Update app.py

app.py

@@ -1,35 +1,25 @@
 import gradio as gr
 from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
 
-# Load documents from "data" directory and build the index
+# Load documents and build the index
 documents = SimpleDirectoryReader("data").load_data()
-index = VectorStoreIndex.from_documents(documents=documents)
+index = VectorStoreIndex.from_documents(documents)
 query_engine = index.as_query_engine()
 
-# Function to handle user queries
+# Define the function that handles the query
 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."
-    )
+# Create a simple Gradio interface
+interface = gr.Interface(
+    fn=query_document,
+    inputs=gr.Textbox(label="Enter your question", lines=2, placeholder="What do you want to know from the documents?"),
+    outputs=gr.Textbox(label="Answer"),
+    title="Document Q&A with LlamaIndex",
+    description="Ask a question and get an answer based on documents stored in the 'data' folder."
+)
 
-    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
+# Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()