Delete app.py

app.py

@@ -1,134 +0,0 @@
-import streamlit as st
-import pandas as pd
-import tempfile
-import os
-import json
-from pathlib import Path
-
-from langchain.schema import Document
-#from langchain.document_loaders import Document
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains import RetrievalQAWithSourcesChain
-from langchain import HuggingFacePipeline
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
-
-USER_AVATAR = "https://raw.githubusercontent.com/achilela/vila_fofoka_analysis/9904d9a0d445ab0488cf7395cb863cce7621d897/USER_AVATAR.png"
-BOT_AVATAR = "https://raw.githubusercontent.com/achilela/vila_fofoka_analysis/991f4c6e4e1dc7a8e24876ca5aae5228bcdb4dba/Ataliba_Avatar.jpg"
-CHAT_HISTORY_FILE = Path("chat_memory.json")
-
-def load_chat_history():
-    if CHAT_HISTORY_FILE.exists():
-        with open(CHAT_HISTORY_FILE, "r") as f:
-            return json.load(f)
-    return []
-
-def save_chat_history(history):
-    with open(CHAT_HISTORY_FILE, "w") as f:
-        json.dump(history, f)
-
-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)
-    df.columns = df.columns.astype(str)
-    return df
-
-def build_vectorstore_from_structured_records(df: pd.DataFrame):
-    df.fillna("", inplace=True)
-    records = []
-    for i, row in df.iterrows():
-        item_class = str(row.get("Item Class", "")).strip()
-        job_done = str(row.get("Job Done", "")).strip()
-        backlog = str(row.get("Backlog?", "")).strip()
-        days = str(row.get("Days in Backlog", "")).strip()
-        if not any([item_class, job_done, backlog, days]):
-            continue
-        sentence = f"Item Class {item_class} has status {job_done}, is in {backlog} backlog, and has {days} days."
-        records.append(Document(page_content=sentence, metadata={"source": f"Row {i+1}"}))
-
-    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
-    split_docs = splitter.split_documents(records)
-
-    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 = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=retriever)
-    return qa
-
-st.set_page_config(page_title="Excel-Aware RAG Chatbot", layout="wide")
-st.title("📊 Excel-Aware RAG Chatbot (Structured QA)")
-
-with st.sidebar:
-    uploaded_file = st.file_uploader("Upload your Excel file (.xlsx or .xlsm with 'Data Base' sheet)", type=["xlsx", "xlsm"])
-    if st.button("🗑️ Clear Chat History"):
-        st.session_state.chat_history = []
-        if CHAT_HISTORY_FILE.exists():
-            CHAT_HISTORY_FILE.unlink()
-        st.rerun()
-
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = load_chat_history()
-
-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:
-            df = preprocess_excel(tmp_path)
-            vectorstore = build_vectorstore_from_structured_records(df)
-            qa = create_qa_pipeline(vectorstore)
-            st.success("✅ File processed and chatbot ready! Ask your questions below.")
-        except Exception as e:
-            st.error(f"❌ Error processing file: {e}")
-        finally:
-            os.remove(tmp_path)
-
-    for message in st.session_state.chat_history:
-        st.chat_message(message["role"], avatar=USER_AVATAR if message["role"] == "user" else BOT_AVATAR).markdown(message["content"])
-
-    user_prompt = st.chat_input("Ask about item classes, backlog, or status...")
-
-    if user_prompt:
-        st.session_state.chat_history.append({"role": "user", "content": user_prompt})
-        st.chat_message("user", avatar=USER_AVATAR).markdown(user_prompt)
-
-        with st.chat_message("assistant", avatar=BOT_AVATAR):
-            with st.spinner("Thinking..."):
-                try:
-                    response = qa.invoke({"question": user_prompt})
-                    final_response = response['answer']
-                    sources = response.get('sources', '')
-                    placeholder = st.empty()
-                    streamed = ""
-                    for word in final_response.split():
-                        streamed += word + " "
-                        placeholder.markdown(streamed + "▌")
-                    placeholder.markdown(f"**{final_response.strip()}**")
-                    if sources:
-                        st.markdown(f"<sub>📎 <i>{sources}</i></sub>", unsafe_allow_html=True)
-                    st.session_state.chat_history.append({"role": "assistant", "content": final_response})
-                    save_chat_history(st.session_state.chat_history)
-                except Exception as e:
-                    st.error(f"❌ Error: {e}")
-else:
-    st.info("Upload a file on the left to get started.")