import base64
import io
import json
import os
from typing import Dict, List, Tuple, Any, Optional

import requests
from PIL import Image
import gradio as gr
# =========================
# Config
# =========================
DEFAULT_API_URL = os.environ.get("API_URL")
LOGO_IMAGE_PATH = './assets/logo.jpg'
GOOGLE_FONTS_URL = "<link href='https://fonts.googleapis.com/css2?family=Noto+Sans+SC:wght@400;700&display=swap' rel='stylesheet'>"
LATEX_DELIMS = [
    {"left": "$$", "right": "$$", "display": True},
    {"left": "$",  "right": "$",  "display": False},
    {"left": "\\(", "right": "\\)", "display": False},
    {"left": "\\[", "right": "\\]", "display": True},
]

# =========================
# Base64 and Example Loading Logic (From New Script)
# =========================
def image_to_base64_data_url(filepath: str) -> str:
    """Reads a local image file and encodes it into a Base64 Data URL."""
    try:
        ext = os.path.splitext(filepath)[1].lower()
        mime_types = {'.jpg': 'image/jpeg', '.jpeg': 'image/jpeg', '.png': 'image/png', '.gif': 'image/gif'}
        mime_type = mime_types.get(ext, 'image/jpeg')
        with open(filepath, "rb") as image_file:
            encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
        return f"data:{mime_type};base64,{encoded_string}"
    except Exception as e:
        print(f"Error encoding image to Base64: {e}")
        return ""

def _get_examples_from_dir(dir_path: str) -> List[List[str]]:
    supported_exts = {".png", ".jpg", ".jpeg", ".bmp", ".webp"}
    examples = []
    if not os.path.exists(dir_path): return []
    for filename in sorted(os.listdir(dir_path)):
        if os.path.splitext(filename)[1].lower() in supported_exts:
            examples.append([os.path.join(dir_path, filename)])
    return examples

TARGETED_EXAMPLES_DIR = "examples/targeted"
COMPLEX_EXAMPLES_DIR = "examples/complex"
targeted_recognition_examples = _get_examples_from_dir(TARGETED_EXAMPLES_DIR)
complex_document_examples = _get_examples_from_dir(COMPLEX_EXAMPLES_DIR)

# =========================
# UI Helpers (From New Script)
# =========================
def render_uploaded_image_div(file_path: str) -> str:
    data_url = image_to_base64_data_url(file_path)
    return f"""
    <div class="uploaded-image">
        <img src="{data_url}" alt="Uploaded image" style="width:100%;height:100%;object-fit:contain;"/>
    </div>
    """

def update_preview_visibility(file_path: Optional[str]) -> Dict:
    if file_path:
        html_content = render_uploaded_image_div(file_path)
        return gr.update(value=html_content, visible=True)
    else:
        return gr.update(value="", visible=False)

def _on_gallery_select(example_paths: List[str], evt: gr.SelectData):
    try:
        idx = evt.index
        return example_paths[idx]
    except Exception:
        return None

# =========================
# API Call Logic (From New Script - More feature complete)
# =========================
def _file_to_b64_image_only(file_path: str) -> Tuple[str, int]:
    if not file_path: raise ValueError("Please upload an image first.")
    ext = os.path.splitext(file_path)[1].lower()
    if ext not in {".png", ".jpg", ".jpeg", ".bmp", ".webp"}: raise ValueError("Only image files are supported.")
    with open(file_path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8"), 1

def _call_api(api_url: str, file_path: str, use_layout_detection: bool, prompt_label: Optional[str], use_chart_recognition: bool = False) -> Dict[str, Any]:
    b64, file_type = _file_to_b64_image_only(file_path)
    payload = {"file": b64, "useLayoutDetection": bool(use_layout_detection), "fileType": file_type, "layoutMergeBboxesMode": "union"}

    if not use_layout_detection:
        if not prompt_label: raise ValueError("Please select a recognition type.")
        payload["promptLabel"] = prompt_label.strip().lower()

    # This parameter is from the new script's logic
    if use_layout_detection and use_chart_recognition:
        payload["use_chart_recognition"] = True

    try:
        resp = requests.post(api_url, json=payload, timeout=120)
        resp.raise_for_status()
        data = resp.json()
    except requests.exceptions.RequestException as e:
        raise gr.Error(f"API request failed: {e}")
    except json.JSONDecodeError:
        raise gr.Error(f"Invalid JSON response from server:\n{getattr(resp, 'text', '')}")
    if data.get("errorCode", -1) != 0:
        raise gr.Error(f"API returned an error: errorCode={data.get('errorCode')} errorMsg={data.get('errorMsg')}")
    return data

# =========================
# Core Logic for Handling Image URLs (From Old "Xinghe" Script)
# =========================
def url_to_base64_data_url(url: str) -> str:
    """Downloads an image from a URL and formats it as a Base64 Data URL for Markdown."""
    try:
        response = requests.get(url, timeout=30)
        response.raise_for_status()
        mime_type = response.headers.get('Content-Type', 'image/jpeg')
        if not mime_type.startswith('image/'):
             print(f"Warning: URL did not return an image content type. Got: {mime_type}")
             mime_type = 'image/jpeg'
        image_bytes = response.content
        encoded_string = base64.b64encode(image_bytes).decode('utf-8')
        return f"data:{mime_type};base64,{encoded_string}"
    except requests.exceptions.RequestException as e:
        print(f"Error fetching markdown image from URL {url}: {e}")
        return url # Fallback to original URL on error
    except Exception as e:
        print(f"An unexpected error occurred while processing markdown URL {url}: {e}")
        return url

def replace_image_urls_with_data_urls(md_text: str, md_images_map: Dict[str, str]) -> str:
    """Replaces image placeholder paths in Markdown with Base64 Data URLs fetched from external URLs."""
    if not md_images_map:
        return md_text
    for placeholder_path, image_url in md_images_map.items():
        print(f"Processing markdown image for '{placeholder_path}' from URL: {image_url}")
        data_url = url_to_base64_data_url(image_url)
        md_text = md_text.replace(f'src="{placeholder_path}"', f'src="{data_url}"') \
                         .replace(f']({placeholder_path})', f']({data_url})')
    return md_text

def url_to_pil_image(url: str) -> Optional[Image.Image]:
    """Downloads an image from a URL and returns it as a PIL Image object for the Gradio Image component."""
    if not url or not url.startswith(('http://', 'https://')):
        print(f"Warning: Invalid URL provided for visualization image: {url}")
        return None
    try:
        response = requests.get(url, timeout=30)
        response.raise_for_status()
        image_bytes = response.content
        pil_image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
        return pil_image
    except requests.exceptions.RequestException as e:
        print(f"Error fetching visualization image from URL {url}: {e}")
        return None
    except Exception as e:
        print(f"Error processing visualization image from URL {url}: {e}")
        return None

# =========================
# API Response Processing (From Old "Xinghe" Script - Handles URLs)
# =========================
def _process_api_response_page(result: Dict[str, Any]) -> Tuple[str, Optional[Image.Image], str]:
    """
    Processes the API response which contains URLs for images.
    1. Converts markdown image URLs to inline Base64 Data URLs.
    2. Downloads the visualization image URL into a PIL Image object.
    """
    layout_results = (result or {}).get("layoutParsingResults", [])
    if not layout_results:
        return "No content was recognized.", None, ""

    page0 = layout_results[0] or {}

    # Step 1: Process Markdown content using URL-to-Base64 logic
    md_data = page0.get("markdown") or {}
    md_text = md_data.get("text", "") or ""
    md_images_map = md_data.get("images", {}) # This map contains URLs: {"placeholder.jpg": "http://..."}
    if md_images_map:
        md_text = replace_image_urls_with_data_urls(md_text, md_images_map)

    # Step 2: Process Visualization images by downloading from URLs
    vis_images: List[Image.Image] = []
    out_imgs = page0.get("outputImages") or {} # This dict contains URLs: {"0": "http://...", "1": "http://..."}
    for _, img_url in sorted(out_imgs.items()):
        pil_image = url_to_pil_image(img_url)
        if pil_image:
            vis_images.append(pil_image)
        else:
            print(f"Warning: Failed to load visualization image from URL: {img_url}")

    # Logic to select the final visualization image
    output_image: Optional[Image.Image] = None
    if len(vis_images) >= 2:
        output_image = vis_images[1]
    elif vis_images:
        output_image = vis_images[0]

    return md_text or "(Empty result)", output_image, md_text

# =========================
# Handlers (From New Script - More feature complete)
# =========================
def handle_complex_doc(file_path: str, use_chart_recognition: bool) -> Tuple[str, Optional[Image.Image], str]:
    if not file_path: raise gr.Error("Please upload an image first.")
    data = _call_api(DEFAULT_API_URL, file_path, use_layout_detection=True, prompt_label=None, use_chart_recognition=use_chart_recognition)
    result = data.get("result", {})
    return _process_api_response_page(result)

def handle_targeted_recognition(file_path: str, prompt_choice: str) -> Tuple[str, str]:
    if not file_path: raise gr.Error("Please upload an image first.")
    mapping = {"Text Recognition": "ocr", "Formula Recognition": "formula", "Table Recognition": "table", "Chart Recognition": "chart"}
    label = mapping.get(prompt_choice, "ocr")
    data = _call_api(DEFAULT_API_URL, file_path, use_layout_detection=False, prompt_label=label)
    result = data.get("result", {})
    md_preview, _, md_raw = _process_api_response_page(result)
    return md_preview, md_raw

# =========================
# CSS & UI (From New Script)
# =========================
custom_css = '''
body, button, input, textarea, select, p, label { font-family: "Microsoft YaHei","微软雅黑","Microsoft YaHei UI", "Noto Sans SC","PingFang SC",sans-serif !important; }
.app-header { text-align: center; max-width: 900px; margin: 0 auto 4px !important; padding: 0 !important; }
.gradio-container { padding-top: 2px !important; padding-bottom: 2px !important; }
.gradio-container .tabs { margin-top: 0px !important; }
.gradio-container .tabitem { padding-top: 4px !important; }
.prompt-grid { gap: 8px !important; margin-top: 4px !important; }
.prompt-grid button { height: 40px !important; min-height: 0 !important; padding: 0 12px !important; border-radius: 8px !important; font-weight: 600 !important; font-size: 13px !important; letter-spacing: .2px; }
.quick-links { text-align: center; padding: 8px 0; border: 1px solid #e5e7eb; border-radius: 8px; margin: 8px auto !important; max-width: 900px; }
.quick-links a { margin: 0 15px; font-size: 14px; font-weight: 600; text-decoration: none; color: #3b82f6; }
.quick-links a:hover { text-decoration: underline; }
#image_preview_vl, #image_preview_doc { height: 60vh !important; overflow: auto; }
#vis_image_doc { height: 42vh !important; }
#image_preview_vl .uploaded-image, #image_preview_doc .uploaded-image { height: 100%; }
#image_preview_vl img, #image_preview_doc img, #vis_image_doc img { width: 100% !important; height: 100% !important; object-fit: contain !important; }
#md_preview_vl, #md_preview_doc { max-height: 60vh; overflow: auto; scrollbar-gutter: stable both-edges; }
#md_preview_doc .prose img,
#md_preview_vl .prose img {
  display: block !important;
  margin-left: auto !important;
  margin-right: auto !important; /* 块级元素用 margin auto 居中 */
  height: auto; /* 可选：保持比例 */
  max-width: 100%; /* 可选：避免溢出 */
}
#md_preview_vl .prose, #md_preview_doc .prose { line-height: 1.7 !important; font-family: 'Microsoft YaHei','Noto Sans SC','PingFang SC',sans-serif !important; }
'''

with gr.Blocks(head=GOOGLE_FONTS_URL, css=custom_css, theme=gr.themes.Soft()) as demo:
    logo_data_url = image_to_base64_data_url(LOGO_IMAGE_PATH) if os.path.exists(LOGO_IMAGE_PATH) else ""
    gr.HTML(f"""
        <div class="app-header">
            <img src="{logo_data_url}" alt="App Logo" style="max-height:10%; width: auto; margin: 10px auto; display: block;">
        </div>
    """)
    gr.HTML("""
        <div class="quick-links">
            <a href="https://github.com/PaddlePaddle/PaddleOCR" target="_blank">GitHub</a> |
            <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/main/ppstructure/docs/vls.md" target="_blank">Technical Report</a> |
            <a href="https://xinghe.baidu.com/" target="_blank">Model</a>
        </div>
    """)

    with gr.Tabs():
        with gr.Tab("Document Parsing"):
            with gr.Row():
                with gr.Column(scale=5):
                    file_doc = gr.File(label="Upload Image", file_count="single", type="filepath", file_types=["image"])
                    preview_doc_html = gr.HTML(value="", elem_id="image_preview_doc", visible=False)

                    gr.Markdown("_( Use this mode for recognizing full-page documents with structured layouts, such as reports, papers, or magazines.)_")
                    gr.Markdown("💡 *To recognize a single, pre-cropped element (e.g., a table or formula), switch to the 'Content Recognition' tab for better results.*")

                    with gr.Row(variant="panel"):
                        chart_parsing_switch = gr.Checkbox(label="Enable chart parsing", value=False, scale=1)
                        btn_parse = gr.Button("Parse Document", variant="primary", scale=2)

                    if complex_document_examples:
                        complex_paths = [e[0] for e in complex_document_examples]
                        complex_state = gr.State(complex_paths)
                        gr.Markdown("**Document Examples (Click an image to load)**")
                        gallery_complex = gr.Gallery(value=complex_paths, columns=4, height=400, preview=False, label=None, allow_preview=False)
                        gallery_complex.select(fn=_on_gallery_select, inputs=[complex_state], outputs=[file_doc])

                with gr.Column(scale=7):
                    with gr.Tabs():
                        with gr.Tab("Markdown Preview"):
                            md_preview_doc = gr.Markdown("Please upload an image and click 'Parse Document'.", latex_delimiters=LATEX_DELIMS, elem_id="md_preview_doc")
                        with gr.Tab("Visualization"):
                            vis_image_doc = gr.Image(label="Detection Visualization", interactive=False, elem_id="vis_image_doc")
                        with gr.Tab("Markdown Source"):
                            md_raw_doc = gr.Code(label="Markdown Source Code", language="markdown")

            file_doc.change(fn=update_preview_visibility, inputs=[file_doc], outputs=[preview_doc_html])
            btn_parse.click(fn=handle_complex_doc, inputs=[file_doc, chart_parsing_switch], outputs=[md_preview_doc, vis_image_doc, md_raw_doc])

        with gr.Tab("Content Recognition"):
            with gr.Row():
                with gr.Column(scale=5):
                    file_vl = gr.File(label="Upload Image", file_count="single", type="filepath", file_types=["image"])
                    preview_vl_html = gr.HTML(value="", elem_id="image_preview_vl", visible=False)

                    gr.Markdown("_(Best for images with a **simple, single-column layout** (e.g., pure text), or for a **pre-cropped single element** like a table, formula, or chart.)_")
                    gr.Markdown("Choose a recognition type:")
                    with gr.Row(elem_classes=["prompt-grid"]):
                        btn_ocr = gr.Button("Text Recognition", variant="secondary")
                        btn_formula = gr.Button("Formula Recognition", "secondary")
                    with gr.Row(elem_classes=["prompt-grid"]):
                        btn_table = gr.Button("Table Recognition", variant="secondary")
                        btn_chart = gr.Button("Chart Recognition", variant="secondary")

                    if targeted_recognition_examples:
                        targeted_paths = [e[0] for e in targeted_recognition_examples]
                        targeted_state = gr.State(targeted_paths)
                        gr.Markdown("**Content Recognition Examples (Click an image to load)**")
                        gallery_targeted = gr.Gallery(value=targeted_paths, columns=4, height=400, preview=False, label=None, allow_preview=False)
                        gallery_targeted.select(fn=_on_gallery_select, inputs=[targeted_state], outputs=[file_vl])

                with gr.Column(scale=7):
                    with gr.Tabs():
                        with gr.Tab("Recognition Result"):
                            md_preview_vl = gr.Markdown("Please upload an image and click a recognition type.", latex_delimiters=LATEX_DELIMS, elem_id="md_preview_vl")
                        with gr.Tab("Raw Output"):
                            md_raw_vl = gr.Code(label="Raw Output", language="markdown")

            file_vl.change(fn=update_preview_visibility, inputs=[file_vl], outputs=[preview_vl_html])
            btn_ocr.click(fn=handle_targeted_recognition, inputs=[file_vl, gr.State("Text Recognition")], outputs=[md_preview_vl, md_raw_vl])
            btn_formula.click(fn=handle_targeted_recognition, inputs=[file_vl, gr.State("Formula Recognition")], outputs=[md_preview_vl, md_raw_vl])
            btn_table.click(fn=handle_targeted_recognition, inputs=[file_vl, gr.State("Table Recognition")], outputs=[md_preview_vl, md_raw_vl])
            btn_chart.click(fn=handle_targeted_recognition, inputs=[file_vl, gr.State("Chart Recognition")], outputs=[md_preview_vl, md_raw_vl])

if __name__ == "__main__":
    demo.queue()
    demo.launch(server_name="0.0.0.0", server_port=8812, share=False)