import sys
import os
import pandas as pd
import pdfplumber
import gradio as gr
from tabulate import tabulate

# ✅ Add src to Python path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")))
from txagent.txagent import TxAgent

def extract_all_text_from_csv_or_excel(file_path, progress=None, index=0, total=1):
    try:
        if file_path.endswith(".csv"):
            df = pd.read_csv(file_path, low_memory=False)
        elif file_path.endswith((".xls", ".xlsx")):
            df = pd.read_excel(file_path)
        else:
            return f"Unsupported spreadsheet format: {file_path}"

        if progress:
            progress((index + 1) / total, desc=f"Processed table: {os.path.basename(file_path)}")

        if "Booking Number" in df.columns:
            groups = df.groupby("Booking Number")
        elif "Form Name" in df.columns:
            groups = df.groupby("Form Name")
        else:
            return tabulate(df, headers="keys", tablefmt="github", showindex=False).encode("utf-8", errors="replace").decode("utf-8")

        result = []
        for group_name, group_df in groups:
            result.append(f"\n### Group: {group_name}\n")
            formatted = tabulate(group_df, headers="keys", tablefmt="github", showindex=False)
            result.append(formatted.encode("utf-8", errors="replace").decode("utf-8"))

        return "\n".join(result)

    except Exception as e:
        return f"Error parsing file: {e}"

def extract_all_text_from_pdf(file_path, progress=None, index=0, total=1):
    extracted = []
    try:
        with pdfplumber.open(file_path) as pdf:
            num_pages = len(pdf.pages)
            for i, page in enumerate(pdf.pages):
                tables = page.extract_tables()
                for table in tables:
                    for row in table:
                        if any(row):
                            extracted.append("\t".join([cell or "" for cell in row]))
                if progress:
                    progress((index + i / num_pages) / total, desc=f"Parsing PDF: {os.path.basename(file_path)} ({i+1}/{num_pages})")
        return "\n".join(extracted).encode("utf-8", errors="replace").decode("utf-8")
    except Exception as e:
        return f"Error parsing PDF: {e}"

def create_ui(agent: TxAgent):
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("<h1 style='text-align: center;'>📋 CPS: Clinical Patient Support System</h1>")
        chatbot = gr.Chatbot(label="CPS Assistant", height=600, type="messages")

        file_upload = gr.File(
            label="Upload Medical File",
            file_types=[".pdf", ".txt", ".docx", ".jpg", ".png", ".csv", ".xls", ".xlsx"],
            file_count="multiple"
        )
        message_input = gr.Textbox(placeholder="Ask a biomedical question or just upload the files...", show_label=False)
        send_button = gr.Button("Send", variant="primary")
        conversation_state = gr.State([])

        def handle_chat(message, history, conversation, uploaded_files, progress=gr.Progress()):
            context = (
                "You are an expert clinical AI assistant reviewing medical form or interview data. "
                "Your job is to analyze this data and reason about any information or red flags that a human doctor might have overlooked. "
                "Provide a **detailed and structured response**, including examples, supporting evidence from the form, and clinical rationale for why these items matter. "
                "Ensure the output is informative and helpful for improving patient care. "
                "Do not hallucinate. Base the response only on the provided form content. "
                "End with a section labeled '🧠 Final Analysis' where you summarize key findings the doctor may have missed."
            )

            if uploaded_files:
                extracted_text = ""
                total_files = len(uploaded_files)

                for index, file in enumerate(uploaded_files):
                    path = file.name
                    if path.endswith((".csv", ".xls", ".xlsx")):
                        extracted_text += extract_all_text_from_csv_or_excel(path, progress, index, total_files) + "\n"
                    elif path.endswith(".pdf"):
                        extracted_text += extract_all_text_from_pdf(path, progress, index, total_files) + "\n"
                    else:
                        extracted_text += f"(Uploaded file: {os.path.basename(path)})\n"
                        if progress:
                            progress((index + 1) / total_files, desc=f"Skipping unsupported file: {os.path.basename(path)}")

                message = f"{context}\n\n---\n{extracted_text.strip()}\n---\n\nBegin your reasoning."

            generator = agent.run_gradio_chat(
                message=message,
                history=history,
                temperature=0.3,
                max_new_tokens=1024,
                max_token=8192,
                call_agent=False,
                conversation=conversation,
                uploaded_files=uploaded_files,
                max_round=30
            )
            for update in generator:
                yield update

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

        gr.Examples([
            ["Upload your medical form and ask what the doctor might’ve missed."],
            ["This patient was treated with antibiotics for UTI. What else should we check?"],
            ["Is there anything abnormal in the attached blood work report?"]
        ], inputs=message_input)

    return demo