Create app.py

app.py

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+import gradio as gr
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+
+# Load documents from "data" directory and build the index
+documents = SimpleDirectoryReader("data").load_data()
+index = VectorStoreIndex.from_documents(documents=documents)
+query_engine = index.as_query_engine()
+
+# Function to handle user queries
+def query_document(query):
+    response = query_engine.query(query)
+    return str(response)
+
+# Build Gradio app using Blocks for better layout and UX
+with gr.Blocks(css=".gradio-container {font-family: 'Arial'; background-color: #fafafa;}") as demo:
+    gr.Markdown("<h1 style='text-align: center;'>📄 RAG Application with LlamaIndex</h1>")
+    gr.Markdown(
+        "Ask questions about the documents stored in the local directory. "
+        "This app uses Retrieval-Augmented Generation (RAG) powered by LlamaIndex."
+    )
+
+    with gr.Box():
+        query_input = gr.Textbox(
+            label="Enter your query",
+            placeholder="e.g., What is the refund policy mentioned in the document?",
+            lines=3
+        )
+        submit_btn = gr.Button("Submit", variant="primary")
+        response_output = gr.Textbox(label="Response", lines=8)
+
+    submit_btn.click(fn=query_document, inputs=query_input, outputs=response_output)
+
+# Run the app
+if __name__ == "__main__":
+    demo.launch()