Create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+import tempfile
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.chat_models 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
+
+    loader = PyPDFLoader(pdf_path)
+    docs = loader.load()
+
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
+    chunks = splitter.split_documents(docs)
+
+    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    vectordb = FAISS.from_documents(chunks, embeddings)
+
+    llm = ChatOpenAI(temperature=0)
+
+    qa = RetrievalQA.from_chain_type(
+        llm=llm,
+        retriever=vectordb.as_retriever(),
+        return_source_documents=True
+    )
+
+    result = qa(question)
+
+    sources = "\n\n".join(
+        [doc.page_content[:500] for doc in result["source_documents"][:2]]
+    )
+
+    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."
+    )
+
+    pdf = gr.File(label="Upload PDF", type="binary")
+    question = gr.Textbox(label="Ask a question")
+    output = gr.Markdown()
+
+    btn = gr.Button("Run")
+    btn.click(run_qa, inputs=[pdf, question], outputs=output)
+
+demo.launch()