Update app.py

app.py

@@ -138,68 +138,3 @@
 
 
 
-Filename: app.py
-
-import streamlit as st from langchain.embeddings import HuggingFaceEmbeddings from langchain.vectorstores import FAISS from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.document_loaders import PyPDFLoader from langchain.chains import RetrievalQA from langchain.llms import HuggingFaceHub import tempfile import os
-
-Constants
-
-EMBEDDING_MODEL_NAME = "BAAI/bge-base-en-v1.5" LLM_MODEL_REPO = "mistralai/Mistral-7B-Instruct-v0.1" CHUNK_SIZE = 500 CHUNK_OVERLAP = 300
-
-Load and split documents
-
-def load_and_split_pdf(pdf_file): with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file: tmp_file.write(pdf_file.read()) tmp_file_path = tmp_file.name
-
-loader = PyPDFLoader(tmp_file_path)
-documents = loader.load()
-splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
-chunks = splitter.split_documents(documents)
-return chunks
-
-Create FAISS vectorstore
-
-def build_vectorstore(chunks): embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME) db = FAISS.from_documents(chunks, embedding=embeddings) return db
-
-Initialize LLM from Hugging Face Hub
-
-def get_llm(): return HuggingFaceHub( repo_id=LLM_MODEL_REPO, model_kwargs={"temperature": 0.3, "max_new_tokens": 512, "top_k": 10} )
-
-Custom prompt for better accuracy
-
-CUSTOM_PROMPT = """ You are a professional resume chatbot. Use the context below to accurately and concisely answer the user's question. If the information is not available in the context, say "Not found in the document.".
-
-Context: {context}
-
-Question: {question}
-
-Answer: """
-
-Build QA chain
-
-def build_qa_chain(vectorstore): return RetrievalQA.from_chain_type( llm=get_llm(), retriever=vectorstore.as_retriever(), chain_type="stuff", chain_type_kwargs={ "prompt": CUSTOM_PROMPT } )
-
-Streamlit UI
-
-def main(): st.set_page_config(page_title="Resume Q&A Bot", layout="wide") st.title("Resume Chatbot - Ask Anything About the Uploaded PDF")
-
-uploaded_file = st.file_uploader("Upload your resume (PDF)", type="pdf")
-
-if uploaded_file is not None:
-    st.success("PDF uploaded successfully!")
-    with st.spinner("Processing document and creating knowledge base..."):
-        chunks = load_and_split_pdf(uploaded_file)
-        vectorstore = build_vectorstore(chunks)
-        qa_chain = build_qa_chain(vectorstore)
-
-    st.success("Knowledge base ready! Ask your question below:")
-
-    question = st.text_input("Your Question:")
-
-    if question:
-        with st.spinner("Generating answer..."):
-            response = qa_chain.run(question)
-            st.markdown(f"**Answer:** {response}")
-
-if name == 'main': main()
-
-