import sys
import os
import pandas as pd
import json
import gradio as gr
from typing import List, Tuple, Dict, Any, Union
import hashlib
import shutil
import re
from datetime import datetime
import time
from concurrent.futures import ThreadPoolExecutor, as_completed

# Configuration and setup
persistent_dir = "/data/hf_cache"
os.makedirs(persistent_dir, exist_ok=True)

model_cache_dir = os.path.join(persistent_dir, "txagent_models")
tool_cache_dir = os.path.join(persistent_dir, "tool_cache")
file_cache_dir = os.path.join(persistent_dir, "cache")
report_dir = os.path.join(persistent_dir, "reports")

for directory in [model_cache_dir, tool_cache_dir, file_cache_dir, report_dir]:
    os.makedirs(directory, exist_ok=True)

os.environ["HF_HOME"] = model_cache_dir
os.environ["TRANSFORMERS_CACHE"] = model_cache_dir

current_dir = os.path.dirname(os.path.abspath(__file__))
src_path = os.path.abspath(os.path.join(current_dir, "src"))
sys.path.insert(0, src_path)

from txagent.txagent import TxAgent

# Constants
MAX_MODEL_TOKENS = 32768
MAX_CHUNK_TOKENS = 8192
MAX_NEW_TOKENS = 2048
PROMPT_OVERHEAD = 500

def clean_response(text: str) -> str:
    try:
        text = text.encode('utf-8', 'surrogatepass').decode('utf-8')
    except UnicodeError:
        text = text.encode('utf-8', 'replace').decode('utf-8')
    text = re.sub(r"\[.*?\]|\bNone\b", "", text, flags=re.DOTALL)
    text = re.sub(r"\n{3,}", "\n\n", text)
    text = re.sub(r"[^\n#\-\*\w\s\.,:\(\)]+", "", text)
    return text.strip()

def estimate_tokens(text: str) -> int:
    return len(text) // 3.5 + 1

def extract_text_from_excel(file_path: str) -> str:
    all_text = []
    try:
        xls = pd.ExcelFile(file_path)
        for sheet_name in xls.sheet_names:
            df = xls.parse(sheet_name)
            df = df.astype(str).fillna("")
            rows = df.apply(lambda row: " | ".join(row), axis=1)
            sheet_text = [f"[{sheet_name}] {line}" for line in rows]
            all_text.extend(sheet_text)
    except Exception as e:
        raise ValueError(f"Failed to extract text from Excel file: {str(e)}")
    return "\n".join(all_text)

def split_text_into_chunks(text: str, max_tokens: int = MAX_CHUNK_TOKENS) -> List[str]:
    effective_max_tokens = max_tokens - PROMPT_OVERHEAD
    if effective_max_tokens <= 0:
        raise ValueError(f"Effective max tokens ({effective_max_tokens}) must be positive.")
    lines = text.split("\n")
    chunks, current_chunk, current_tokens = [], [], 0
    for line in lines:
        line_tokens = estimate_tokens(line)
        if current_tokens + line_tokens > effective_max_tokens:
            if current_chunk:
                chunks.append("\n".join(current_chunk))
            current_chunk, current_tokens = [line], line_tokens
        else:
            current_chunk.append(line)
            current_tokens += line_tokens
    if current_chunk:
        chunks.append("\n".join(current_chunk))
    return chunks

def build_prompt_from_text(chunk: str) -> str:
    return f"""
### Unstructured Clinical Records

You are reviewing unstructured, mixed-format clinical documentation from various forms, tables, and sheets.

**Objective:** Identify patterns, missed diagnoses, inconsistencies, and follow-up gaps.

Here is the extracted content chunk:

{chunk}

Please analyze the above and provide:
- Diagnostic Patterns
- Medication Issues
- Missed Opportunities
- Inconsistencies
- Follow-up Recommendations
"""

def init_agent():
    default_tool_path = os.path.abspath("data/new_tool.json")
    target_tool_path = os.path.join(tool_cache_dir, "new_tool.json")
    if not os.path.exists(target_tool_path):
        shutil.copy(default_tool_path, target_tool_path)
    agent = TxAgent(
        model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B",
        rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
        tool_files_dict={"new_tool": target_tool_path},
        force_finish=True,
        enable_checker=True,
        step_rag_num=4,
        seed=100,
        additional_default_tools=[]
    )
    agent.init_model()
    return agent

def process_final_report(agent, file, chatbot_state: List[Dict[str, str]]) -> Tuple[List[Dict[str, str]], Union[str, None]]:
    messages = chatbot_state if chatbot_state else []
    report_path = None

    if file is None or not hasattr(file, "name"):
        messages.append({"role": "assistant", "content": "❌ Please upload a valid Excel file before analyzing."})
        return messages, report_path

    try:
        messages.append({"role": "user", "content": f"Processing Excel file: {os.path.basename(file.name)}"})
        messages.append({"role": "assistant", "content": "⏳ Extracting and analyzing data..."})
        extracted_text = extract_text_from_excel(file.name)
        chunks = split_text_into_chunks(extracted_text)
        chunk_responses = [None] * len(chunks)

        def analyze_chunk(index: int, chunk: str) -> Tuple[int, str]:
            prompt = build_prompt_from_text(chunk)
            prompt_tokens = estimate_tokens(prompt)
            if prompt_tokens > MAX_MODEL_TOKENS:
                return index, f"❌ Chunk {index+1} prompt too long ({prompt_tokens} tokens). Skipping..."
            response = ""
            try:
                for result in agent.run_gradio_chat(
                    message=prompt,
                    history=[],
                    temperature=0.2,
                    max_new_tokens=MAX_NEW_TOKENS,
                    max_token=MAX_MODEL_TOKENS,
                    call_agent=False,
                    conversation=[],
                ):
                    if isinstance(result, str):
                        response += result
                    elif hasattr(result, "content"):
                        response += result.content
                    elif isinstance(result, list):
                        for r in result:
                            if hasattr(r, "content"):
                                response += r.content
            except Exception as e:
                return index, f"❌ Error analyzing chunk {index+1}: {str(e)}"
            return index, clean_response(response)

        with ThreadPoolExecutor(max_workers=1) as executor:
            futures = [executor.submit(analyze_chunk, i, chunk) for i, chunk in enumerate(chunks)]
            for future in as_completed(futures):
                i, result = future.result()
                chunk_responses[i] = result
                if not result.startswith("❌"):
                    messages.append({"role": "assistant", "content": f"✅ Chunk {i+1} analysis complete"})
                else:
                    messages.append({"role": "assistant", "content": result})

        valid_responses = [res for res in chunk_responses if not res.startswith("❌")]
        if not valid_responses:
            messages.append({"role": "assistant", "content": "❌ No valid chunk responses to summarize."})
            return messages, report_path

        summary = ""
        current_summary_tokens = 0
        for i, response in enumerate(valid_responses):
            response_tokens = estimate_tokens(response)
            if current_summary_tokens + response_tokens > MAX_MODEL_TOKENS - PROMPT_OVERHEAD - MAX_NEW_TOKENS:
                summary_prompt = f"Summarize the following analysis:\n\n{summary}\n\nProvide a concise summary."
                summary_response = ""
                try:
                    for result in agent.run_gradio_chat(
                        message=summary_prompt,
                        history=[],
                        temperature=0.2,
                        max_new_tokens=MAX_NEW_TOKENS,
                        max_token=MAX_MODEL_TOKENS,
                        call_agent=False,
                        conversation=[],
                    ):
                        if isinstance(result, str):
                            summary_response += result
                        elif hasattr(result, "content"):
                            summary_response += result.content
                        elif isinstance(result, list):
                            for r in result:
                                if hasattr(r, "content"):
                                    summary_response += r.content
                    summary = clean_response(summary_response)
                    current_summary_tokens = estimate_tokens(summary)
                except Exception as e:
                    messages.append({"role": "assistant", "content": f"❌ Error summarizing intermediate results: {str(e)}"})
                    return messages, report_path
            summary += f"\n\n### Chunk {i+1} Analysis\n{response}"
            current_summary_tokens += response_tokens

        final_prompt = f"Summarize the key findings from the following analyses:\n\n{summary}"
        messages.append({"role": "assistant", "content": "📊 Generating final report..."})

        final_report_text = ""
        try:
            for result in agent.run_gradio_chat(
                message=final_prompt,
                history=[],
                temperature=0.2,
                max_new_tokens=MAX_NEW_TOKENS,
                max_token=MAX_MODEL_TOKENS,
                call_agent=False,
                conversation=[],
            ):
                if isinstance(result, str):
                    final_report_text += result
                elif hasattr(result, "content"):
                    final_report_text += result.content
                elif isinstance(result, list):
                    for r in result:
                        if hasattr(r, "content"):
                            final_report_text += r.content
        except Exception as e:
            messages.append({"role": "assistant", "content": f"❌ Error generating final report: {str(e)}"})
            return messages, report_path

        final_report = f"# 🧠 Final Patient Report\n\n{clean_response(final_report_text)}"
        messages[-1]["content"] = f"📊 Final Report:\n\n{clean_response(final_report_text)}"

        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        report_path = os.path.join(report_dir, f"report_{timestamp}.md")

        with open(report_path, 'w') as f:
            f.write(final_report)

        messages.append({"role": "assistant", "content": f"✅ Report generated and saved: report_{timestamp}.md"})

    except Exception as e:
        messages.append({"role": "assistant", "content": f"❌ Error processing file: {str(e)}"})

    return messages, report_path

def create_ui(agent):
    with gr.Blocks(
        title="Patient History Chat",
        css="""
        .gradio-container {
            max-width: 900px !important;
            margin: auto;
            font-family: 'Segoe UI', sans-serif;
            background-color: #f8f9fa;
        }
        .gr-button.primary {
            background: linear-gradient(to right, #4b6cb7, #182848);
            color: white;
            border: none;
            border-radius: 8px;
        }
        .gr-button.primary:hover {
            background: linear-gradient(to right, #3552a3, #101a3e);
        }
        .gr-file-upload, .gr-chatbot, .gr-markdown {
            background-color: white;
            border-radius: 10px;
            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
            padding: 1rem;
        }
        .gr-chatbot {
            border-left: 4px solid #4b6cb7;
        }
        .gr-file-upload input {
            font-size: 0.95rem;
        }
        .chat-message-content p {
            margin: 0.3em 0;
        }
        .chat-message-content ul {
            padding-left: 1.2em;
            margin: 0.4em 0;
        }
        """
    ) as demo:
        gr.Markdown("""
        <h2 style='color:#182848'>🏥 Patient History Analysis Tool</h2>
        <p style='color:#444;'>Upload an Excel file containing clinical data. The assistant will analyze it for patterns, inconsistencies, and recommendations.</p>
        """)

        with gr.Row():
            with gr.Column(scale=3):
                chatbot = gr.Chatbot(
                    label="Clinical Assistant",
                    show_copy_button=True,
                    height=600,
                    type="messages",
                    avatar_images=(None, "https://i.imgur.com/6wX7Zb4.png"),
                    render_markdown=True
                )
            with gr.Column(scale=1):
                file_upload = gr.File(label="Upload Excel File", file_types=[".xlsx"], height=100)
                analyze_btn = gr.Button("🧠 Analyze Patient History", variant="primary", elem_classes="primary")
                report_output = gr.File(label="Download Report", visible=False, interactive=False)

        chatbot_state = gr.State(value=[])

        def update_ui(file, current_state):
            messages, report_path = process_final_report(agent, file, current_state)
            formatted_messages = []
            for msg in messages:
                role = msg.get("role")
                content = msg.get("content", "")
                if role == "assistant":
                    content = content.replace("- ", "\n- ")
                    content = f"<div class='chat-message-content'>{content}</div>"
                formatted_messages.append({"role": role, "content": content})
            report_update = gr.update(visible=report_path is not None, value=report_path)
            return formatted_messages, report_update, formatted_messages

        analyze_btn.click(fn=update_ui, inputs=[file_upload, chatbot_state], outputs=[chatbot, report_output, chatbot_state], api_name="analyze")

    return demo

if __name__ == "__main__":
    try:
        agent = init_agent()
        demo = create_ui(agent)
        demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True, allowed_paths=["/data/hf_cache/reports"], share=False)
    except Exception as e:
        print(f"Error: {str(e)}")
        sys.exit(1)