config = transformers.BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model_config = transformers.AutoConfig.from_pretrained(model_name, max_new_tokens=1024)
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    config=model_config,
+    quantization_config=bnb_config,
+    device_map=device,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+query_pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    return_full_text=True,
+    torch_dtype=torch.float16,
+    device_map=device,
+    temperature=0.7,
+    top_p=0.9,
+    top_k=50,
+    max_new_tokens=256
+)
+
+llm = HuggingFacePipeline(pipeline=query_pipeline)
+
+books_db_client_retriever = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=books_db_client,
+    verbose=True
+)
+
+st.title("RAG System with ChromaDB")
+
+# Initialize session state for tracking previous questions and answers
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+# Function to retrieve answer using the RAG system
+def test_rag(qa, query):
+    return qa.run(query)
+
+query = st.text_input("Enter your question:")
+
+if st.button("Submit"):
+    if query:
+        # Get the answer from RAG
+        books_retriever = test_rag(books_db_client_retriever, query)
+
+        # Extracting the relevant answer using regex
+        corrected_text_match = re.search(r"Helpful Answer:(.*)", books_retriever, re.DOTALL)
+        
+        if corrected_text_match:
+            corrected_text_books = corrected_text_match.group(1).strip()
+        else:
+            corrected_text_books = "No helpful answer found."
+
+        # Store the query and answer in session state
+        st.session_state.history.append({"question": query, "answer": corrected_text_books})
+
+# Display previous questions and answers
+if st.session_state.history:
+    # st.write("### Previous Questions and Answers")
+    for idx, item in enumerate(st.session_state.history):
+        st.write(f"**Question:** {item['question']}")
+        st.write(f"**Answer:** {item['answer']}")
+        st.write("---")
+