import base64
import concurrent.futures
import io
import json
import os
from pathlib import Path
from typing import Any

import numpy as np
import requests
from PIL import Image, ImageFilter
from PyPDF2 import PdfReader


class OCRProcessor:
    def __init__(
        self,
        model_name: str = "llama3.2-vision:11b",
        base_url: str = "http://localhost:11434/api/generate",
        max_workers: int = 1,
        provider: str = "ollama",
        openai_api_key: str | None = None,
        openai_base_url: str = "https://api.openai.com/v1/chat/completions",
    ):
        self.model_name = model_name
        self.base_url = base_url
        self.max_workers = max_workers
        self.provider = provider.lower()
        self.openai_api_key = openai_api_key or os.getenv("OPENAI_API_KEY")
        self.openai_base_url = openai_base_url

    def _encode_image(self, image_path: str) -> str:
        """Convert image to base64 string"""
        with open(image_path, "rb") as image_file:
            return base64.b64encode(image_file.read()).decode("utf-8")

    def _pdf_to_images(self, pdf_path: str) -> list[str]:
        """
        Convert each page of a PDF to an image without PyMuPDF.
        Strategy: extract largest embedded image per page via PyPDF2.
        Saves each selected image as a temporary PNG and returns paths.

        Note: Text-only pages with no embedded images will be skipped here.
        Use _pdf_extract_text as a fallback for such pages.
        """
        image_paths: list[str] = []
        try:
            reader = PdfReader(pdf_path)
            for page_index, page in enumerate(reader.pages):
                try:
                    resources = page.get("/Resources")
                    if resources is None:
                        continue
                    xobject = resources.get("/XObject")
                    if xobject is None:
                        continue
                    xobject = xobject.get_object()
                    largest = None
                    largest_area = -1
                    for _, obj_ref in xobject.items():
                        try:
                            obj = obj_ref.get_object()
                            if obj.get("/Subtype") != "/Image":
                                continue
                            width = int(obj.get("/Width", 0))
                            height = int(obj.get("/Height", 0))
                            area = width * height
                            if area > largest_area:
                                largest = obj
                                largest_area = area
                        except Exception:
                            continue

                    if largest is None:
                        continue

                    data = largest.get_data()
                    filt = largest.get("/Filter")
                    out_path = f"{pdf_path}_page{page_index}.png"
                    # If JPEG/JPX, write bytes directly; else convert via PIL
                    if filt in ("/DCTDecode",):
                        # JPEG
                        out_path = f"{pdf_path}_page{page_index}.jpg"
                        with open(out_path, "wb") as f:
                            f.write(data)
                    elif filt in ("/JPXDecode",):
                        out_path = f"{pdf_path}_page{page_index}.jp2"
                        with open(out_path, "wb") as f:
                            f.write(data)
                    else:
                        mode = "RGB"
                        colorspace = largest.get("/ColorSpace")
                        if colorspace in ("/DeviceGray",):
                            mode = "L"
                        width = int(largest.get("/Width", 0))
                        height = int(largest.get("/Height", 0))
                        try:
                            img = Image.frombytes(mode, (width, height), data)
                        except Exception:
                            # Best-effort decode via Pillow
                            img = Image.open(io.BytesIO(data))
                        img.save(out_path, format="PNG")

                    image_paths.append(out_path)
                except Exception:
                    # Continue gracefully for problematic pages/objects
                    continue
            return image_paths
        except Exception as e:
            raise ValueError(f"Could not extract images from PDF: {e}")

    def _pdf_extract_text(self, pdf_path: str) -> list[str]:
        """Extract text per page using pdfplumber if available, else PyPDF2."""
        texts: list[str] = []
        try:
            try:
                import pdfplumber

                with pdfplumber.open(pdf_path) as pdf:
                    for page in pdf.pages:
                        texts.append(page.extract_text() or "")
                return texts
            except Exception:
                # Fallback to PyPDF2
                reader = PdfReader(pdf_path)
                for page in reader.pages:  # type: ignore
                    texts.append(page.extract_text() or "")
                return texts
        except Exception as e:
            raise ValueError(f"Could not extract text from PDF: {e}")

    def _call_ollama_vision(self, prompt: str, image_base64: str) -> str:
        payload = {
            "model": self.model_name,
            "prompt": prompt,
            "stream": False,
            "images": [image_base64],
        }
        response = requests.post(self.base_url, json=payload)
        response.raise_for_status()
        return response.json().get("response", "")  # type: ignore

    def _call_openai_vision(self, prompt: str, image_base64: str) -> str:
        if not self.openai_api_key:
            raise ValueError("OPENAI_API_KEY not set")
        # Compose chat.completions payload for GPT-4o/mini vision
        payload = {
            "model": self.model_name or "gpt-4o-mini",
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": prompt},
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:image/jpeg;base64,{image_base64}",
                            },
                        },
                    ],
                }
            ],
            "temperature": 0,
        }
        headers = {
            "Authorization": f"Bearer {self.openai_api_key}",
            "Content-Type": "application/json",
        }
        response = requests.post(self.openai_base_url, headers=headers, json=payload)
        response.raise_for_status()
        data = response.json()
        try:
            return data["choices"][0]["message"]["content"]  # type: ignore
        except Exception:
            return json.dumps(data)

    def _preprocess_image(self, image_path: str, language: str = "en") -> str:
        """
        Preprocess image before OCR using Pillow + NumPy:
        - Convert to grayscale
        - Histogram equalization (contrast)
        - Median denoise
        - Otsu threshold and invert
        """
        try:
            with Image.open(image_path) as img:
                if img.mode in ("RGBA", "LA"):
                    img = img.convert("RGB")
                gray = img.convert("L")

                # Histogram equalization via cumulative distribution
                arr = np.asarray(gray)
                hist, _ = np.histogram(arr.flatten(), 256, [0, 256])  # type: ignore
                cdf = hist.cumsum()
                cdf_masked = np.ma.masked_equal(cdf, 0)  # type: ignore
                cdf_min = cdf_masked.min() if cdf_masked.size else 0
                cdf_max = cdf_masked.max() if cdf_masked.size else 0
                if cdf_max == cdf_min:
                    eq = arr
                else:
                    cdf_scaled = (cdf_masked - cdf_min) * 255 / (cdf_max - cdf_min)
                    lut = np.ma.filled(cdf_scaled, 0).astype("uint8")
                    eq = lut[arr]

                eq_img = Image.fromarray(eq, mode="L")
                # Median filter (3x3) to reduce noise
                eq_img = eq_img.filter(ImageFilter.MedianFilter(size=3))
                arr_eq = np.asarray(eq_img)

                # Otsu threshold
                hist2, _ = np.histogram(arr_eq, 256, [0, 256])  # type: ignore
                total = arr_eq.size
                sum_total = (np.arange(256) * hist2).sum()
                sum_b = 0.0
                w_b = 0.0
                max_var = 0.0
                thr = 0
                for t in range(256):
                    w_b += hist2[t]
                    if w_b == 0:
                        continue
                    w_f = total - w_b
                    if w_f == 0:
                        break
                    sum_b += t * hist2[t]
                    m_b = sum_b / w_b
                    m_f = (sum_total - sum_b) / w_f
                    var_between = w_b * w_f * (m_b - m_f) ** 2
                    if var_between > max_var:
                        max_var = var_between
                        thr = t

                binary = (arr_eq > thr).astype(np.uint8) * 255
                # Invert: black text on white background
                binary = 255 - binary

                out_img = Image.fromarray(binary, mode="L")
                preprocessed_path = f"{image_path}_preprocessed.jpg"
                out_img.save(preprocessed_path, format="JPEG", quality=95)
                return preprocessed_path
        except Exception as e:
            raise ValueError(f"Failed to preprocess image {image_path}: {e}")

    def process_image(
        self,
        image_path: str,
        format_type: str = "markdown",
        preprocess: bool = True,
        custom_prompt: str | None = None,
        language: str = "en",
    ) -> str:
        """
        Process an image (or PDF) and extract text in the specified format

        Args:
            image_path: Path to the image file or PDF file
            format_type: One of ["markdown", "text", "json", "structured", "key_value","custom"]
            preprocess: Whether to apply image preprocessing
            custom_prompt: If provided, this prompt overrides the default based on format_type
            language: Language code to apply language specific OCR preprocessing
        """
        try:
            # If the input is a PDF, process all pages
            if image_path.lower().endswith(".pdf"):
                image_pages = self._pdf_to_images(image_path)
                responses: list[str] = []
                if image_pages:
                    for idx, page_file in enumerate(image_pages):
                        # Process each page with preprocessing if enabled
                        if preprocess:
                            preprocessed_path = self._preprocess_image(
                                page_file, language
                            )
                        else:
                            preprocessed_path = page_file

                        image_base64 = self._encode_image(preprocessed_path)

                        if custom_prompt and custom_prompt.strip():
                            prompt = custom_prompt
                        else:
                            prompts = {
                                "markdown": f"""Extract all text content from this image in {language} **exactly as it appears**, without modification, summarization, or omission.
                                Format the output in markdown:
                                - Use headers (#, ##, ###) **only if they appear in the image**
                                - Preserve original lists (-, *, numbered lists) as they are
                                - Maintain all text formatting (bold, italics, underlines) exactly as seen
                                - **Do not add, interpret, or restructure any content**
                            """,
                                "text": f"""Extract all visible text from this image in {language} **without any changes**.
                                - **Do not summarize, paraphrase, or infer missing text.**
                                - Retain all spacing, punctuation, and formatting exactly as in the image.
                                - If text is unclear or partially visible, extract as much as possible without guessing.
                                - **Include all text, even if it seems irrelevant or repeated.**
                                """,
                                "json": f"""Extract all text from this image in {language} and format it as JSON, **strictly preserving** the structure.
                                - **Do not summarize, add, or modify any text.**
                                - Maintain hierarchical sections and subsections as they appear.
                                - Use keys that reflect the document's actual structure (e.g., "title", "body", "footer").
                                - Include all text, even if fragmented, blurry, or unclear.
                                """,
                                "structured": f"""Extract all text from this image in {language}, **ensuring complete structural accuracy**:
                                - Identify and format tables **without altering content**.
                                - Preserve list structures (bulleted, numbered) **exactly as shown**.
                                - Maintain all section headings, indents, and alignments.
                                - **Do not add, infer, or restructure the content in any way.**
                                """,
                                "key_value": f"""Extract all key-value pairs from this image in {language} **exactly as they appear**:
                                - Identify and extract labels and their corresponding values without modification.
                                - Maintain the exact wording, punctuation, and order.
                                - Format each pair as 'key: value' **only if clearly structured that way in the image**.
                                - **Do not infer missing values or add any extra text.**
                                """,
                                "table": f"""Extract all tabular data from this image in {language} **exactly as it appears**, without modification, summarization, or omission.
                                - **Preserve the table structure** (rows, columns, headers) as closely as possible.
                                - **Do not add missing values or infer content**—if a cell is empty, leave it empty.
                                - Maintain all numerical, textual, and special character formatting.
                                - If the table contains merged cells, indicate them clearly without altering their meaning.
                                - Output the table in a structured format such as Markdown, CSV, or JSON, based on the intended use.
                                """,
                            }
                            prompt = prompts.get(format_type, prompts["text"])

                        # Route to chosen provider
                        if self.provider == "openai":
                            res = self._call_openai_vision(prompt, image_base64)
                        else:
                            res = self._call_ollama_vision(prompt, image_base64)

                        responses.append(f"Page {idx + 1}:\n{res}")

                        # Clean up temporary files
                        if preprocess and preprocessed_path.endswith(
                            "_preprocessed.jpg"
                        ):
                            try:
                                os.remove(preprocessed_path)
                            except OSError:
                                pass
                        if page_file.endswith((".png", ".jpg", ".jp2")):
                            try:
                                os.remove(page_file)
                            except OSError:
                                pass

                    final_result = "\n".join(responses)
                    if format_type == "json":
                        try:
                            json_data = json.loads(final_result)
                            return json.dumps(json_data, indent=2)
                        except json.JSONDecodeError:
                            return final_result
                    return final_result
                else:
                    # Fallback: no images found; extract raw text per page
                    text_pages = self._pdf_extract_text(image_path)
                    combined = []
                    for i, t in enumerate(text_pages):
                        combined.append(f"Page {i + 1}:\n{t}")
                    return "\n".join(combined)

            # Process non-PDF images as before.
            if preprocess:
                image_path = self._preprocess_image(image_path, language)

            image_base64 = self._encode_image(image_path)

            # Clean up temporary files
            if image_path.endswith(("_preprocessed.jpg", "_temp.jpg")):
                os.remove(image_path)

            if custom_prompt and custom_prompt.strip():
                prompt = custom_prompt
                print("Using custom prompt:", prompt)
            else:
                prompts = {
                    "markdown": f"""Extract all text content from this image in {language} **exactly as it appears**, without modification, summarization, or omission.
                                Format the output in markdown:
                                - Use headers (#, ##, ###) **only if they appear in the image**
                                - Preserve original lists (-, *, numbered lists) as they are
                                - Maintain all text formatting (bold, italics, underlines) exactly as seen
                                - **Do not add, interpret, or restructure any content**
                            """,
                    "text": f"""Extract all visible text from this image in {language} **without any changes**.
                                - **Do not summarize, paraphrase, or infer missing text.**
                                - Retain all spacing, punctuation, and formatting exactly as in the image.
                                - If text is unclear or partially visible, extract as much as possible without guessing.
                                - **Include all text, even if it seems irrelevant or repeated.**
                                """,
                    "json": f"""Extract all text from this image in {language} and format it as JSON, **strictly preserving** the structure.
                                - **Do not summarize, add, or modify any text.**
                                - Maintain hierarchical sections and subsections as they appear.
                                - Use keys that reflect the document's actual structure (e.g., "title", "body", "footer").
                                - Include all text, even if fragmented, blurry, or unclear.
                                """,
                    "structured": f"""Extract all text from this image in {language}, **ensuring complete structural accuracy**:
                                - Identify and format tables **without altering content**.
                                - Preserve list structures (bulleted, numbered) **exactly as shown**.
                                - Maintain all section headings, indents, and alignments.
                                - **Do not add, infer, or restructure the content in any way.**
                                """,
                    "key_value": f"""Extract all key-value pairs from this image in {language} **exactly as they appear**:
                                - Identify and extract labels and their corresponding values without modification.
                                - Maintain the exact wording, punctuation, and order.
                                - Format each pair as 'key: value' **only if clearly structured that way in the image**.
                                - **Do not infer missing values or add any extra text.**
                                """,
                    "table": f"""Extract all tabular data from this image in {language} **exactly as it appears**, without modification, summarization, or omission.
                                - **Preserve the table structure** (rows, columns, headers) as closely as possible.
                                - **Do not add missing values or infer content**—if a cell is empty, leave it empty.
                                - Maintain all numerical, textual, and special character formatting.
                                - If the table contains merged cells, indicate them clearly without altering their meaning.
                                - Output the table in a structured format such as Markdown, CSV, or JSON, based on the intended use.
                                """,
                }
                prompt = prompts.get(format_type, prompts["text"])
                print("Using default prompt:", prompt)  # Debug print

            # Call chosen provider with single image
            if self.provider == "openai":
                result = self._call_openai_vision(prompt, image_base64)
            else:
                result = self._call_ollama_vision(prompt, image_base64)

            if format_type == "json":
                try:
                    json_data = json.loads(result)
                    return json.dumps(json_data, indent=2)
                except json.JSONDecodeError:
                    return str(result)

            return str(result)
        except Exception as e:
            return f"Error processing image: {str(e)}"

    def process_batch(
        self,
        input_path: str | list[str],
        format_type: str = "markdown",
        recursive: bool = False,
        preprocess: bool = True,
        custom_prompt: str | None = None,
        language: str = "en",
    ) -> dict[str, Any]:
        """
        Process multiple images in batch

        Args:
            input_path: Path to directory or list of image paths
            format_type: Output format type
            recursive: Whether to search directories recursively
            preprocess: Whether to apply image preprocessing
            custom_prompt: If provided, this prompt overrides the default for each image
            language: Language code to apply language specific OCR preprocessing

        Returns:
            Dictionary with results and statistics
        """
        # Collect all image paths
        image_paths: list[str | Path] = []
        if isinstance(input_path, str):
            base_path = Path(input_path)
            if base_path.is_dir():
                pattern = "**/*" if recursive else "*"
                for ext in [".png", ".jpg", ".jpeg", ".pdf", ".tiff"]:
                    image_paths.extend(base_path.glob(f"{pattern}{ext}"))
            else:
                image_paths = [base_path]
        else:
            image_paths = [Path(p) for p in input_path]

        results = {}
        errors = {}

        # Process images in parallel
        with concurrent.futures.ThreadPoolExecutor(
            max_workers=self.max_workers
        ) as executor:
            future_to_path = {
                executor.submit(
                    self.process_image,
                    str(path),
                    format_type,
                    preprocess,
                    custom_prompt,
                    language,
                ): path
                for path in image_paths
            }

            for future in concurrent.futures.as_completed(future_to_path):
                path = future_to_path[future]
                try:
                    results[str(path)] = future.result()
                except Exception as e:
                    errors[str(path)] = str(e)
                    # pbar.update(1)

        return {
            "results": results,
            "errors": errors,
            "statistics": {
                "total": len(image_paths),
                "successful": len(results),
                "failed": len(errors),
            },
        }