Upload app.py

app.py

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+import streamlit as st
+from streamlit_lottie import st_lottie
+import fitz  # PyMuPDF
+import requests
+import os, shutil
+import llm_model
+
+
+SYSTEM_PROMPT = [
+    """
+    You are not Mistral AI, but rather a Q&A bot trained by Krishna Kumar while building a cool side project based on RAG. Whenever asked, you need to answer as Q&A bot.
+    """,
+    """You are a RAG based Document Q&A bot. Based on the input prompt and retrieved context from the vector database you will answer questions that are closer to the context. 
+    If no context was found then, say "I don't know" instead of making up answer on your own. Follow above rules strictly.
+    """
+]
+
+
+@st.cache_data(experimental_allow_widgets=True)
+def index_document(_llm_object, uploaded_file):
+
+    if uploaded_file is not None:
+        # Specify the folder path where you want to store the uploaded file in the 'assets' folder
+        assets_folder = "assets/uploaded_files"
+        if not os.path.exists(assets_folder):
+            os.makedirs(assets_folder)
+
+        # Save the uploaded file to the specified folder
+        file_path = os.path.join(assets_folder, uploaded_file.name)
+        with open(file_path, "wb") as f:
+            f.write(uploaded_file.getvalue())
+
+        file_name = os.path.join(assets_folder, uploaded_file.name)
+        st.success(f"File '{file_name}' uploaded !")
+
+        with st.spinner("Indexing document... This is a free CPU version and may take a while ⏳"):
+            retriever = _llm_object.create_vector_db(file_name)
+                        
+        return file_name, retriever
+    else:
+        return None, None
+
+
+def load_lottieurl(url: str):
+    r = requests.get(url)
+    if r.status_code != 200:
+        return None
+    return r.json()
+
+
+def is_query_valid(query: str) -> bool:
+    if not query:
+        st.error("Please enter a question!")
+        return False
+    return True
+
+def init_state() :
+    if "filename" not in st.session_state:
+        st.session_state.filename = None
+        
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    if "temp" not in st.session_state:
+        st.session_state.temp = 0.7
+
+    if "history" not in st.session_state:
+        st.session_state.history = [SYSTEM_PROMPT]
+
+    if "repetion_penalty" not in st.session_state:
+        st.session_state.repetion_penalty = 1
+
+    if "chat_bot" not in st.session_state:
+        st.session_state.chat_bot = "Mixtral-8x7B-Instruct-v0.1"
+       
+        
+
+def faq():
+    st.markdown(
+        """
+        # FAQ
+        ## How does Document Q&A Bot work?
+        When you upload a document (in Pdf, word, csv or txt format), it will be divided into smaller chunks 
+        and stored in a special type of database called a vector index 
+        that allows for semantic search and retrieval.
+
+        When you ask a question, our Q&A bot will first look through the document chunks and find the
+        most relevant ones using the vector index. This acts as a context to our custom prompt which will be feed to the LLM model.
+        If the context was not found in the document then, LLM will reply 'I don't know'
+
+        ## Is my data safe?
+        Yes, your data is safe. Our bot does not store your documents or
+        questions. All uploaded data is deleted after you close the browser tab.
+
+        ## Why does it take so long to index my document?
+        Since, this is a sample QA bot project that uses open-source model
+        and doesn't have much resource capabilities like GPU, it may take time 
+        to index your document based on the size of the document.
+
+        ## Are the answers 100% accurate?
+        No, the answers are not 100% accurate. 
+        But for most use cases, our QA bot is very accurate and can answer
+        most questions. Always check with the sources to make sure that the answers
+        are correct.
+        """
+    )
+
+
+def sidebar():
+    with st.sidebar:
+        st.markdown("## Document Q&A Bot")
+        st.write("LLM: Mixtral-8x7B-Instruct-v0.1")
+        #st.success('API key already provided!', icon='✅')
+               
+        st.markdown("### Set Model Parameters")
+        # select LLM model
+        st.session_state.model_name = 'Mixtral-8x7B-Instruct-v0.1'
+        # set model temperature
+        st.session_state.temperature = st.slider(label="Temperature", min_value=0.0, max_value=1.0, step=0.1, value=0.7)
+        st.session_state.top_p = st.slider(label="Top Probablity", min_value=0.0, max_value=1.0, step=0.1, value=0.95)
+        st.session_state.repetition_penalty = st.slider(label="Repetition Penalty", min_value=0.0, max_value=1.0, step=0.1, value=1.0)
+        
+        # load model parameters
+        st.session_state.llm_object = load_model()
+        st.markdown("---")
+        # Upload file through Streamlit
+        st.session_state.uploaded_file = st.file_uploader("Upload a file", type=["pdf", "doc", "docx", "txt"])
+        _, retriever = index_document(st.session_state.llm_object, st.session_state.uploaded_file)
+        
+        st.markdown("---")
+        st.markdown("# About")
+        st.markdown(
+            """QA bot 🤖 allows you to ask questions about your 
+            documents and get accurate answers with citations."""
+        )
+
+        st.markdown("Created with ❤️ by Krishna Kumar Yadav")
+        st.markdown(
+            """
+            - [Email](mailto:krishna158@live.com)
+            - [LinkedIn](https://www.linkedin.com/in/krishna-kumar-yadav-726831105/)
+            - [Github](https://github.com/krish-yadav23)
+            - [LeetCode](https://leetcode.com/KrishnaKumar23/)
+            """
+        )
+
+        faq()
+        return retriever
+
+
+def chat_box() :
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+            
+
+def generate_chat_stream(prompt, retriever) :
+    
+    with st.spinner("Fetching relevant answers from source document..."):
+        response, sources = st.session_state.llm_object.mixtral_chat_inference(prompt, st.session_state.history, st.session_state.temperature, 
+                                                                               st.session_state.top_p, st.session_state.repetition_penalty, retriever)
+    
+        
+    return response, sources
+
+def stream_handler(chat_stream, placeholder) :
+    full_response = ''
+
+    for chunk in chat_stream :
+        if chunk.token.text!='</s>' :
+            full_response += chunk.token.text
+            placeholder.markdown(full_response + "▌")
+    placeholder.markdown(full_response)
+
+    return full_response
+
+def show_source(sources) :
+    with st.expander("Show source") :
+        for source in sources:
+            st.info(f"{source}")
+            
+
+# Function to load model parameters
+@st.cache_resource()
+def load_model():
+    # create llm object
+    return llm_model.LlmModel()
+
+st.set_page_config(page_title="Document QA Bot")
+#lottie_book = load_lottieurl("https://assets4.lottiefiles.com/temp/lf20_aKAfIn.json")
+#st_lottie(lottie_book, speed=1, height=200, key="initial")
+# Place the title below the Lottie animation
+st.title("Document Q&A Bot 🤖")
+
+# initialize session state for streamlit app
+init_state()
+# Left Sidebar
+retriever = sidebar()
+chat_box()
+
+if prompt := st.chat_input("Ask a question about your document!"):
+    st.chat_message("user").markdown(prompt)
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    try:
+        chat_stream, sources = generate_chat_stream(prompt, retriever)
+  
+        with st.chat_message("assistant"):
+            placeholder = st.empty()
+            full_response = stream_handler(chat_stream, placeholder)
+            show_source(sources)
+
+        st.session_state.history.append([prompt, full_response])
+        st.session_state.messages.append({"role": "assistant", "content": full_response})
+    except Exception as e:
+        if not st.session_state.uploaded_file:
+            st.error("Kindly provide the document file by uploading it before posing any questions. Your cooperation is appreciated!")
+        else:
+            st.error(e)
+