Create app.py

app.py

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+import os
+import streamlit as st
+from langchain_community.document_loaders import PDFPlumberLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import ChatPromptTemplate
+from langchain.chains import LLMChain
+from langchain.llms import CTransformers
+import torch
+
+# ==== Configuration ====
+pdfs_directory = 'pdfs'
+vectorstores_directory = 'vectorstores_medical'
+os.makedirs(pdfs_directory, exist_ok=True)
+os.makedirs(vectorstores_directory, exist_ok=True)
+
+PREDEFINED_BOOKS = [f for f in os.listdir(pdfs_directory) if f.endswith(".pdf")]
+
+TEMPLATE = """
+You are a medical assistant with deep clinical knowledge. 
+Use the following retrieved context to answer the question.
+If unsure, say "I don't know." Keep answers accurate, concise, and clear.
+
+Question: {question}
+Context: {context}
+Answer:
+"""
+
+# ==== Embedding Model (Medical) ====
+embedding_model = HuggingFaceEmbeddings(
+    model_name='pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb',
+    model_kwargs={"device": "cuda" if torch.cuda.is_available() else "cpu"},
+    encode_kwargs={"normalize_embeddings": False}
+)
+
+# ==== LLM (Local Quantized Medical Model) ====
+# llm = CTransformers(
+#     model='TheBloke/MedAlpaca-7B-GGUF',
+#     model_file='medalpaca-7b.Q4_K_M.gguf',
+#     model_type='llama',
+#     config={'max_new_tokens': 512, 'temperature': 0.4}
+# )
+from langchain.llms import HuggingFaceHub
+
+hf_token = "your_huggingface_token"
+
+llm = HuggingFaceHub(
+    repo_id="epfl-llm/meditron-7b",  # Or BioGPT, GatorTron, ClinicalT5, etc.
+    model_kwargs={"temperature": 0.4, "max_new_tokens": 512},
+    huggingfacehub_api_token=hf_token
+)
+
+# ==== Helpers ====
+def split_text(documents):
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+        add_start_index=True
+    )
+    return splitter.split_documents(documents)
+
+def get_vectorstore_path(book_filename):
+    base_name = os.path.splitext(book_filename)[0]
+    return os.path.join(vectorstores_directory, base_name)
+
+def load_or_create_vectorstore(book_filename, documents=None):
+    vs_path = get_vectorstore_path(book_filename)
+
+    if os.path.exists(os.path.join(vs_path, "index.faiss")):
+        return FAISS.load_local(vs_path, embedding_model, allow_dangerous_deserialization=True)
+
+    if documents is None:
+        raise ValueError("Documents required to create vector store.")
+
+    with st.spinner(f"⏳ Creating vector store for '{book_filename}'..."):
+        os.makedirs(vs_path, exist_ok=True)
+        chunks = split_text(documents)
+        vector_store = FAISS.from_documents(chunks, embedding_model)
+        vector_store.save_local(vs_path)
+        st.success(f"✅ Vector store created for '{book_filename}'.")
+    return vector_store
+
+def retrieve_docs(vector_store, query):
+    return vector_store.similarity_search(query)
+
+def answer_question(question, documents):
+    context = "\n\n".join(doc.page_content for doc in documents)
+    prompt = ChatPromptTemplate.from_template(TEMPLATE)
+    chain = LLMChain(llm=llm, prompt=prompt)
+    return chain.run({"question": question, "context": context})
+
+def upload_pdf(file):
+    save_path = os.path.join(pdfs_directory, file.name)
+    with open(save_path, "wb") as f:
+        f.write(file.getbuffer())
+    return file.name
+
+def load_pdf(file_path):
+    loader = PDFPlumberLoader(file_path)
+    return loader.load()
+
+# ==== Streamlit App ====
+st.set_page_config(page_title="🩺 Medical PDF Chat", layout="centered")
+st.title("📚 Medical Assistant - PDF Q&A")
+
+with st.sidebar:
+    st.header("Select or Upload a Medical Book")
+    selected_book = st.selectbox("Choose a PDF", PREDEFINED_BOOKS + ["Upload new book"])
+
+    if selected_book == "Upload new book":
+        uploaded_file = st.file_uploader("Upload Medical PDF", type="pdf")
+        if uploaded_file:
+            filename = upload_pdf(uploaded_file)
+            st.success(f"📥 Uploaded: {filename}")
+            selected_book = filename
+
+# ==== Main Logic ====
+if selected_book and selected_book != "Upload new book":
+    st.info(f"📖 You selected: {selected_book}")
+    file_path = os.path.join(pdfs_directory, selected_book)
+    vectorstore_path = get_vectorstore_path(selected_book)
+
+    try:
+        if os.path.exists(os.path.join(vectorstore_path, "index.faiss")):
+            st.success("✅ Vector store already exists. Using cached version.")
+            vector_store = load_or_create_vectorstore(selected_book)
+        else:
+            documents = load_pdf(file_path)
+            vector_store = load_or_create_vectorstore(selected_book, documents)
+
+        # Chat Input
+        question = st.chat_input("Ask your medical question...")
+        if question:
+            st.chat_message("user").write(question)
+            related_docs = retrieve_docs(vector_store, question)
+            answer = answer_question(question, related_docs)
+            st.chat_message("assistant").write(answer)
+
+    except Exception as e:
+        st.error(f"❌ Error loading or processing the PDF: {e}")