add two files one for streamlit and one for huggingface

streamlit_app.py

@@ -0,0 +1,71 @@
+from src.data_processor import DataProcessor
+from src.embedding import EmbeddingManager
+from src.vectorstore import VectorStore
+from src.download_data import download_pmc_docs
+from src.llm import LLM
+from tqdm import tqdm
+
+import streamlit as st 
+
+@st.cache_resource(show_spinner="🔄 Building pipeline...")
+def load_pipeline():
+    limit = 2000
+    download_pmc_docs(limit=limit)
+    dp = DataProcessor()
+    chunks, document = dp.build()
+    chunks_list = [c.page_content for c in tqdm(chunks, desc='Chunking')]
+    embd = EmbeddingManager()
+    embd_model = embd.get_model()
+    chunks_embedding = embd.embed_texts(chunks_list)
+    vectorstore = VectorStore()
+    vectorstore.add_documents(chunks, chunks_embedding)
+    retriever = vectorstore.get_retriever(embd_model)
+    llm = LLM(retriever)
+    return llm
+
+
+if __name__ == '__main__':
+
+    st.set_page_config(
+        page_title="MedRAG: AI-Powered Biomedical Paper Search",
+        layout="wide",
+        page_icon="🧬",
+    )
+
+    st.title("🧬 MedRAG")
+    st.caption("Ask questions. Explore research. Ground answers in biomedical literature.")
+
+    llm = load_pipeline()
+
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+
+    for q, a in st.session_state.chat_history:
+        with st.chat_message("user"):
+            st.write(q)
+        with st.chat_message("assistant"):
+            st.write(a)
+
+    if query := st.chat_input("Type your biomedical question here..."):
+        with st.chat_message("user"):
+            st.write(query)
+
+        with st.chat_message("assistant"):
+            with st.spinner("🤖 Thinking... please wait"):
+                result = llm.invoke(query)
+                answer = result["result"]
+
+                sources = []
+
+                if result['source_documents']:
+                    for doc in result['source_documents']:
+                        preview = doc.page_content[:200].replace("\n", " ")
+                        sources.append(preview + "...")
+                st.write(answer)
+
+                if sources:
+                    with st.expander('📚 Sources'):
+                        for idx, src in enumerate(sources, 1):
+                            st.markdown(f"**{idx}.** {src}")
+
+        st.session_state.chat_history.append((query, (answer, sources)))