Delete utils.py

utils.py

@@ -1,124 +0,0 @@
-import os, tempfile, streamlit as st
-from typing import List, IO, Tuple
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from docx import Document
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.schema import Document as LangchainDocument
-from langchain_community.vectorstores import FAISS
-from cerebras.cloud.sdk import Cerebras # <-- NEW
-from langchain.prompts import PromptTemplate
-from langchain_together.embeddings import TogetherEmbeddings
-
-load_dotenv()
-
-# ---------- Helpers ---------------------------------------------------------
-def get_cerebras_api_key() -> str:
-    key = os.environ.get("CEREBRAS_API_KEY") or st.secrets.get("CEREBRAS_API_KEY", None)
-    if not key:
-        raise EnvironmentError("CEREBRAS_API_KEY not found in env or Streamlit secrets.")
-    return key
-
-# ---------- File-reading utilities -----------------------------------------
-def get_pdf_text(pdf_docs: List[IO[bytes]]) -> str:
-    txt = ""
-    for pdf in pdf_docs:
-        for page in PdfReader(pdf).pages:
-            if (t := page.extract_text()):
-                txt += t + "\n"
-    return txt
-
-def get_docx_text(docx_docs: List[IO[bytes]]) -> str:
-    txt = ""
-    for d in docx_docs:
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".docx") as tmp:
-            tmp.write(d.getvalue()); tmp.flush()
-        try:
-            doc = Document(tmp.name)
-            txt += "\n".join(p.text for p in doc.paragraphs) + "\n"
-        finally:
-            os.unlink(tmp.name)
-    return txt
-
-def get_text_chunks(text: str) -> List[str]:
-    return RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200).split_text(text)
-
-# ---------- Vector-store build & save --------------------------------------
-def get_vector_store(text_chunks: List[str]) -> None:
-    api_key = get_together_api_key()
-    embeddings = TogetherEmbeddings(model="BAAI/bge-base-en-v1.5", api_key=api_key)
-    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index")
-
-# ---------- QA chain helpers ----------------------------------------------
-def get_conversational_chain() -> Tuple[Cerebras, PromptTemplate]:
-    # Cerebras client is instantiated here, prompt template unchanged
-    client = Cerebras(api_key=get_cerebras_api_key())
-    prompt = PromptTemplate(
-        template=(
-            "As a professional assistant, provide a detailed and formally written "
-            "answer to the question using the provided context.\n\nContext:\n{context}\n\n"
-            "Question:\n{question}\n\nAnswer:"
-        ),
-        input_variables=["context", "question"]
-    )
-    return client, prompt
-
-def self_assess(question: str) -> str:
-    client = Cerebras(api_key=get_cerebras_api_key())
-    msgs = [
-        {"role": "system", "content": "You are an expert assistant…"},
-        {"role": "user", "content": (
-            "If you can confidently answer the following question from your own "
-            "knowledge, do so; otherwise reply with 'NEED_RETRIEVAL'.\n\n"
-            f"Question: {question}"
-        )}
-    ]
-    result = client.chat.completions.create(
-        messages=msgs,
-        model="llama-3.3-70b",
-        max_completion_tokens=1024,
-        temperature=0.2,
-        top_p=1,
-        stream=False
-    )
-    return result.choices[0].message.content.strip()
-
-def process_docs_for_query(docs: List[LangchainDocument], question: str) -> str:
-    if not docs:
-        return "Sorry, I couldn’t find relevant info in the documents."
-    ctx = "\n\n".join(d.page_content for d in docs)
-    client, prompt = get_conversational_chain()
-    prompt_text = prompt.format(context=ctx, question=question)
-    result = client.chat.completions.create(
-        messages=[{"role": "user", "content": prompt_text}],
-        model="llama-3.3-70b",
-        max_completion_tokens=1024,
-        temperature=0.2,
-        top_p=1,
-        stream=False
-    )
-    return result.choices[0].message.content
-
-
-# ---------- Main user-query orchestrator -----------------------------------
-def user_input(user_question: str) -> None:
-    assessment = self_assess(user_question)
-    need_retrieval = assessment.upper() == "NEED_RETRIEVAL"
-    st.info("🔍 Searching documents…" if need_retrieval else "💡 Using model knowledge…")
-
-    try:
-        if need_retrieval:
-            # Embeddings usage remains, need to replace TogetherEmbeddings if you want Cerebras embedding alternative
-            api_key = get_cerebras_api_key()
-            # Comment or replace TogetherEmbeddings below if unsupported
-            embeddings = TogetherEmbeddings(model="BAAI/bge-base-en-v1.5", api_key=api_key)  
-            vs = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-            docs = vs.similarity_search(user_question)
-            answer = process_docs_for_query(docs, user_question)
-        else:
-            answer = assessment
-        st.markdown("### Answer")
-        st.markdown(answer)
-    except Exception as e:
-        st.error(f"⚠️ Error: {e}")