Create app.py

app.py

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+# app.py
+import os
+import glob
+import tempfile
+from typing import List
+import streamlit as st
+
+# LangChain / loaders / vectorstore / embeddings / LLM
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_groq import ChatGroq
+from langchain.chains import RetrievalQA
+
+st.set_page_config(page_title="RAG Papers Chat (Groq)", layout="wide")
+
+# -----------------------
+# Load custom CSS
+# -----------------------
+def load_css(path="style.css"):
+    if os.path.exists(path):
+        with open(path) as f:
+            st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+load_css()
+
+# -----------------------
+# Sidebar / settings
+# -----------------------
+st.sidebar.title("⚙️ Settings")
+chunk_size = st.sidebar.number_input("Chunk size", min_value=256, max_value=5000, value=1000, step=100)
+chunk_overlap = st.sidebar.number_input("Chunk overlap", min_value=0, max_value=1000, value=200, step=50)
+top_k = st.sidebar.slider("Top-k chunks to retrieve", min_value=1, max_value=10, value=3)
+model_choice = st.sidebar.selectbox(
+    "Groq model",
+    options=["llama-3.1-8b-instant", "llama-3.1-8b-8192", "mixtral-3b-16384"],
+    index=0
+)
+st.sidebar.markdown("🔑 Your **Groq API key** must be set as a secret (`GROQ_API_KEY`) in Hugging Face Settings.")
+
+# -----------------------
+# Utility functions
+# -----------------------
+@st.cache_data(show_spinner=False)
+def load_and_split_pdfs(file_paths: List[str], chunk_size: int, chunk_overlap: int):
+    """Load PDFs and return list of split documents (LangChain docs)."""
+    all_docs = []
+    splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+    for path in file_paths:
+        loader = PyPDFLoader(path)
+        loaded = loader.load()
+        splitted = splitter.split_documents(loaded)
+        all_docs.extend(splitted)
+    return all_docs
+
+@st.cache_resource(show_spinner=False)
+def build_vectorstore(docs):
+    """Create HuggingFace embeddings + FAISS vectorstore."""
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vectorstore = FAISS.from_documents(docs, embeddings)
+    return vectorstore
+
+def initialize_llm(model_name: str):
+    api_key = os.environ.get("GROQ_API_KEY")
+    if not api_key:
+        st.error("🚨 No `GROQ_API_KEY` found. Please add it in Hugging Face Space → Settings → Secrets.")
+        st.stop()
+    return ChatGroq(model=model_name, api_key=api_key, temperature=0)
+
+# -----------------------
+# Main UI
+# -----------------------
+st.title("📚 RAG Chat for Research Papers — Streamlit (Groq)")
+st.write("Upload multiple PDFs and ask questions. Answers will include deduplicated file sources.")
+
+uploaded_files = st.file_uploader("Upload PDF files", type="pdf", accept_multiple_files=True)
+process_btn = st.button("Process uploaded PDFs")
+
+if process_btn:
+    if not uploaded_files:
+        st.warning("Please upload one or more PDF files first.")
+    else:
+        tmp_paths = []
+        for f in uploaded_files:
+            tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+            tmp.write(f.read())
+            tmp.flush()
+            tmp_paths.append(tmp.name)
+
+        st.success("✅ PDFs saved. Processing...")
+
+        with st.spinner("Splitting into chunks..."):
+            docs = load_and_split_pdfs(tmp_paths, chunk_size, chunk_overlap)
+            st.write(f"✅ Created {len(docs)} chunks.")
+
+        with st.spinner("Building FAISS vectorstore..."):
+            vectorstore = build_vectorstore(docs)
+
+        st.session_state["vectorstore"] = vectorstore
+        st.session_state["processed"] = True
+        st.success("✅ Vectorstore ready! Ask questions below.")
+
+# -----------------------
+# Chat section
+# -----------------------
+st.markdown("---")
+st.subheader("💬 Chat with your papers")
+
+if "processed" not in st.session_state:
+    st.info("Process PDFs first to build the index.")
+else:
+    if "llm" not in st.session_state:
+        st.session_state["llm"] = initialize_llm(model_choice)
+
+    if "qa_chain" not in st.session_state:
+        retriever = st.session_state["vectorstore"].as_retriever(search_kwargs={"k": top_k})
+        st.session_state["qa_chain"] = RetrievalQA.from_chain_type(
+            llm=st.session_state["llm"],
+            retriever=retriever,
+            chain_type="stuff",
+            return_source_documents=True,
+        )
+
+    if "history" not in st.session_state:
+        st.session_state["history"] = []
+
+    query = st.text_input("Enter your question")
+    ask = st.button("Ask")
+
+    if ask and query.strip():
+        with st.spinner("Thinking..."):
+            result = st.session_state["qa_chain"]({"query": query})
+            answer = result.get("result", "")
+            source_docs = result.get("source_documents", [])
+
+            unique_sources = list({doc.metadata.get("source", "unknown") for doc in source_docs})
+            sources_text = "\n".join([f"- {os.path.basename(s)}" for s in unique_sources])
+
+            full_answer = f"{answer}\n\n📚 **Sources:**\n{sources_text}"
+            st.session_state["history"].append((query, full_answer))
+
+    st.markdown("### 📜 Conversation History")
+    for user_msg, bot_msg in reversed(st.session_state["history"]):
+        st.markdown(f"**You:** {user_msg}")
+        st.markdown(f"**Bot:** {bot_msg}")