Create app.py

app.py

@@ -0,0 +1,162 @@
+import base64
+import os
+
+import streamlit as st
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import PDFMinerLoader
+from langchain.embeddings import SentenceTransformerEmbeddings
+from langchain.llms import HuggingFacePipeline
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from streamlit_chat import message
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+import torch
+
+st.set_page_config(layout="wide")
+
+def process_answer(instruction, qa_chain):
+    response = ''
+    generated_text = qa_chain.run(instruction)
+    return generated_text
+
+
+def get_file_size(file):
+    file.seek(0, os.SEEK_END)
+    file_size = file.tell()
+    file.seek(0)
+    return file_size
+
+
+@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)
+    splits = text_splitter.split_documents(documents)
+
+    # create embeddings here
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    vectordb = FAISS.from_documents(splits, embeddings)
+    vectordb.save_local("faiss_index")
+
+
+@st.cache_resource
+def initialize_qa_chain(selected_model):
+    # Constants
+    CHECKPOINT = selected_model
+    TOKENIZER = AutoTokenizer.from_pretrained(CHECKPOINT)
+    BASE_MODEL = AutoModelForSeq2SeqLM.from_pretrained(CHECKPOINT, device_map=torch.device('cpu'), torch_dtype=torch.float32)
+    pipe = pipeline(
+        'text2text-generation',
+        model=BASE_MODEL,
+        tokenizer=TOKENIZER,
+        max_length=256,
+        do_sample=True,
+        temperature=0.3,
+        top_p=0.95,
+        # device=torch.device('cpu')
+    )
+
+    llm = HuggingFacePipeline(pipeline=pipe)
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+
+    vectordb = FAISS.load_local("faiss_index", embeddings)
+
+    # Build a QA chain
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vectordb.as_retriever(),
+    )
+    return qa_chain
+
+
+@st.cache_data
+# function to display the PDF of a given file
+def display_pdf(file):
+    try:
+        # 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)
+    except Exception as e:
+        st.error(f"An error occurred while displaying the PDF: {e}")
+
+
+# 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=f"{i}_user")
+        message(history["generated"][i], key=str(i))
+
+
+def main():
+     # Add a sidebar for model selection
+    model_options = ["MBZUAI/LaMini-T5-738M", "google/flan-t5-base", "google/flan-t5-small"]
+    selected_model = st.sidebar.selectbox("Select Model", model_options)
+    
+    st.markdown("<h1 style='text-align: center; color: blue;'>Custom PDF Chatbot 🦜📄 </h1>", unsafe_allow_html=True)
+    st.markdown("<h2 style='text-align: center; color:red;'>Upload your PDF, and Ask Questions 👇</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)
+        }
+        os.makedirs("docs", exist_ok=True)
+        filepath = os.path.join("docs", uploaded_file.name)
+        try:
+            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 = display_pdf(filepath)
+
+            with col2:
+                st.success(f'model selected successfully: {selected_model}')
+                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}, initialize_qa_chain(selected_model))
+                    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)
+
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+
+
+if __name__ == "__main__":
+    main()