app.py

import streamlit as st
import os
from openai import OpenAI
import tempfile
from langchain.chains import ConversationalRetrievalChain
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_community.document_loaders import (
    PyPDFLoader, 
    TextLoader, 
    CSVLoader
)
from datetime import datetime
from pydub import AudioSegment
import pytz

from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.chains import ConversationalRetrievalChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_community.document_loaders import PyPDFLoader, TextLoader, CSVLoader
import os
import tempfile
from datetime import datetime
import pytz


class DocumentRAG:
    def __init__(self):
        self.document_store = None
        self.qa_chain = None
        self.document_summary = ""
        self.chat_history = []
        self.last_processed_time = None
        self.api_key = os.getenv("OPENAI_API_KEY")  # Fetch the API key from environment variable
        self.init_time = datetime.now(pytz.UTC)

        if not self.api_key:
            raise ValueError("API Key not found. Make sure to set the 'OPENAI_API_KEY' environment variable.")

        # Persistent directory for Chroma to avoid tenant-related errors
        self.chroma_persist_dir = "./chroma_storage"
        os.makedirs(self.chroma_persist_dir, exist_ok=True)

    def process_documents(self, uploaded_files, embedding_choice):
        """Process uploaded files by saving them temporarily and extracting content."""
        if not self.api_key:
            return "Please set the OpenAI API key in the environment variables."
        if not uploaded_files:
            return "Please upload documents first."

        try:
            documents = []
            for uploaded_file in uploaded_files:
                # Save uploaded file to a temporary location
                temp_file_path = tempfile.NamedTemporaryFile(
                    delete=False, suffix=os.path.splitext(uploaded_file.name)[1]
                ).name
                with open(temp_file_path, "wb") as temp_file:
                    temp_file.write(uploaded_file.read())

                # Determine the loader based on the file type
                if temp_file_path.endswith('.pdf'):
                    loader = PyPDFLoader(temp_file_path)
                elif temp_file_path.endswith('.txt'):
                    loader = TextLoader(temp_file_path)
                elif temp_file_path.endswith('.csv'):
                    loader = CSVLoader(temp_file_path)
                else:
                    return f"Unsupported file type: {uploaded_file.name}"

                # Load the documents
                try:
                    documents.extend(loader.load())
                except Exception as e:
                    return f"Error loading {uploaded_file.name}: {str(e)}"

            if not documents:
                return "No valid documents were processed. Please check your files."

            # Split text for better processing
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=1000,
                chunk_overlap=200,
                length_function=len
            )
            documents = text_splitter.split_documents(documents)

            # Combine text for later summary generation
            self.document_text = " ".join([doc.page_content for doc in documents])  # Store for later use

            # Create embeddings and initialize retrieval chain
            embeddings = OpenAIEmbeddings(api_key=self.api_key)


            if embedding_choice == "OpenAI Embeddings":
                embeddings = OpenAIEmbeddings(api_key=self.api_key)
            else:
                embeddings = SentenceTransformerEmbeddings(model_name="NeuML/pubmedbert-base-embeddings")


            
            self.document_store = Chroma.from_documents(
                documents,
                embeddings,
                persist_directory=self.chroma_persist_dir  # Persistent directory for Chroma
            )

            self.qa_chain = ConversationalRetrievalChain.from_llm(
                ChatOpenAI(temperature=0, model_name='gpt-4', api_key=self.api_key),
                self.document_store.as_retriever(search_kwargs={'k': 6}),
                return_source_documents=True,
                verbose=False
            )

            self.last_processed_time = datetime.now(pytz.UTC)
            return "Documents processed successfully!"
        except Exception as e:
            return f"Error processing documents: {str(e)}"

    def generate_summary(self, text, language):
        """Generate a clinically relevant summary in the specified language."""
        if not self.api_key:
            return "API Key not set. Please set it in the environment variables."
        try:
            client = OpenAI(api_key=self.api_key)
            response = client.chat.completions.create(
                model="gpt-4",
                messages=[
                    {
                        "role": "system",
                        "content": f"""
You are a multilingual clinical AI assistant. Summarize the following medical document (e.g., discharge summary, progress note, or diagnostic report) in **{language}**, preserving all **critical medical information**.

Please ensure the summary includes:
- Patient history (if available)
- Current diagnosis and relevant symptoms
- Medications and treatments administered
- Investigations and results (if mentioned)
- Any follow-up instructions or discharge plans

Use clear, concise language suitable for healthcare professionals. Maintain clinical accuracy and do not hallucinate. Aim for a structured summary under 300 words.
                        """
                    },
                    {
                        "role": "user",
                        "content": text[4000]
                    }
                ],
                temperature=0.3
            )
            return response.choices[0].message.content
        except Exception as e:
            return f"Error generating summary: {str(e)}"



    def handle_query(self, question, history, language):
        """Handle user queries in the specified language."""
        if not self.qa_chain:
            return history + [("System", "Please process the documents first.")]
        try:
            preface = (
            f"Instruction: Respond in {language}. Be professional and concise, "
            f"keeping the response under 300 words. If you cannot provide an answer, say: "
            f'"I am not sure about this question. Please try asking something else."'
        )
            query = f"{preface}\nQuery: {question}"

            result = self.qa_chain({
                "question": query,
                "chat_history": [(q, a) for q, a in history]
            })

            if "answer" not in result:
                return history + [("System", "Sorry, an error occurred.")]

            history.append((question, result["answer"]))
            return history
        except Exception as e:
            return history + [("System", f"Error: {str(e)}")]

# Initialize RAG system in session state
if "rag_system" not in st.session_state:
    st.session_state.rag_system = DocumentRAG()

with st.sidebar:
    st.markdown("## About:")
    st.markdown(
        """
        This prototype is part of a research project – **Multilingual Clinical Text Understanding**.

        **Interim Goals:**
        1. Summarize clinical notes in local languages
        2. Enable question answering over clinical documents using RAG
        3. Evaluate performance in under-resourced languages like Bangla, 

        **Tasks Covered:**
        1. Summarization
        2. Question Answering
        """
    )

    st.markdown("## Steps:")
    st.markdown(
        """
        1. Upload documents
        2. Generate summary
        3. Ask Questions
        4. Log User Interactions

        """
    )

    st.markdown("## Contributors:")
    st.markdown("Azmine Toushik Wasi, Drishti, Prahitha, Anik, Ashay, AbdurRahman, Iqramul")

    st.markdown("### References:")
    st.markdown("[1. RAG_HW HuggingFace Space](https://huggingface.co/spaces/wint543/RAG_HW)")


# Streamlit UI
st.title("Multilingual Clinical Summarization & QA with RAG")
st.image("./cover_image.png", use_container_width=True)


# Step 1: Upload and Process Documents
st.subheader("Step 1: Upload and Process Documents")
uploaded_files = st.file_uploader("Upload files (PDF, TXT, CSV)", accept_multiple_files=True)
embedding_model_choice = st.radio(
    "Choose Embedding Model:",
    ["OpenAI Embeddings", "PubMedBERT Embeddings"],
    horizontal=True,
    key="embedding_model_choice"
)


if st.button("Process Documents"):
    if uploaded_files:
        with st.spinner("Processing documents, please wait..."):
            result = st.session_state.rag_system.process_documents(uploaded_files, embedding_model_choice)

        if "successfully" in result:
            st.success(result)
        else:
            st.error(result)
    else:
        st.warning("No files uploaded.")

# Step 2: Generate Summary
st.subheader("Step 2: Generate Summary")
st.write("Select Summary Language:")
summary_language_options = ["English", "Hindi", "Bangla", "Spanish", "French", "German", "Chinese", "Japanese"]
summary_language = st.radio(
    "", 
    summary_language_options, 
    horizontal=True, 
    key="summary_language"
)

if st.button("Generate Summary"):
    if hasattr(st.session_state.rag_system, "document_text") and st.session_state.rag_system.document_text:
        with st.spinner("Generating summary, please wait..."):
            summary = st.session_state.rag_system.generate_summary(st.session_state.rag_system.document_text, summary_language)
        if summary:
            st.session_state.rag_system.document_summary = summary
            st.text_area("Document Summary", summary, height=200)
            st.success("Summary generated successfully!")
        else:
            st.error("Failed to generate summary.")
    else:
        st.info("Please process documents first to generate summary.")

# Step 3: Ask Questions
st.subheader("Step 3: Ask Questions")
st.write("Select Q&A Language:")
qa_language_options = ["English", "Hindi", "Bangla", "Spanish", "French", "German", "Chinese", "Japanese"]
qa_language = st.radio(
    "", 
    qa_language_options, 
    horizontal=True, 
    key="qa_language"
)

if st.session_state.rag_system.qa_chain:
    history = []
    user_question = st.text_input("Ask a question:")
    if st.button("Submit Question"):
        with st.spinner("Answering your question, please wait..."):
            history = st.session_state.rag_system.handle_query(user_question, history, qa_language)
        for question, answer in history:
            st.chat_message("user").write(question)
            st.chat_message("assistant").write(answer)
else:
    st.info("Please process documents first to enable Q&A.")