Update app.py

app.py

@@ -1,52 +1,54 @@
 import gradio as gr
-import tempfile
 
 from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.vectorstores import FAISS
 from langchain.chains import RetrievalQA
 from langchain_openai import ChatOpenAI
 
-def run_qa(pdf_file, question):
-    if pdf_file is None or question.strip() == "":
-        return "Please upload a PDF and enter a question."
 
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
-        tmp.write(pdf_file)
-        pdf_path = tmp.name
+def run_qa(pdf_path, question):
+    if pdf_path is None or question.strip() == "":
+        return "Please upload a PDF and enter a question."
 
+    # Load PDF
     loader = PyPDFLoader(pdf_path)
     docs = loader.load()
 
+    # Split into chunks
     splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
     chunks = splitter.split_documents(docs)
 
+    # Create embeddings + vector store
     embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
     vectordb = FAISS.from_documents(chunks, embeddings)
 
+    # LLM
     llm = ChatOpenAI(temperature=0)
 
+    # Retrieval QA chain
     qa = RetrievalQA.from_chain_type(
         llm=llm,
         retriever=vectordb.as_retriever(),
         return_source_documents=True
     )
 
-    result = qa(question)
+    # Newer LangChain-safe call
+    result = qa.invoke({"query": question})
 
-    sources = "\n\n".join(
-        [doc.page_content[:500] for doc in result["source_documents"][:2]]
-    )
+    answer_text = result.get("result", "")
+    source_docs = result.get("source_documents", [])
+
+    sources = "\n\n".join([d.page_content[:500] for d in source_docs[:2]])
+
+    return f"### Answer\n{answer_text}\n\n---\n### Sources\n{sources}"
 
-    return f"### Answer\n{result['result']}\n\n---\n### Sources\n{sources}"
 
 with gr.Blocks(title="Agentic Document Intelligence") as demo:
-    gr.Markdown(
-        "# 📄 Agentic Document Intelligence\nUpload a PDF and ask questions using RAG."
-    )
+    gr.Markdown("# 📄 Agentic Document Intelligence\nUpload a PDF and ask questions using RAG.")
 
-    pdf = gr.File(label="Upload PDF", type="binary")
+    pdf = gr.File(label="Upload PDF", type="filepath")
     question = gr.Textbox(label="Ask a question")
     output = gr.Markdown()