app.py

import os
import streamlit as st
from PIL import Image, ImageOps
from langchain_openai import ChatOpenAI
from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain import PromptTemplate
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import FlashrankRerank
from dotenv import load_dotenv
from langchain_community.embeddings.bedrock import BedrockEmbeddings
load_dotenv()
# Hyperparameters
PDF_CHUNK_SIZE = 1024
PDF_CHUNK_OVERLAP = 256
k = 3

# Load favicon image
def load_and_pad_image(image_path, size=(64, 64)):
    img = Image.open(image_path)
    return ImageOps.pad(img, size)

favicon_path = "medical.png"
favicon_image = load_and_pad_image(favicon_path)

# Streamlit Page Config
st.set_page_config(
    page_title="Chatbot",
    page_icon=favicon_image,
)

# Set up logo and title
col1, col2 = st.columns([1, 8])
with col1:
    st.image(favicon_image)
with col2:
    st.markdown(
        """
        <h1 style='text-align: left; margin-top: -12px;'>Chatbot</h1>
        """, unsafe_allow_html=True
    )

# Model and Embedding Selection
model_options = ["gpt-4o", "gpt-4o-mini", "deepseek-chat"]
selected_model = st.selectbox("Choose a GPT model", model_options)

embedding_model_options = ["OpenAI", "Huggingface MedEmbed"]
selected_embedding_model = st.selectbox("Choose an Embedding model", embedding_model_options)

# Load the model
def get_llm(selected_model):
    api_key = os.getenv("DeepSeek_API_KEY") if selected_model == "deepseek-chat" else os.getenv("OPENAI_API_KEY")
    return ChatOpenAI(
        model=selected_model,
        temperature=0,
        max_tokens=None,
        api_key=api_key,
    )

# Cache the vector store loading
@st.cache_resource
def load_vector_store(selected_embedding_model):
    if selected_embedding_model == "OpenAI":
        embeddings = OpenAIEmbeddings(model="text-embedding-3-large", api_key=os.getenv("OPENAI_API_KEY"))
        return FAISS.load_local("faiss_index_medical_OpenAI", embeddings, allow_dangerous_deserialization=True)
    else:
        embeddings = HuggingFaceEmbeddings(model_name="abhinand/MedEmbed-large-v0.1")
        return FAISS.load_local("faiss_index_medical_MedEmbed", embeddings, allow_dangerous_deserialization=True)

# Load the selected vector store
vector_store = load_vector_store(selected_embedding_model)
llm = get_llm(selected_model)

# Main App Logic
def main():
    st.session_state['knowledge_base'] = vector_store
    st.header("Ask a Question")

    question = st.text_input("Enter your question")
    if st.button("Get Answer"):
        knowledge_base = st.session_state['knowledge_base']
        retriever = knowledge_base.as_retriever(search_kwargs={"k": k})
        compressor = FlashrankRerank()
        compression_retriever = ContextualCompressionRetriever(
            base_compressor=compressor, base_retriever=retriever
        )

        system_prompt = """
        You are a friendly and knowledgeable assistant who is an expert in medical education...
        """

        template = f"""
        {system_prompt}
        -------------------------------
        Context: {{context}}
        Question: {{question}}
        Answer:
        """

        prompt = PromptTemplate(
            template=template,
            input_variables=['context', 'question']
        )

        qa_chain = RetrievalQA.from_chain_type(
            llm,
            retriever=compression_retriever,
            return_source_documents=True,
            chain_type_kwargs={"prompt": prompt}
        )

        response = qa_chain.invoke({"query": question})
        st.write(f"**Answer:** {response['result']}")

if __name__ == "__main__":
    main()