Update app.py

app.py

@@ -1,8 +1,73 @@
+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 = []