Upload app.py

app.py

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+import gradio as gr
+from rag_core import scrape_and_process_url, answer_question
+
+# Global state to store the processing status
+processing_status = ""
+
+def process_url_and_update_status(url: str):
+    global processing_status
+    processing_status = "Processing... Please wait."
+    gr.Info(processing_status)
+    result = scrape_and_process_url(url)
+    processing_status = result
+    return processing_status
+
+def get_answer_from_rag(question: str):
+    if "Successfully scraped" not in processing_status:
+        return "Please scrape and process a URL first before asking questions."
+    return answer_question(question)
+
+with gr.Blocks(title="RAG-Powered Documentation Assistant") as demo:
+    gr.Markdown("# 📚 RAG-Powered Documentation Assistant\n\n"\
+               "Enter a documentation URL, then ask questions about its content.")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            url_input = gr.Textbox(label="Documentation URL", placeholder="e.g., https://docs.python.org/3/")
+            process_button = gr.Button("Scrape & Process URL")
+        with gr.Column(scale=1):
+            status_output = gr.Markdown(value=processing_status, label="Processing Status")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            question_input = gr.Textbox(label="Your Question", placeholder="e.g., How to define a function?")
+            ask_button = gr.Button("Ask Question")
+        with gr.Column(scale=1):
+            answer_output = gr.Markdown(label="Answer")
+
+    process_button.click(
+        process_url_and_update_status,
+        inputs=[url_input],
+        outputs=[status_output]
+    )
+
+    ask_button.click(
+        get_answer_from_rag,
+        inputs=[question_input],
+        outputs=[answer_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()
+