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app.py

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+import streamlit as st 
+import os
+import base64
+import time
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM 
+from transformers import pipeline
+import torch 
+import textwrap 
+from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader 
+from langchain.text_splitter import RecursiveCharacterTextSplitter 
+from langchain.embeddings import SentenceTransformerEmbeddings 
+from langchain.vectorstores import Chroma 
+from langchain.llms import HuggingFacePipeline
+from langchain.chains import RetrievalQA 
+from constants import CHROMA_SETTINGS
+from streamlit_chat import message
+
+st.set_page_config(layout="wide")
+
+checkpoint = "LaMini-T5-738M"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+base_model = AutoModelForSeq2SeqLM.from_pretrained(
+    checkpoint,
+    device_map="auto",
+    torch_dtype = torch.float32
+)
+
+persist_directory = "db"
+
+@st.cache_resource
+def data_ingestion():
+    for root, dirs, files in os.walk("docs"):
+        for file in files:
+            if file.endswith(".pdf"):
+                print(file)
+                loader = PDFMinerLoader(os.path.join(root, file))
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=500)
+    texts = text_splitter.split_documents(documents)
+    #create embeddings here
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    #create vector store here
+    db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
+    db.persist()
+    db=None 
+
+@st.cache_resource
+def llm_pipeline():
+    pipe = pipeline(
+        'text2text-generation',
+        model = base_model,
+        tokenizer = tokenizer,
+        max_length = 256,
+        do_sample = True,
+        temperature = 0.3,
+        top_p= 0.95
+    )
+    local_llm = HuggingFacePipeline(pipeline=pipe)
+    return local_llm
+
+@st.cache_resource
+def qa_llm():
+    llm = llm_pipeline()
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    db = Chroma(persist_directory="db", embedding_function = embeddings, client_settings=CHROMA_SETTINGS)
+    retriever = db.as_retriever()
+    qa = RetrievalQA.from_chain_type(
+        llm = llm,
+        chain_type = "stuff",
+        retriever = retriever,
+        return_source_documents=True
+    )
+    return qa
+
+def process_answer(instruction):
+    response = ''
+    instruction = instruction
+    qa = qa_llm()
+    generated_text = qa(instruction)
+    answer = generated_text['result']
+    return answer
+
+def get_file_size(file):
+    file.seek(0, os.SEEK_END)
+    file_size = file.tell()
+    file.seek(0)
+    return file_size
+
+@st.cache_data
+#function to display the PDF of a given file 
+def displayPDF(file):
+    # Opening file from file path
+    with open(file, "rb") as f:
+        base64_pdf = base64.b64encode(f.read()).decode('utf-8')
+
+    # Embedding PDF in HTML
+    pdf_display = F'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
+
+    # Displaying File
+    st.markdown(pdf_display, unsafe_allow_html=True)
+
+# Display conversation history using Streamlit messages
+def display_conversation(history):
+    for i in range(len(history["generated"])):
+        message(history["past"][i], is_user=True, key=str(i) + "_user")
+        message(history["generated"][i],key=str(i))
+
+def main():
+    st.markdown("<h1 style='text-align: center; color: blue;'>ChatPDFv2</h1>", unsafe_allow_html=True)
+
+    st.markdown("<h2 style='text-align: center; color:red;'>Upload your PDF</h2>", unsafe_allow_html=True)
+
+    uploaded_file = st.file_uploader("", type=["pdf"])
+
+    if uploaded_file is not None:
+        file_details = {
+            "Filename": uploaded_file.name,
+            "File size": get_file_size(uploaded_file)
+        }
+        filepath = "docs/"+uploaded_file.name
+        with open(filepath, "wb") as temp_file:
+                temp_file.write(uploaded_file.read())
+
+        col1, col2= st.columns([1,2])
+        with col1:
+            st.markdown("<h4 style color:black;'>File details</h4>", unsafe_allow_html=True)
+            st.json(file_details)
+            st.markdown("<h4 style color:black;'>File preview</h4>", unsafe_allow_html=True)
+            pdf_view = displayPDF(filepath)
+
+        with col2:
+            with st.spinner('Embeddings are in process...'):
+                ingested_data = data_ingestion()
+            st.success('Embeddings are created successfully!')
+            st.markdown("<h4 style color:black;'>Chat Here</h4>", unsafe_allow_html=True)
+
+
+            user_input = st.text_input("", key="input")
+
+            # Initialize session state for generated responses and past messages
+            if "generated" not in st.session_state:
+                st.session_state["generated"] = ["I am ready to help you"]
+            if "past" not in st.session_state:
+                st.session_state["past"] = ["Hey there!"]
+                
+            # Search the database for a response based on user input and update session state
+            if user_input:
+                answer = process_answer({'query': user_input})
+                st.session_state["past"].append(user_input)
+                response = answer
+                st.session_state["generated"].append(response)
+
+            # Display conversation history using Streamlit messages
+            if st.session_state["generated"]:
+                display_conversation(st.session_state)
+        
+
+        
+
+
+
+
+if __name__ == "__main__":
+    main()
+

constants.py

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+import os 
+from chromadb.config import Settings 
+
+#Define the chroma settings 
+CHROMA_SETTINGS = Settings(
+    chroma_db_impl = 'duckdb+parquet',
+    persist_directory = "db",
+    anonymized_telemetry = False
+)

ingest.py

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+from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader 
+from langchain.text_splitter import RecursiveCharacterTextSplitter 
+from langchain.embeddings import SentenceTransformerEmbeddings 
+from langchain.vectorstores import Chroma 
+import os 
+from constants import CHROMA_SETTINGS
+
+persist_directory = "db"
+
+def main():
+    for root, dirs, files in os.walk("docs"):
+        for file in files:
+            if file.endswith(".pdf"):
+                            print(file)
+                            loader = PDFMinerLoader(os.path.join(root, file))
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=500)
+    texts = text_splitter.split_documents(documents)
+    #create embeddings here
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    #create vector store here
+    db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
+    db.persist()
+    db=None 
+
+if __name__ == "__main__":
+    main()