Update app.py

app.py

@@ -1 +1,123 @@
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+import streamlit as st
+import pandas as pd
+import tempfile
+import os
+import json
+from pathlib import Path
+
+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 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)
+    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 = 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 (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 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:
+            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.")
+        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 inspections, delays, backlog...")
+
+    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("Searching and generating..."):
+                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.")