app.py

import streamlit as st
import requests
import logging
import os
from langchain_community.document_loaders import PDFPlumberLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.vectorstores import InMemoryVectorStore
from langchain.embeddings import HuggingFaceEmbeddings

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Page configuration
st.set_page_config(
    page_title="DeepSeek Chatbot with RAG - ruslanmv.com",
    page_icon="🤖",
    layout="centered"
)

# Initialize session state for chat history and vector store
if "messages" not in st.session_state:
    st.session_state.messages = []
if "vector_store" not in st.session_state:
    st.session_state.vector_store = None

# Set up PDF directory and embedding model
pdfs_directory = "./pdfs"
os.makedirs(pdfs_directory, exist_ok=True)
embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")

# Sidebar configuration
with st.sidebar:
    st.header("Model Configuration")
    st.markdown("[Get HuggingFace Token](https://huggingface.co/settings/tokens)")

    # Dropdown to select model
    model_options = [
        "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
    ]
    selected_model = st.selectbox("Select Model", model_options, index=0)

    system_message = st.text_area(
        "System Message",
        value="You are a helpful assistant created by ruslanmv.com. Use the provided context to answer questions clearly and concisely. If the answer isn't in the context, say you don't know.",
        height=100
    )

    max_tokens = st.slider("Max Tokens", 10, 4000, 100)
    temperature = st.slider("Temperature", 0.1, 4.0, 0.3)
    top_p = st.slider("Top-p", 0.1, 1.0, 0.6)

# Main interface
st.title("🤖 DeepSeek Chatbot with RAG")
st.caption("Powered by Hugging Face Inference API - Configure in sidebar")

# PDF upload section
uploaded_file = st.file_uploader(
    "Upload a PDF for context",
    type="pdf",
    accept_multiple_files=False
)

if uploaded_file:
    try:
        # Save uploaded PDF
        pdf_path = os.path.join(pdfs_directory, uploaded_file.name)
        with open(pdf_path, "wb") as f:
            f.write(uploaded_file.getbuffer())
        
        # Load and process PDF
        loader = PDFPlumberLoader(pdf_path)
        documents = loader.load()
        
        # Split text into chunks
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200
        )
        chunks = text_splitter.split_documents(documents)
        
        # Create and store vector store
        vector_store = InMemoryVectorStore.from_documents(chunks, embedding_model)
        st.session_state.vector_store = vector_store
        st.success("PDF processed and indexed successfully!")
    except Exception as e:
        st.error(f"Error processing PDF: {str(e)}")

# Display chat history
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Function to query Hugging Face API
def query(payload, api_url):
    headers = {"Authorization": f"Bearer {st.secrets['HF_TOKEN']}"}
    logger.info(f"Sending request to {api_url} with payload: {payload}")
    response = requests.post(api_url, headers=headers, json=payload)
    logger.info(f"Received response: {response.status_code}, {response.text}")
    try:
        return response.json()
    except requests.exceptions.JSONDecodeError:
        logger.error(f"Failed to decode JSON response: {response.text}")
        return None

# Handle user input
if prompt := st.chat_input("Type your message..."):
    st.session_state.messages.append({"role": "user", "content": prompt})
    
    with st.chat_message("user"):
        st.markdown(prompt)

    try:
        with st.spinner("Generating response..."):
            # Check if vector store is available
            if not st.session_state.vector_store:
                st.error("Please upload a PDF first to provide context.")
                st.stop()
            
            # Retrieve relevant documents
            vector_store = st.session_state.vector_store
            related_docs = vector_store.similarity_search(prompt, k=3)
            
            # Build context
            context = "\n\n".join([doc.page_content for doc in related_docs])
            
            # Prepare full prompt
            full_prompt = (
                f"{system_message}\n\n"
                f"Context: {context}\n\n"
                f"User: {prompt}\n"
                "Assistant:"
            )
            
            # Prepare API payload
            payload = {
                "inputs": full_prompt,
                "parameters": {
                    "max_new_tokens": max_tokens,
                    "temperature": temperature,
                    "top_p": top_p,
                    "return_full_text": False
                }
            }

            # Query API
            api_url = f"https://api-inference.huggingface.co/models/{selected_model}"
            output = query(payload, api_url)

            # Handle response
            if output and isinstance(output, list) and len(output) > 0:
                if 'generated_text' in output[0]:
                    assistant_response = output[0]['generated_text'].strip()
                    
                    with st.chat_message("assistant"):
                        st.markdown(assistant_response)
                    
                    st.session_state.messages.append({
                        "role": "assistant",
                        "content": assistant_response
                    })
                else:
                    st.error("Unexpected response format from the model")
            else:
                st.error("No response generated - please try again")
                
    except Exception as e:
        logger.error(f"Error: {str(e)}", exc_info=True)
        st.error(f"An error occurred: {str(e)}")