import os
import sys
import gradio as gr
from multiprocessing import freeze_support
import importlib
import inspect
import json

# Fix path to include src
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src"))

# Reload TxAgent from txagent.py
import txagent.txagent
importlib.reload(txagent.txagent)
from txagent.txagent import TxAgent

# Debug info
print(">>> TxAgent loaded from:", inspect.getfile(TxAgent))
print(">>> TxAgent has run_gradio_chat:", hasattr(TxAgent, "run_gradio_chat"))

# Env vars
current_dir = os.path.abspath(os.path.dirname(__file__))
os.environ["MKL_THREADING_LAYER"] = "GNU"
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Model config
model_name = "mims-harvard/TxAgent-T1-Llama-3.1-8B"
rag_model_name = "mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B"
new_tool_files = {
    "new_tool": os.path.join(current_dir, "data", "new_tool.json")
}

# Sample questions
question_examples = [
    ["Given a patient with WHIM syndrome on prophylactic antibiotics, is it advisable to co-administer Xolremdi with fluconazole?"],
    ["What treatment options exist for HER2+ breast cancer resistant to trastuzumab?"]
]

# Helper: format assistant responses in collapsible panels

def format_collapsible(content, tool_name=None):
    # Try parsing if it's a JSON string
    if isinstance(content, str):
        try:
            content = json.loads(content)
        except Exception:
            pass

    if isinstance(content, dict) and "results" in content:
        readable = ""
        for i, result in enumerate(content["results"], 1):
            readable += f"\n🔹 **Result {i}:**\n"
            for key, value in result.items():
                key_str = key.replace("openfda.", "").replace("_", " ").capitalize()
                val_str = ", ".join(value) if isinstance(value, list) else str(value)
                readable += f"- **{key_str}**: {val_str}\n"
        formatted = readable.strip()
    elif isinstance(content, (dict, list)):
        formatted = json.dumps(content, indent=2)
    else:
        formatted = str(content)

    title = f"{tool_name or 'Answer'}"
    return (
        "<details style='border: 1px solid #aaa; border-radius: 8px; padding: 10px; margin: 12px 0; background-color: #f8f8f8;'>"
        f"<summary style='font-weight: bold; font-size: 16px; color: #333;'>{title}</summary>"
        f"<div style='white-space: pre-wrap; font-family: sans-serif; color: #222; padding-top: 6px;'>{formatted}</div>"
        "</details>"
    )

# === UI setup
def create_ui(agent):
    with gr.Blocks(css="body { background-color: #f5f5f5; font-family: sans-serif; }") as demo:
        gr.Markdown("<h1 style='text-align: center;'>TxAgent: Therapeutic Reasoning</h1>")
        gr.Markdown("<p style='text-align: center;'>Ask biomedical or therapeutic questions. Powered by step-by-step reasoning and tools.</p>")

        conversation_state = gr.State([])

        chatbot = gr.Chatbot(label="TxAgent", height=600, type="messages")
        message_input = gr.Textbox(placeholder="Ask your biomedical question...", show_label=False)
        send_button = gr.Button("Send", variant="primary")

        # Main handler
        def handle_chat(message, history, conversation):
            generator = agent.run_gradio_chat(
                message=message,
                history=history,
                temperature=0.3,
                max_new_tokens=1024,
                max_token=8192,
                call_agent=False,
                conversation=conversation,
                max_round=30
            )

            for update in generator:
                formatted = []
                for m in update:
                    role = m["role"] if isinstance(m, dict) else getattr(m, "role", "assistant")
                    content = m["content"] if isinstance(m, dict) else getattr(m, "content", "")
                    tool_name = m.get("tool_name") if isinstance(m, dict) else getattr(m, "tool_name", None)

                    if role == "assistant":
                        content = format_collapsible(content, tool_name)

                    formatted.append({"role": role, "content": content})
                yield formatted

        inputs = [message_input, chatbot, conversation_state]
        send_button.click(fn=handle_chat, inputs=inputs, outputs=chatbot)
        message_input.submit(fn=handle_chat, inputs=inputs, outputs=chatbot)

        gr.Examples(examples=question_examples, inputs=message_input)
        gr.Markdown("<p style='font-size: 12px; text-align: center; color: gray;'>This demo is for research purposes only and does not provide medical advice.</p>")

    return demo

# === Entry point
if __name__ == "__main__":
    freeze_support()
    try:
        agent = TxAgent(
            model_name=model_name,
            rag_model_name=rag_model_name,
            tool_files_dict=new_tool_files,
            force_finish=True,
            enable_checker=True,
            step_rag_num=10,
            seed=100,
            additional_default_tools=[]
        )
        agent.init_model()

        if not hasattr(agent, "run_gradio_chat"):
            raise AttributeError("TxAgent missing run_gradio_chat")

        demo = create_ui(agent)
        demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=True, show_error=True)

    except Exception as e:
        print(f"\u274c App failed to start: {e}")
        raise