Added streamlit app and requirements

app.py

@@ -0,0 +1,183 @@
+import tempfile
+import fitz
+import streamlit as st
+from langchain.chains import ConversationalRetrievalChain
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.llms import CTransformers
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from PIL import Image
+from streamlit_chat import message
+
+st.set_page_config(
+    page_title="AskBot",
+    page_icon=":robot_face:",
+    layout="wide"
+)
+
+st.sidebar.title("""
+                 Ask A Bot :robot_face: \n Talk with your PDFs
+                 """)
+
+st.sidebar.write("""
+                ###### A Q&A chatbot for you to talk with your PDFs.
+                ###### Upload the PDF you want to talk to and start asking questions. The display will show the page where the answer was found.
+                ###### When you upload a new PDF, the chat history is reset for you to start fresh.
+                ###### The chatbot is based on Langchain and the Llama language model, which is a large language model trained on the Common Crawl dataset. Obtained from [here](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML).
+                ###### The performance of this bot is limited due to its size. For better performance, a larger LLM should be used.
+                ###### :warning: Sometimes the Streamlit app will not re-run and refresh the PDF. If this happens, refresh the page.
+                ###### Developed by [Carlos Pereira](https://linkedin.com/in/carlos-miguel-pereira/).
+                """)
+
+if 'pdf_page' not in st.session_state:
+    st.session_state['pdf_page'] = 0
+
+if 'chat_history' not in st.session_state:
+    st.session_state['chat_history'] = []
+
+if 'generated' not in st.session_state:
+    st.session_state['generated'] = []
+
+if 'past' not in st.session_state:
+    st.session_state['past'] = []
+
+def update_state():
+    """
+    Reset state when a new PDF is uploaded
+    """
+    st.session_state.pdf_page = 0
+    st.session_state.chat_history = []
+    st.session_state['generated'] = []
+    st.session_state['past'] = []
+
+@st.cache_resource(show_spinner=False)
+def load_llm():
+    """
+    Load Llama LLM
+    """
+    llm_model = CTransformers(
+        model="llama-2-13b-chat.ggmlv3.q3_K_L.bin",
+        model_type="llama",
+        max_new_tokens=150,
+        temperature=0.2
+    )
+    return llm_model
+
+@st.cache_resource(show_spinner=False)
+def gen_embeddings():
+    """
+    Generate embeddings
+    """
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2",
+                                       model_kwargs={'device': 'cpu'})
+
+    return embeddings
+
+def load_pdf(file):
+    """
+    Load PDF and process for Search
+    """
+    # create tempfile to load pdf to PyPDFLoader
+    temp_file = tempfile.NamedTemporaryFile()
+    temp_file.write(file.getbuffer())
+    loader = PyPDFLoader(temp_file.name)
+    documents = loader.load()
+    pdf_file = fitz.open(temp_file.name)
+    temp_file.close()
+
+    # split doc into chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_documents(documents)
+
+    #get embedding model
+    embeddings = gen_embeddings()
+    pdf_search = Chroma.from_documents(texts, embeddings)
+
+    return pdf_search, pdf_file
+
+def generate_chain(pdf_vector, llm):
+    """
+    Generate Retrieval chain
+    """
+    chain = ConversationalRetrievalChain.from_llm(llm,
+                                chain_type="stuff",
+                                retriever=pdf_vector.as_retriever(search_kwargs={"k": 1}),
+                                return_source_documents=True)
+
+    return chain
+
+def get_answer(chain, query, chat_history):
+    """
+    Get an answer from the chain
+    """
+    result = chain({"question": query, 'chat_history': chat_history}, return_only_outputs=True)
+    answer = result["answer"]
+    # if you want history uncomment the line below
+    # st.session_state.chat_history += [(query, answer)]
+    st.session_state.pdf_page = list(result['source_documents'][0])[1][1]['page']
+
+    return answer
+
+def render_page_file(file, page):
+    """
+    Render page from PDF file
+    """
+    try:
+        page = file[page]
+    except: # todo: fix this exception handling
+        page = file[0]
+        st.session_state.pdf_page = 0
+
+    # Render the PDF page as an image
+    pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))
+    image = Image.frombytes('RGB', [pix.width, pix.height], pix.samples)
+
+    return image
+
+uploaded_file = st.file_uploader("Upload your PDF", type=["pdf"],
+                                 accept_multiple_files=False,
+                                 on_change=update_state)
+
+def app():
+    """
+    Main app
+    """
+    if uploaded_file:
+        # Load LLM
+        with st.spinner('Loading LLM...'):
+            llm = load_llm()
+        # Load and process the uploaded PDF file
+        with st.spinner('Loading PDF...'):
+            pdf_vector, pdf_file = load_pdf(uploaded_file)
+        with st.spinner('Generating chain...'):
+            chain = generate_chain(pdf_vector, llm)
+
+        col1, col2 = st.columns(2)
+        with col1:
+            # Question and answering
+            with st.form(key='question_form', clear_on_submit=True):
+                question = st.text_input('Enter your question:', value="", key='text_value')
+                submit_question = st.form_submit_button(label="Enter")
+
+            if submit_question:
+                with st.spinner('Getting answer...'):
+                    answer = get_answer(chain, question,
+                                    st.session_state.chat_history)
+                st.session_state.past.append(question)
+                st.session_state.generated.append(answer)
+
+            if st.session_state['generated']:
+                for i in range(len(st.session_state['generated'])-1, -1, -1):
+                    message(st.session_state["generated"][i], is_user=False,
+                            avatar_style="bottts", key=str(i))
+                    message(st.session_state['past'][i], is_user=True,
+                            avatar_style="adventurer", key=str(i) + '_user')
+
+        with col2:
+            # Render PDF page
+            if pdf_file:
+                st.image(render_page_file(pdf_file, st.session_state.pdf_page))
+
+if __name__ == "__main__":
+    app()

requirements.txt

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+chromadb==0.4.6
+ctransformers==0.2.22
+faiss-cpu==1.7.4
+langchain==0.0.266
+PyMuPDF==1.22.5
+pypdf==3.15.1
+sentence-transformers==2.2.2
+streamlit==1.25.0
+streamlit-chat==0.1.1