|
|
| import streamlit as st |
| from langchain_community.document_loaders import PyPDFLoader |
| from langchain_text_splitters import RecursiveCharacterTextSplitter |
| from langchain_huggingface import HuggingFaceEmbeddings |
| from langchain.vectorstores import Chroma |
| from langchain.prompts import ChatPromptTemplate |
| from langchain_google_genai import ChatGoogleGenerativeAI |
| from langchain_core.runnables import RunnablePassthrough |
| import tempfile |
|
|
|
|
| |
| st.title("📄 Medical Education Visual Aid - PDF RAG Chatbot") |
|
|
| |
| uploaded_file = st.file_uploader("Upload a PDF document", type=["pdf"]) |
|
|
| |
| if uploaded_file: |
| |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file: |
| tmp_file.write(uploaded_file.read()) |
| temp_pdf_path = tmp_file.name |
|
|
| |
| loader = PyPDFLoader(temp_pdf_path) |
| pages = loader.load() |
|
|
| splitter = RecursiveCharacterTextSplitter( |
| chunk_size=100, |
| chunk_overlap=20 |
| ) |
| split = splitter.split_documents(pages) |
|
|
| |
| hfembedding = HuggingFaceEmbeddings() |
| db = Chroma.from_documents(documents=split, embedding=hfembedding) |
|
|
| |
| template_string = """ |
| You are a smart medical assistant. Use the provided context to answer the question. |
| context: {context} |
| question: {question} |
| """ |
| prompt_template = ChatPromptTemplate.from_template(template_string) |
|
|
| |
| GOOGLE_API_KEY = "AIzaSyDA1zZ006f4krgUDjhdju_RcwZNwNPOJwI" |
| llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest", api_key=GOOGLE_API_KEY) |
|
|
| |
| retriever = db.as_retriever() |
| chain = ( |
| {"context": retriever, "question": RunnablePassthrough()} |
| | prompt_template |
| | llm |
| ) |
|
|
| |
| query = st.text_input("💬 Ask a question about the uploaded PDF:") |
| if query: |
| response = chain.invoke(query) |
| st.subheader("Answer") |
| st.write(response.content) |
|
|
