#
# SPDX-FileCopyrightText: Hadad <hadad@linuxmail.org>
# SPDX-License-Identifier: Apache-2.0
#

import asyncio
import docx
import gradio as gr
import httpx
import json
import os
import pandas as pd
import pdfplumber
import pytesseract
import random
import requests
import threading
import uuid
import zipfile
import io

from PIL import Image
from pathlib import Path
from pptx import Presentation
from openpyxl import load_workbook

os.system("apt-get update -q -y && apt-get install -q -y tesseract-ocr tesseract-ocr-eng tesseract-ocr-ind libleptonica-dev libtesseract-dev")

INTERNAL_AI_GET_SERVER = os.getenv("INTERNAL_AI_GET_SERVER")
INTERNAL_TRAINING_DATA = os.getenv("INTERNAL_TRAINING_DATA")

SYSTEM_PROMPT_MAPPING = json.loads(os.getenv("SYSTEM_PROMPT_MAPPING", "{}"))
SYSTEM_PROMPT_DEFAULT = os.getenv("DEFAULT_SYSTEM")

LINUX_SERVER_HOSTS = [h for h in json.loads(os.getenv("LINUX_SERVER_HOST", "[]")) if h]
LINUX_SERVER_HOSTS_MARKED = set()
LINUX_SERVER_HOSTS_ATTEMPTS = {}

LINUX_SERVER_PROVIDER_KEYS = [k for k in json.loads(os.getenv("LINUX_SERVER_PROVIDER_KEY", "[]")) if k]
LINUX_SERVER_PROVIDER_KEYS_MARKED = set()
LINUX_SERVER_PROVIDER_KEYS_ATTEMPTS = {}

LINUX_SERVER_ERRORS = set(map(int, os.getenv("LINUX_SERVER_ERROR", "").split(",")))

AI_TYPES = {f"AI_TYPE_{i}": os.getenv(f"AI_TYPE_{i}") for i in range(1, 8)}

RESPONSES = {f"RESPONSE_{i}": os.getenv(f"RESPONSE_{i}") for i in range(1, 11)}

MODEL_MAPPING = json.loads(os.getenv("MODEL_MAPPING", "{}"))
MODEL_CONFIG = json.loads(os.getenv("MODEL_CONFIG", "{}"))
MODEL_CHOICES = list(MODEL_MAPPING.values())

DEFAULT_CONFIG = json.loads(os.getenv("DEFAULT_CONFIG", "{}"))
DEFAULT_MODEL_KEY = list(MODEL_MAPPING.keys())[0] if MODEL_MAPPING else None

META_TAGS = os.getenv("META_TAGS")

ALLOWED_EXTENSIONS = json.loads(os.getenv("ALLOWED_EXTENSIONS", "[]"))

class SessionWithID(requests.Session):
    def __init__(sess):
        super().__init__()
        sess.session_id = str(uuid.uuid4())

def create_session():
    return SessionWithID()

def ensure_stop_event(sess):
    if not hasattr(sess, "stop_event"):
        sess.stop_event = asyncio.Event()

def get_available_items(items, marked):
    a = [i for i in items if i not in marked]
    random.shuffle(a)
    return a

def marked_item(item, marked, attempts):
    marked.add(item)
    attempts[item] = attempts.get(item, 0) + 1
    if attempts[item] >= 3:
        def remove():
            marked.discard(item)
            attempts.pop(item, None)
        threading.Timer(300, remove).start()

def get_model_key(display):
    return next((k for k, v in MODEL_MAPPING.items() if v == display), DEFAULT_MODEL_KEY)

def extract_pdf_content(fp):
    content = ""
    try:
        with pdfplumber.open(fp) as pdf:
            for page in pdf.pages:
                text = page.extract_text() or ""
                content += text + "\n"
                if page.images:
                    img_obj = page.to_image(resolution=300)
                    for img in page.images:
                        bbox = (img["x0"], img["top"], img["x1"], img["bottom"])
                        cropped = img_obj.original.crop(bbox)
                        ocr_text = pytesseract.image_to_string(cropped)
                        if ocr_text.strip():
                            content += ocr_text + "\n"
                tables = page.extract_tables()
                for table in tables:
                    for row in table:
                        cells = [str(cell) for cell in row if cell is not None]
                        if cells:
                            content += "\t".join(cells) + "\n"
    except Exception as e:
        content += f"{fp}: {e}"
    return content.strip()

def extract_docx_content(fp):
    content = ""
    try:
        doc = docx.Document(fp)
        for para in doc.paragraphs:
            content += para.text + "\n"
        for table in doc.tables:
            for row in table.rows:
                cells = [cell.text for cell in row.cells]
                content += "\t".join(cells) + "\n"
        with zipfile.ZipFile(fp) as z:
            for file in z.namelist():
                if file.startswith("word/media/"):
                    data = z.read(file)
                    try:
                        img = Image.open(io.BytesIO(data))
                        ocr_text = pytesseract.image_to_string(img)
                        if ocr_text.strip():
                            content += ocr_text + "\n"
                    except:
                        pass
    except Exception as e:
        content += f"{fp}: {e}"
    return content.strip()

def extract_excel_content(fp):
    content = ""
    try:
        sheets = pd.read_excel(fp, sheet_name=None)
        for name, df in sheets.items():
            content += f"Sheet: {name}\n"
            content += df.to_csv(index=False) + "\n"
        wb = load_workbook(fp, data_only=True)
        if wb._images:
            for image in wb._images:
                img = image.ref
                if isinstance(img, bytes):
                    try:
                        pil_img = Image.open(io.BytesIO(img))
                        ocr_text = pytesseract.image_to_string(pil_img)
                        if ocr_text.strip():
                            content += ocr_text + "\n"
                    except:
                        pass
    except Exception as e:
        content += f"{fp}: {e}"
    return content.strip()

def extract_pptx_content(fp):
    content = ""
    try:
        prs = Presentation(fp)
        for slide in prs.slides:
            for shape in slide.shapes:
                if hasattr(shape, "text") and shape.text:
                    content += shape.text + "\n"
                if shape.shape_type == 13 and hasattr(shape, "image") and shape.image:
                    try:
                        img = Image.open(io.BytesIO(shape.image.blob))
                        ocr_text = pytesseract.image_to_string(img)
                        if ocr_text.strip():
                            content += ocr_text + "\n"
                    except:
                        pass
            for shape in slide.shapes:
                if shape.has_table:
                    table = shape.table
                    for row in table.rows:
                        cells = [cell.text for cell in row.cells]
                        content += "\t".join(cells) + "\n"
    except Exception as e:
        content += f"{fp}: {e}"
    return content.strip()

def extract_file_content(fp):
    ext = Path(fp).suffix.lower()
    if ext == ".pdf":
        return extract_pdf_content(fp)
    elif ext in [".doc", ".docx"]:
        return extract_docx_content(fp)
    elif ext in [".xlsx", ".xls"]:
        return extract_excel_content(fp)
    elif ext in [".ppt", ".pptx"]:
        return extract_pptx_content(fp)
    else:
        try:
            return Path(fp).read_text(encoding="utf-8").strip()
        except Exception as e:
            return f"{fp}: {e}"

async def fetch_response_stream_async(host, key, model, msgs, cfg, sid, stop_event):
    for t in [1, 2]:
        try:
            async with httpx.AsyncClient(timeout=t) as client:
                async with client.stream("POST", host, json={**{"model": model, "messages": msgs, "session_id": sid, "stream": True}, **cfg}, headers={"Authorization": f"Bearer {key}"}) as response:
                    async for line in response.aiter_lines():
                        if stop_event.is_set():
                            return
                        if not line:
                            continue
                        if line.startswith("data: "):
                            data = line[6:]
                            if data.strip() == RESPONSES["RESPONSE_10"]:
                                return
                            try:
                                j = json.loads(data)
                                if isinstance(j, dict) and j.get("choices"):
                                    ch = j["choices"][0]
                                    if ch.get("delta") and isinstance(ch["delta"].get("content"), str):
                                        yield ch["delta"]["content"]
                            except:
                                continue
        except:
            continue
        marked_item(key, LINUX_SERVER_PROVIDER_KEYS_MARKED, LINUX_SERVER_PROVIDER_KEYS_ATTEMPTS)
    return

async def chat_with_model_async(history, user_input, model_display, sess, custom_prompt):
    ensure_stop_event(sess)
    if not get_available_items(LINUX_SERVER_PROVIDER_KEYS, LINUX_SERVER_PROVIDER_KEYS_MARKED) or not get_available_items(LINUX_SERVER_HOSTS, LINUX_SERVER_HOSTS_ATTEMPTS):
        yield RESPONSES["RESPONSE_3"]
        return
    if not hasattr(sess, "session_id") or not sess.session_id:
        sess.session_id = str(uuid.uuid4())
        sess.stop_event = asyncio.Event()
    if not hasattr(sess, "active_candidate"):
        sess.active_candidate = None
    model_key = get_model_key(model_display)
    cfg = MODEL_CONFIG.get(model_key, DEFAULT_CONFIG)
    msgs = [{"role": "user", "content": u} for u, _ in history] + [{"role": "assistant", "content": a} for _, a in history if a]
    prompt = INTERNAL_TRAINING_DATA if model_key == DEFAULT_MODEL_KEY and INTERNAL_TRAINING_DATA else (custom_prompt or SYSTEM_PROMPT_MAPPING.get(model_key, SYSTEM_PROMPT_DEFAULT))
    msgs.insert(0, {"role": "system", "content": prompt})
    msgs.append({"role": "user", "content": user_input})
    if sess.active_candidate:
        async for chunk in fetch_response_stream_async(sess.active_candidate[0], sess.active_candidate[1], model_key, msgs, cfg, sess.session_id, sess.stop_event):
            if sess.stop_event.is_set():
                return
            yield chunk
        return
    keys = get_available_items(LINUX_SERVER_PROVIDER_KEYS, LINUX_SERVER_PROVIDER_KEYS_MARKED)
    hosts = get_available_items(LINUX_SERVER_HOSTS, LINUX_SERVER_HOSTS_ATTEMPTS)
    random.shuffle(keys)
    random.shuffle(hosts)
    for k in keys:
        for h in hosts:
            stream_gen = fetch_response_stream_async(h, k, model_key, msgs, cfg, sess.session_id, sess.stop_event)
            full_text = ""
            got_any = False
            async for chunk in stream_gen:
                if sess.stop_event.is_set():
                    return
                if not got_any:
                    got_any = True
                    sess.active_candidate = (h, k)
                full_text += chunk
                yield chunk
            if got_any and full_text:
                return
    yield RESPONSES["RESPONSE_2"]

async def respond_async(multi, history, model_display, sess, custom_prompt):
    ensure_stop_event(sess)
    sess.stop_event.clear()
    msg_input = {"text": multi.get("text", "").strip(), "files": multi.get("files", [])}
    if not msg_input["text"] and not msg_input["files"]:
        yield history, gr.update(value="", interactive=True, submit_btn=True, stop_btn=False), sess
        return
    inp = ""
    for f in msg_input["files"]:
        fp = f.get("data", f.get("name", "")) if isinstance(f, dict) else f
        inp += f"{Path(fp).name}\n\n{extract_file_content(fp)}\n\n"
    if msg_input["text"]:
        inp += msg_input["text"]
    history.append([inp, RESPONSES["RESPONSE_8"]])
    yield history, gr.update(interactive=False, submit_btn=False, stop_btn=True), sess
    queue = asyncio.Queue()
    async def background():
        full = ""
        async for chunk in chat_with_model_async(history, inp, model_display, sess, custom_prompt):
            full += chunk
            await queue.put(chunk)
        await queue.put(None)
        return full
    bg_task = asyncio.create_task(background())
    stop_task = asyncio.create_task(sess.stop_event.wait())
    first_meaningful_chunk_found = False
    try:
        while True:
            done, _ = await asyncio.wait({stop_task, asyncio.create_task(queue.get())}, return_when=asyncio.FIRST_COMPLETED)
            if stop_task in done:
                bg_task.cancel()
                history[-1][1] = RESPONSES["RESPONSE_1"]
                yield history, gr.update(value="", interactive=True, submit_btn=True, stop_btn=False), sess
                sess.stop_event.clear()
                return
            for d in done:
                chunk = d.result()
                if chunk is None:
                    raise StopAsyncIteration
                if not first_meaningful_chunk_found:
                    if chunk.strip():
                        history[-1][1] = chunk
                        first_meaningful_chunk_found = True
                else:
                    history[-1][1] += chunk
                yield history, gr.update(interactive=False, submit_btn=False, stop_btn=True), sess
    except StopAsyncIteration:
        pass
    finally:
        stop_task.cancel()
    full_response = await bg_task
    yield history, gr.update(value="", interactive=True, submit_btn=True, stop_btn=False), sess

def change_model(new):
    visible = new != MODEL_CHOICES[0]
    default = SYSTEM_PROMPT_MAPPING.get(get_model_key(new), SYSTEM_PROMPT_DEFAULT)
    return [], create_session(), new, default, gr.update(value=default, visible=visible)

def stop_response(history, sess):
    ensure_stop_event(sess)
    sess.stop_event.set()
    if history:
        history[-1][1] = RESPONSES["RESPONSE_1"]
    return history, gr.update(value="", interactive=True, submit_btn=True, stop_btn=False), sess

with gr.Blocks(fill_height=True, fill_width=True, title=AI_TYPES["AI_TYPE_4"], head=META_TAGS) as jarvis:
    user_history = gr.State([])
    user_session = gr.State(create_session())
    selected_model = gr.State(MODEL_CHOICES[0] if MODEL_CHOICES else "")
    custom_prompt_state = gr.State("")
    chatbot = gr.Chatbot(label=AI_TYPES["AI_TYPE_1"], show_copy_button=True, scale=1, elem_id=AI_TYPES["AI_TYPE_2"])
    msg = gr.MultimodalTextbox(show_label=False, placeholder=RESPONSES["RESPONSE_5"], interactive=True, file_count="single", file_types=ALLOWED_EXTENSIONS)
    with gr.Accordion(AI_TYPES["AI_TYPE_6"], open=False):
        model_dropdown = gr.Dropdown(show_label=False, choices=MODEL_CHOICES, value=MODEL_CHOICES[0])
        system_prompt = gr.Textbox(label=AI_TYPES["AI_TYPE_7"], lines=2, interactive=True, visible=False)
    model_dropdown.change(fn=change_model, inputs=[model_dropdown], outputs=[user_history, user_session, selected_model, custom_prompt_state, system_prompt])
    system_prompt.change(fn=lambda x: x, inputs=[system_prompt], outputs=[custom_prompt_state])
    msg.submit(fn=respond_async, inputs=[msg, user_history, selected_model, user_session, custom_prompt_state], outputs=[chatbot, msg, user_session], api_name=INTERNAL_AI_GET_SERVER)
    msg.stop(fn=stop_response, inputs=[user_history, user_session], outputs=[chatbot, msg, user_session])
jarvis.queue(default_concurrency_limit=3).launch(max_file_size="1mb")