moved files for hugging face spaces

.gitignore

@@ -1,6 +1,4 @@
 venv/
-data/
-data_index/
-.streamlit/
-.env
-.DS_Store
+src/data/
+# src/data_index/
+src/.env

Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.13.5-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+COPY src/ ./src/
+
+RUN pip3 install -r requirements.txt
+
+EXPOSE 8501
+
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

basic.py

@@ -1,147 +0,0 @@
-from pathlib import Path
-import os
-import textwrap
-
-# LangChain (HF + community)
-from langchain_community.document_loaders import TextLoader, PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.embeddings import HuggingFaceHubEmbeddings
-
-from langchain.prompts import PromptTemplate
-from langchain.chains import RetrievalQA
-from langchain_community.llms import HuggingFacePipeline
-from langchain_community.llms import HuggingFaceHub
-
-# Hugging Face transformers
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-
-ROOT_DIR = Path(__file__).parent
-DATA_DIR = Path(f"{ROOT_DIR}/data")  
-
-def load_documents(data_dir: Path):
-    docs = []
-    for path in data_dir.rglob("*"):
-        if path.is_dir():
-            continue
-        try:
-            if path.suffix.lower() in [".txt", ".md"]:
-                docs.extend(TextLoader(str(path), encoding="utf-8").load())
-            elif path.suffix.lower() == ".pdf":
-                docs.extend(PyPDFLoader(str(path)).load())
-        except Exception as e:
-            print(f"[skip] {path.name}: {e}")
-    if not docs:
-        raise RuntimeError(f"No documents found in {data_dir}. Put .txt/.md/.pdf files there.")
-    return docs
-
-def build_retriever(docs):
-    splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=120)
-    chunks = splitter.split_documents(docs)
-
-    # HF sentence-transformers embeddings (local)
-    embed_model_name = "sentence-transformers/all-MiniLM-L6-v2"
-    embeddings = HuggingFaceEmbeddings(model_name=embed_model_name)
-
-#    # Embeddings via Hugging Face Inference API (no local model)
-#     embed_model = "sentence-transformers/all-MiniLM-L6-v2"
-#     embeddings = HuggingFaceHubEmbeddings(
-#         repo_id=embed_model,
-#         # Batch size helps when indexing many chunks (tune if needed)
-#         task="feature-extraction",
-#     )
-
-    vs = FAISS.from_documents(chunks, embeddings)
-    return vs.as_retriever(search_kwargs={"k": 4})
-
-
-PROMPT_TMPL = """You are a helpful chatbot that answers questions about the candidate's profile for recruiters.
-Use ONLY the provided context. If the answer is not in the context, say you don't know.
-
-Context:
-{context}
-
-Question: {question}
-
-Answer:"""
-
-def build_chain(retriever, model_name="google/flan-t5-base", llm_repo_id="mistralai/Mistral-7B-Instruct-v0.3"):
-    # Local HF pipeline (CPU-friendly model)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-    gen = pipeline(
-        "text2text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=512,
-    )
-    llm = HuggingFacePipeline(pipeline=gen)
-
-
-    # # Text-generation via Hugging Face Inference API
-    # llm = HuggingFaceHub(
-    #     repo_id=llm_repo_id,
-    #     task="text-generation",
-    #     model_kwargs={
-    #         "max_new_tokens": 512,
-    #         "temperature": 0.1,
-    #         "return_full_text": False,
-    #     },
-    # )
-    
-    prompt = PromptTemplate(
-        input_variables=["context", "question"],
-        template=PROMPT_TMPL,
-    )
-
-    qa = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=retriever,
-        chain_type_kwargs={"prompt": prompt},
-        return_source_documents=False,
-    )
-    return qa
-
-
-def main():
-
-    if not os.environ.get("HUGGINGFACEHUB_API_TOKEN"):
-        print("Please set HUGGINGFACEHUB_API_TOKEN environment variable.")
-        return
-
-    print("Loading documents from", DATA_DIR)
-    docs = load_documents(DATA_DIR)
-    print(f"Loaded {len(docs)} documents. Building index…")
-    retriever = build_retriever(docs)
-    print('Retriever built successfully')
-    # exit()
-    chain = build_chain(retriever)
-
-    print("\nRecruiter Chatbot ready. Ask about the candidate's profile.")
-    print("Type 'exit' to quit.\n")
-
-    while True:
-        try:
-            q = input("You: ").strip()
-        except (EOFError, KeyboardInterrupt):
-            print("\nBye!")
-            break
-        if not q:
-            continue
-        if q.lower() in {"exit", "quit", "q"}:
-            print("Bye!")
-            break
-
-        try:
-            res = chain.invoke({"query": q})
-            answer = res["result"] if isinstance(res, dict) else str(res)
-        except Exception as e:
-            answer = f"[error] {e}"
-
-        print("\nAssistant:", textwrap.fill(answer, width=100))
-        print()
-
-if __name__ == "__main__":
-    main()

extra_qa_chains.py

@@ -1,109 +0,0 @@
-def build_chain(retriever, model_name: str = LLM_MODEL_NAME):
-    # Local HF pipeline
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-    gen = pipeline(
-        "text2text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=512,
-    )
-    llm = HuggingFacePipeline(pipeline=gen)
-
-
-    prompt = PromptTemplate(
-        input_variables=["context", "question"],
-        template=PROMPT_TMPL,
-    )
-
-    qa = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=retriever,
-        chain_type_kwargs={"prompt": prompt},
-        return_source_documents=True,
-    )
-    return qa
-
-
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from langchain_community.llms import HuggingFacePipeline
-from langchain.prompts import PromptTemplate
-from langchain.chains import RetrievalQA
-
-def build_chain_qwen(retriever, model_name: str = "Qwen/Qwen2.5-7B-Instruct"):
-    # Qwen2.5 is a causal LM (decoder-only), not seq2seq.
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    # Ensure padding token exists (use EOS as pad for causal models if missing)
-    if tokenizer.pad_token_id is None:
-        tokenizer.pad_token_id = tokenizer.eos_token_id
-
-    model = AutoModelForCausalLM.from_pretrained(model_name)
-
-    gen = pipeline(
-        task="text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=512,
-        do_sample=False,            # deterministic for QA
-        truncation=True,            # avoid context overruns
-        return_full_text=False,     # only the generated answer
-        eos_token_id=tokenizer.eos_token_id,
-        pad_token_id=tokenizer.pad_token_id,
-    )
-    llm = HuggingFacePipeline(pipeline=gen)
-
-    prompt = PromptTemplate(
-        input_variables=["context", "question"],
-        template=PROMPT_TMPL,
-    )
-
-    qa = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",                 # keep as in your snippet
-        retriever=retriever,
-        chain_type_kwargs={"prompt": prompt},
-        return_source_documents=True,
-    )
-    return qa
-
-
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from langchain_community.llms import HuggingFacePipeline
-from langchain.prompts import PromptTemplate
-from langchain.chains import RetrievalQA
-
-def build_chain_gemma(retriever, model_name: str = "google/gemma-2-2b-it"):
-    # Gemma 2 is a causal LM (decoder-only)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    if tokenizer.pad_token_id is None:
-        tokenizer.pad_token_id = tokenizer.eos_token_id
-
-    model = AutoModelForCausalLM.from_pretrained(model_name)
-
-    gen = pipeline(
-        task="text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=512,
-        do_sample=False,            # deterministic for QA
-        truncation=True,            # avoid context overruns
-        return_full_text=False,     # only generated continuation
-        eos_token_id=tokenizer.eos_token_id,
-        pad_token_id=tokenizer.pad_token_id,
-    )
-    llm = HuggingFacePipeline(pipeline=gen)
-
-    prompt = PromptTemplate(
-        input_variables=["context", "question"],
-        template=PROMPT_TMPL,
-    )
-
-    qa = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",                 # keep your current behavior
-        retriever=retriever,
-        chain_type_kwargs={"prompt": prompt},
-        return_source_documents=True,
-    )
-    return qa

ingest.py

@@ -1,69 +0,0 @@
-from pathlib import Path
-import argparse
-import sys
-import os
-
-from langchain_community.document_loaders import TextLoader, PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
-
-import os, streamlit as st
-from dotenv import load_dotenv
-load_dotenv()  # still works locally
-
-GOOGLE_API_KEY = st.secrets.get("GOOGLE_API_KEY", os.getenv("GOOGLE_API_KEY"))
-HF_API_TOKEN =  st.secrets.get("HUGGING_FACE_API_TOKEN", os.getenv("HUGGING_FACE_API_TOKEN"))
-
-EMBED_MODEL_NAME = st.secrets.get("HUGGING_FACE_EMBEDDING_MODEL", os.getenv("HUGGING_FACE_EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2"))
-LLM_MODEL_NAME = st.secrets.get("LLM_MODEL", os.getenv("LLM_MODEL", "gemini-1.5-flash"))
-
-ROOT_DIR = Path(__file__).parent
-INDEX_DIR = Path(f"{ROOT_DIR}/data_index")  
-DATA_DIR = Path(f"{ROOT_DIR}/data")  
-
-
-def load_documents(data_dir: Path):
-    docs = []
-    for path in data_dir.rglob("*"):
-        if path.is_dir():
-            continue
-        try:
-            if path.suffix.lower() in [".txt", ".md"]:
-                docs.extend(TextLoader(str(path), encoding="utf-8").load())
-            elif path.suffix.lower() == ".pdf":
-                docs.extend(PyPDFLoader(str(path)).load())
-        except Exception as e:
-            print(f"[skip] {path.name}: {e}", file=sys.stderr)
-    if not docs:
-        raise RuntimeError(f"No documents found in {data_dir}. Put .txt/.md/.pdf files there.")
-    return docs
-
-def build_vectorstore(docs):
-    splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=120)
-    chunks = splitter.split_documents(docs)
-    embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL_NAME)
-    vs = FAISS.from_documents(chunks, embeddings)
-    return vs
-
-def main():
-    parser = argparse.ArgumentParser(description="Ingest documents and build FAISS index.")
-    args = parser.parse_args()
-
-
-
-    print(f"Loading documents from {DATA_DIR}")
-    docs = load_documents(DATA_DIR)
-    print(f"Loaded {len(docs)} documents. Building index…")
-
-    vs = build_vectorstore(docs)
-    INDEX_DIR.mkdir(parents=True, exist_ok=True)
-    vs.save_local(str(INDEX_DIR))
-
-    # Persist embedding model name for safety
-    (INDEX_DIR / "embeddings_model.txt").write_text(EMBED_MODEL_NAME, encoding="utf-8")
-
-    print(f"Index saved to {INDEX_DIR.resolve()}")
-
-if __name__ == "__main__":
-    main()

qa_chain.py

@@ -1,101 +0,0 @@
-import argparse
-import textwrap
-from pathlib import Path
-import os
-from dotenv import load_dotenv
-from qa_prompts import PROMPT_TMPL
-
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.prompts import PromptTemplate
-from langchain.chains import RetrievalQA
-from langchain_google_genai import ChatGoogleGenerativeAI
-
-load_dotenv()
-
-HF_API_TOKEN = os.getenv("HUGGING_FACE_API_TOKEN")
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-
-EMBED_MODEL_NAME = os.getenv("HUGGING_FACE_EMBEDDING_MODEL")
-LLM_MODEL_NAME = os.getenv("LLM_MODEL")
-
-ROOT_DIR = Path(__file__).parent
-INDEX_DIR = Path(f"{ROOT_DIR}/data_index")  
-
-
-def load_retriever(index_dir: Path, k: int = 4):
-    # Ensure we use the same embedding model that was used during ingest
-    embed_model_name_path = index_dir / "embeddings_model.txt"
-    if not embed_model_name_path.exists():
-        raise RuntimeError(f"Missing {embed_model_name_path}. Re-run ingest.py.")
-    embed_model_name = embed_model_name_path.read_text(encoding="utf-8").strip()
-
-    embeddings = HuggingFaceEmbeddings(model_name=embed_model_name)
-    vs = FAISS.load_local(str(index_dir), embeddings, allow_dangerous_deserialization=True)
-    return vs.as_retriever(search_kwargs={"k": k})
-
-
-
-def build_chain_gemini(retriever):
-    if not GOOGLE_API_KEY:
-        raise RuntimeError("Set GOOGLE_API_KEY in your .env to use the Gemini inference endpoint.")
-
-    # Uses Google Generative AI (Gemini) hosted inference endpoint
-    llm = ChatGoogleGenerativeAI(
-        model=LLM_MODEL_NAME,
-        api_key=GOOGLE_API_KEY,
-        temperature=0.1,
-        max_output_tokens=512,
-        convert_system_message_to_human=True,
-    )
-
-    prompt = PromptTemplate(
-        input_variables=["context", "question"],
-        template=PROMPT_TMPL,
-    )
-
-    # map_reduce keeps per-call size manageable and robust
-    qa = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=retriever,
-        chain_type_kwargs={"prompt": prompt},
-        return_source_documents=True,
-    )
-    return qa
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Run recruiter Q/A over a saved FAISS index.")
-    args = parser.parse_args()
-
-    retriever = load_retriever(INDEX_DIR)
-
-    chain = build_chain_gemini(retriever)
-
-    print("\My Profile Chatbot ready. Ask about me.")
-    print("Type 'exit' to quit.\n")
-
-    while True:
-        try:
-            q = input("You: ").strip()
-        except (EOFError, KeyboardInterrupt):
-            print("\nBye!")
-            break
-        if not q:
-            continue
-        if q.lower() in {"exit", "quit", "q"}:
-            print("Bye!")
-            break
-
-        try:
-            res = chain.invoke({"query": q})
-            answer = res["result"] if isinstance(res, dict) else str(res)
-        except Exception as e:
-            answer = f"[error] {e}"
-
-        print("\nMaheen:", textwrap.fill(answer, width=100))
-        print()
-
-if __name__ == "__main__":
-    main()

qa_prompts.py

@@ -1,9 +0,0 @@
-PROMPT_TMPL = """You are a helpful chatbot that answers questions about the candidate's profile for recruiters.
-Use ONLY the provided context. If the answer is not in the context, say you don't know. Be concise and factual.
-
-Context:
-{context}
-
-Question: {question}
-
-Answer:"""

readme.md

@@ -6,25 +6,28 @@ A chatbot about my profile, experience, education and skills
 
 - basic.py: 
     - main workflow of the chatbot in cli
-    - run with `python basic.py`
+    - run with `python qa_chain_cli.py`
 - ui_qa.py
     - streamlit app for QA chatbot
-    - run with `streamlit run ui_qa.py`
+    - run with `streamlit run streamlit_app.py`
 
 
 ## Todos:
 
 - update readme for project structure, esp data, venv and env
 - add more data
+    - add updated cv with project
     - educational docs
     - experience letters
     - project docs/ reports/ readme
-    - linkedin stuff
+    - linkedin stuff: get more from linkedin csv
 - better llm selection: using gemini for now
     - add ui selection for gemini free models
+    - improve qa pipeline and prompt
+    - add prompt history for agent mem
 - make router chains for better response
     - add router chains for education, skills, experience and default
 - UI: improve UI
-    - enter for chat
     - chat sequence
-- deploy
+- deploy:
+    - improve the huggingface guthub repo hosting setup

ui_qa.py

@@ -1,181 +0,0 @@
-import subprocess
-from pathlib import Path
-from typing import List
-import streamlit as st
-from qa_prompts import PROMPT_TMPL
-
-from langchain_community.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from langchain.prompts import PromptTemplate
-from langchain.embeddings.base import Embeddings
-from langchain_google_genai import ChatGoogleGenerativeAI
-from huggingface_hub import InferenceClient
-
-import os, streamlit as st
-from dotenv import load_dotenv
-load_dotenv()  # still works locally
-
-GOOGLE_API_KEY = st.secrets.get("GOOGLE_API_KEY", os.getenv("GOOGLE_API_KEY"))
-HF_API_TOKEN =  st.secrets.get("HUGGING_FACE_API_TOKEN", os.getenv("HUGGING_FACE_API_TOKEN"))
-
-EMBED_MODEL_NAME = st.secrets.get("HUGGING_FACE_EMBEDDING_MODEL", os.getenv("HUGGING_FACE_EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2"))
-LLM_MODEL_NAME = st.secrets.get("LLM_MODEL", os.getenv("LLM_MODEL", "gemini-1.5-flash"))
-
-ROOT_DIR = Path(__file__).parent
-INDEX_DIR = Path(f"{ROOT_DIR}/data_index")  
-
-
-###### run ingest.py ######
-
-if not INDEX_DIR.exists():
-    with st.spinner("Index not found. Building FAISS index (first run)…"):
-        # Ensure ingest.py reads the same env/secrets model and paths
-        proc = subprocess.run(["python", "ingest.py"], capture_output=True, text=True)
-        if proc.returncode != 0:
-            st.error(f"ingest.py failed:\n{proc.stderr}")
-            st.stop()
-
-
-class HFAPIEmbeddings(Embeddings):
-    def __init__(self, repo_id: str, token: str | None = None, timeout: float = 120.0):
-        self.client = InferenceClient(model=repo_id, token=token, timeout=timeout)
-
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        return self.client.feature_extraction(texts)
-
-    def embed_query(self, text: str) -> List[float]:
-        vec = self.client.feature_extraction(text)
-        return vec[0] if (isinstance(vec, list) and vec and isinstance(vec[0], list)) else vec
-
-
-
-def build_chain_gemini(retriever, _llm_repo, _max_new, _temp, _show_sources):
-    if not GOOGLE_API_KEY:
-        raise RuntimeError("Set GOOGLE_API_KEY in your .env to use the Gemini inference endpoint.")
-
-    # Uses Google Generative AI (Gemini) hosted inference endpoint
-    llm = ChatGoogleGenerativeAI(
-        model=_llm_repo,
-        api_key=GOOGLE_API_KEY,
-        temperature=_temp,
-        max_output_tokens=_max_new,
-        convert_system_message_to_human=True,
-    )
-
-    prompt = PromptTemplate(
-        input_variables=["context", "question"],
-        template=PROMPT_TMPL,
-    )
-
-    #map reduce or stuff
-    qa = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=retriever,
-        chain_type_kwargs={"prompt": prompt},
-        return_source_documents=_show_sources,
-    )
-    return qa
-
-
-
-# ========================= Streamlit UI =========================
-st.set_page_config(page_title="Maheen's Profile Chatbot", page_icon="💬", layout="centered")
-st.title("Maheen's Profile Chatbot")
-st.caption("RAG over my profile docs using FAISS + Hugging Face Inference API")
-
-# Sidebar settings
-st.sidebar.header("Settings")
-hf_token = HF_API_TOKEN
-if not hf_token:
-    st.sidebar.warning("HUGGINGFACEHUB_API_TOKEN is not set. Set it in your shell before running the app.")
-
-# store_dir = st.sidebar.text_input("FAISS store path", value=INDEX_DIR)
-
-# llm_repo_id = st.sidebar.text_input("LLM repo (HF)", value=LLM_MODEL_NAME)
-# embed_repo_id = st.sidebar.text_input("Embedding model (HF)", value=EMBED_MODEL_NAME)
-
-# Display model names as text (read-only)
-st.sidebar.markdown(f"**Embedding Model:** `{EMBED_MODEL_NAME}`")
-st.sidebar.markdown(f"**Chat Model:** `{LLM_MODEL_NAME}`")
-
-
-# k = st.sidebar.number_input("Top-k retrieved chunks", min_value=1, max_value=20, value=4, step=1)
-k = 4
-# max_new_tokens = st.sidebar.number_input("Max new tokens", min_value=64, max_value=2048, value=512, step=64)
-max_new_tokens = 512
-# temperature = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, value=0.1, step=0.05)
-temperature = 0.1
-# show_sources = st.sidebar.checkbox("Show sources", value=False)
-show_sources = False
-
-
-###################
-
-# Session state for chat history
-if "history" not in st.session_state:
-    st.session_state.history = []  # list of (user, assistant, sources)
-
-# Load vector store & chain lazily, cache across reruns
-@st.cache_resource(show_spinner=True)
-def _load_chain(_store_dir: str, _embed_repo: str, _llm_repo: str, _k: int, _max_new: int, _temp: float, _show_sources: bool):
-    if not Path(_store_dir).exists():
-        raise FileNotFoundError(f"FAISS store not found at '{_store_dir}'. Run ingest.py first.")
-    embeddings = HFAPIEmbeddings(repo_id=_embed_repo, token=hf_token)
-    vs = FAISS.load_local(
-        _store_dir,
-        embeddings,
-        allow_dangerous_deserialization=True,  # required by newer LC versions
-    )
-    retriever = vs.as_retriever(search_kwargs={"k": 4}) # hardcoded, change later
-    chain = build_chain_gemini(retriever, _llm_repo, _max_new, _temp, _show_sources)
-    return chain
-
-
-# Prepare chain
-with st.spinner("Preparing retriever & LLM…"):
-    chain = _load_chain(INDEX_DIR, EMBED_MODEL_NAME, LLM_MODEL_NAME, k, max_new_tokens, temperature, show_sources)
-
-def render_sources(docs):
-    if not docs:
-        return
-    st.markdown("**Sources**")
-    for i, d in enumerate(docs, start=1):
-        src = d.metadata.get("source", "unknown")
-        page = d.metadata.get("page", None)
-        label = f"{Path(src).name}" + (f" (page {page+1})" if isinstance(page, int) else "")
-        with st.expander(f"{i}. {label}"):
-            st.write(d.page_content[:1500] + ("…" if len(d.page_content) > 1500 else ""))
-
-# --- Chat input with Enter submit ---
-with st.form("chat-form", clear_on_submit=True):
-    user_input = st.text_input(
-        "Ask about my profile:",
-        placeholder="e.g., What are your key projects?"
-    )
-    submitted = st.form_submit_button("Ask")
-
-if submitted and user_input.strip():
-    with st.spinner("Thinking…"):
-        try:
-            res = chain.invoke({"query": user_input.strip()})
-            if isinstance(res, dict):
-                answer = res.get("result", "")
-                sources = res.get("source_documents", []) if show_sources else []
-            else:
-                answer, sources = str(res), []
-        except Exception as e:
-            answer, sources = f"[error] {e}", []
-    st.session_state.history.append((user_input.strip(), answer, sources))
-
-# Display history
-for q, a, srcs in st.session_state.history:
-    st.markdown(f"**You:** {q}")
-    st.markdown(f"**Assistant:** {a}")
-    if show_sources:
-        render_sources(srcs)
-    st.markdown("---")
-
-# Footer
-# st.caption("Enter submits. Datastore path fixed from code/env. Models shown read-only.")
-