app_advanced.py

import gradio as gr
from dotenv import load_dotenv
from implementation.answer_advanced import answer_question

load_dotenv(override=True)


def format_context(context):
    if not context:
        return "<h2 style='color: #ff7800;'>📚 Retrieved Context</h2>\n\n*No context retrieved*"
    
    result = "<h2 style='color: #ff7800;'>📚 Retrieved Context (Advanced Reranked)</h2>\n\n"
    for i, doc in enumerate(context, 1):
        source = doc.metadata.get('source', 'unknown')
        result += f"<div style='border-left: 3px solid #ff7800; padding-left: 10px; margin-bottom: 15px;'>"
        result += f"<strong style='color: #ff7800;'>📄 Document {i}:</strong> "
        result += f"<span style='color: #666;'>Source: {source}</span><br>\n"
        result += f"<div style='margin-top: 5px; font-size: 0.95em;'>{doc.page_content}</div>\n"
        result += f"</div>\n\n"
    return result


def chat(history):
    if not history:
        return history, "*No context retrieved*"

    # Strict structural data sanitation for modern Gradio versions
    cleaned_history = []
    for entry in history:
        content = entry["content"]
        if isinstance(content, dict):
            text_content = content.get("text", "")
        else:
            text_content = str(content)
            
        cleaned_history.append({
            "role": entry["role"],
            "content": text_content
        })

    last_message = cleaned_history[-1]["content"]
    prior = cleaned_history[:-1]
    
    # Process through Advanced RAG Pipeline
    answer, context = answer_question(last_message, prior)
    history.append({"role": "assistant", "content": answer})
    return history, format_context(context)


def main():
    def put_message_in_chatbot(message, history):
        if not message or message.strip() == "":
            return "", history
        return "", history + [{"role": "user", "content": message}]

    theme = gr.themes.Soft(font=["Inter", "system-ui", "sans-serif"])

    with gr.Blocks() as ui:
        gr.Markdown("# 🏢 Insurellm Expert Assistant (Advanced RAG)")
        gr.Markdown("Leveraging Query-Rewriting, Multi-Query expansion, and local LLM Reranking.")

        with gr.Row():
            with gr.Column(scale=1):
                chatbot = gr.Chatbot(label="💬 Conversation", height=600)
                message = gr.Textbox(
                    label="Your Question",
                    placeholder="Ask anything about Insurellm...",
                    show_label=False,
                )

            with gr.Column(scale=1):
                context_markdown = gr.Markdown(
                    value="*Retrieved context will appear here*",
                    container=True,
                    height=600,
                )

        message.submit(
            put_message_in_chatbot, inputs=[message, chatbot], outputs=[message, chatbot]
        ).then(chat, inputs=chatbot, outputs=[chatbot, context_markdown])

    ui.launch(inbrowser=True, theme=theme)


if __name__ == "__main__":
    main()