Update app.py

app.py

@@ -1,73 +1,8 @@
-import streamlit as st
-import pandas as pd
-import tempfile
-import os
-
-from langchain.document_loaders import DataFrameLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from langchain import HuggingFacePipeline
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
-
-def preprocess_excel(file_path: str) -> pd.DataFrame:
-    df_raw = pd.read_excel(file_path, sheet_name='Data Base', header=None)
-    df = df_raw.iloc[4:].copy()
-    df.columns = df.iloc[0]
-    df = df[1:]
-    df.dropna(how='all', inplace=True)
-    df.dropna(axis=1, how='all', inplace=True)
-    df.reset_index(drop=True, inplace=True)
-    return df
-
-def build_vectorstore_from_dataframe(df: pd.DataFrame):
-    df.fillna("", inplace=True)
-    df['combined_text'] = df.apply(lambda row: ' | '.join([str(cell) for cell in row]), axis=1)
-
-    docs_loader = DataFrameLoader(df[['combined_text']], page_content_column='combined_text')
-    documents = docs_loader.load()
-
-    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
-    split_docs = splitter.split_documents(documents)
-
-    embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-l6-v2",
-        model_kwargs={"device": "cpu"},
-        encode_kwargs={"normalize_embeddings": False}
-    )
-    vectorstore = FAISS.from_documents(split_docs, embeddings)
-    return vectorstore
-
-def create_qa_pipeline(vectorstore):
-    model_id = "google/flan-t5-base"
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
-
-    gen_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer, max_length=512)
-    llm = HuggingFacePipeline(pipeline=gen_pipeline)
-
-    retriever = vectorstore.as_retriever()
-    qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type="stuff", return_source_documents=False)
-    return qa
-
-st.set_page_config(page_title="Excel-Aware RAG Chatbot", layout="wide")
-st.title("📊 Excel-Aware RAG Chatbot (Professional QA)")
-
-with st.sidebar:
-    uploaded_file = st.file_uploader("Upload your Excel file (.xlsx or .xlsm with 'Data Base' sheet)", type=["xlsx", "xlsm"])
-
-if uploaded_file is not None:
-    with st.spinner("Processing and indexing your Excel sheet..."):
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".xlsm") as tmp_file:
-            tmp_file.write(uploaded_file.read())
-            tmp_path = tmp_file.name
-
         try:
             cleaned_df = preprocess_excel(tmp_path)
             vectorstore = build_vectorstore_from_dataframe(cleaned_df)
             qa = create_qa_pipeline(vectorstore)
-            st.success("✅ File processed and chatbot ready! Ask your questions below.")
+            st.success("✅ File processed and chatbot ready! Ask your questions below.")
 
             if "chat_history" not in st.session_state:
                 st.session_state.chat_history = []