app.py

import os
import gradio as gr
from dotenv import load_dotenv
import time

from implementation.chat import answer_question

load_dotenv(override=True)


def format_context(context):
    if not context:
        return "No relevant context found."
    
    result = "### 📚 Relevant Context\n\n"
    for doc in context:
        source = doc.metadata.get('source', 'Unknown Source')
        result += f"**Source: {source}**\n\n"
        result += f"{doc.page_content}\n\n"
        result += "---\n\n"
    return result


def extract_text(content):
    if isinstance(content, str):
        return content
    if isinstance(content, list):
        text_parts = []
        for part in content:
            if isinstance(part, dict) and part.get("type") == "text":
                text_parts.append(part.get("text", ""))
        return " ".join(text_parts)
    return str(content)


def chat(history):
    try:
        raw_last_message = history[-1]["content"]
        last_message = extract_text(raw_last_message)

        # Convert entire history to string content for the backend
        clean_history = []
        for msg in history[:-1]:
            clean_history.append({
                "role": msg["role"],
                "content": extract_text(msg["content"])
            })

        answer, context = answer_question(last_message, clean_history)

        # Stream the final answer into the UI (typewriter-style).
        # This is robust even when the backend does routing/tool-calls.
        history.append({"role": "assistant", "content": ""})
        context_md = ""
        yield history, context_md

        chunk_size = int(os.getenv("STREAM_CHUNK_SIZE", "24"))
        delay_s = float(os.getenv("STREAM_DELAY_S", "0.01"))
        built = ""
        for i in range(0, len(answer), max(1, chunk_size)):
            built = answer[: i + chunk_size]
            history[-1]["content"] = built
            yield history, context_md
            if delay_s > 0:
                time.sleep(delay_s)

        yield history, format_context(context)
    except Exception as e:
        import traceback
        traceback.print_exc()
        history.append({"role": "assistant", "content": f"Error: {str(e)}"})
        yield history, "Error occurred."



def put_message_in_chatbot(message, history):
    new_history = history + [{"role": "user", "content": message}]
    return "", new_history

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

with gr.Blocks(title="Kharisma Rizki Wijanarko - AI Assistant") as demo:
    gr.Markdown(
        """
        # 👨‍💻 Kharisma Rizki Wijanarko - AI Assistant
        Welcome! I’m an AI assistant that can answer questions about Rizki’s career, background, skills, and projects—and even help you connect with him for opportunities or collaborations.
        """
    )

    with gr.Row():
        with gr.Column(scale=2):
            chatbot = gr.Chatbot(
                label="💬 Conversation",
                height=550,
                show_label=False,
                avatar_images=(None, "https://api.dicebear.com/7.x/avataaars/svg?seed=Rizki"),
            )
            with gr.Row():
                message = gr.Textbox(
                    placeholder="Ask me about Rizki's projects, skills, or experience...",
                    show_label=False,
                    scale=7,
                    container=False
                )
                submit_btn = gr.Button("Send", variant="primary", scale=1)
            
            gr.Examples(
                examples=[
                    "What is Rizki's professional background?",
                    "Tell me about Rizki's technical skills.",
                    "What kind of projects has Rizki worked on?",
                    "How can I contact Rizki?",
                ],
                inputs=message,
                label="Try asking:"
            )

        with gr.Column(scale=1):
            with gr.Accordion("🔍 Behind the scenes: Retrieved Context", open=False):
                context_markdown = gr.Markdown(
                    value="*Retrieved context will appear here when you ask a question*",
                )

    submit_btn.click(
        put_message_in_chatbot, inputs=[message, chatbot], outputs=[message, chatbot]
    ).then(chat, inputs=chatbot, outputs=[chatbot, context_markdown], queue=True)

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

if __name__ == "__main__":
    # Hugging Face Spaces expects the server to bind to 0.0.0.0 and listen on $PORT.
    demo.launch(
        theme=theme,
        # HF Spaces health-check can fail with Gradio's experimental SSR.
        ssr_mode=False,
        server_name="0.0.0.0",
        server_port=int(os.getenv("PORT", "7860")),
        show_error=True,
    )
    print("APP STARTED")