Update app.py

app.py

@@ -1,128 +1,342 @@
-*** Begin Patch
-*** Update File: app.py
-@@
--        if 'pdf' in request.files and request.files['pdf'].filename:
--            pdf = request.files['pdf']
--            pdf_path = os.path.join(temp_dir, pdf.filename)
--            pdf.save(pdf_path)
--            print(f"📄 PDF saved to {pdf_path}")
--
--            try:
--                print("🚀 Sending PDF to /handle_complex_doc...")
--                result = ocr_client.predict(
--                    file_path=handle_file(pdf_path),
--                    use_chart_recognition=False,
--                    api_name="/handle_complex_doc"
--                )
--                print("✅ OCR completed for PDF")
--                print(f"OCR raw result: {result}")
--
--                if isinstance(result, (list, tuple)) and len(result) >= 1:
--                    extracted_text = str(result[0])
--                elif isinstance(result, str):
--                    extracted_text = result
--                else:
--                    extracted_text = f"Unexpected result format: {type(result)}"
--
--                return jsonify({"extracted_text": extracted_text.strip()})
--            except Exception as e:
--                return jsonify({"error": f"Error processing PDF: {e}"}), 500
-+        if 'pdf' in request.files and request.files['pdf'].filename:
-+            pdf = request.files['pdf']
-+            pdf_path = os.path.join(temp_dir, pdf.filename)
-+            pdf.save(pdf_path)
-+            print(f"📄 PDF saved to {pdf_path}")
-+
-+            # Try local text extraction via PyMuPDF first
-+            try:
-+                doc = fitz.open(pdf_path)
-+                collected_text = []
-+                for page_index, page in enumerate(doc):
-+                    text = page.get_text("text") or ""
-+                    collected_text.append(f"--- Page {page_index + 1} ---\n{text.strip()}\n")
-+                local_text = "\n".join(collected_text).strip()
-+                if local_text:
-+                    print("✅ Extracted text locally via PyMuPDF")
-+                    return jsonify({"extracted_text": local_text})
-+            except Exception as e:
-+                print(f"⚠️ Local PyMuPDF extraction failed: {e}")
-+
-+            # Fallback to remote endpoint for complex layout parsing
-+            try:
-+                print("🚀 Sending PDF to /handle_complex_doc...")
-+                result = ocr_client.predict(
-+                    file_path=handle_file(pdf_path),
-+                    use_chart_recognition=False,
-+                    api_name="/handle_complex_doc"
-+                )
-+                print("✅ OCR completed for PDF")
-+                print(f"OCR raw result: {result}")
-+
-+                if isinstance(result, (list, tuple)) and len(result) >= 1:
-+                    extracted_text = str(result[0])
-+                elif isinstance(result, str):
-+                    extracted_text = result
-+                else:
-+                    extracted_text = f"Unexpected result format: {type(result)}"
-+
-+                return jsonify({"extracted_text": extracted_text.strip()})
-+            except Exception as e:
-+                return jsonify({"error": f"Error processing PDF: {e}"}), 500
-*** End Patched_text = f"Unexpected result format: {type(result)}"
-
-                return jsonify({"extracted_text": extracted_text.strip()})
-            except Exception as e:
-                return jsonify({"error": f"Error processing image: {e}"}), 500
-
-        # Handle PDF uploads
-        if 'pdf' in request.files and request.files['pdf'].filename:
-            pdf = request.files['pdf']
-            pdf_path = os.path.join(temp_dir, pdf.filename)
-            pdf.save(pdf_path)
-            print(f"📄 PDF saved to {pdf_path}")
-
-            # Try local text extraction via PyMuPDF first
-            try:
-                doc = fitz.open(pdf_path)
-                collected_text = []
-                for page_index, page in enumerate(doc):
-                    text = page.get_text("text") or ""
-                    collected_text.append(f"--- Page {page_index + 1} ---\n{text.strip()}\n")
-                local_text = "\n".join(collected_text).strip()
-                if local_text:
-                    print("✅ Extracted text locally via PyMuPDF")
-                    return jsonify({"extracted_text": local_text})
-            except Exception as e:
-                print(f"⚠️ Local PyMuPDF extraction failed: {e}")
-
-            # Fallback to remote endpoint for complex layout parsing
-            try:
-                print("🚀 Sending PDF to /handle_complex_doc...")
-                result = ocr_client.predict(
-                    file_path=handle_file(pdf_path),
-                    use_chart_recognition=False,
-                    api_name="/handle_complex_doc"
-                )
-                print("✅ OCR completed for PDF")
-                print(f"OCR raw result: {result}")
-
-                if isinstance(result, (list, tuple)) and len(result) >= 1:
-                    extracted_text = str(result[0])
-                elif isinstance(result, str):
-                    extracted_text = result
-                else:
-                    extracted_text = f"Unexpected result format: {type(result)}"
-
-                return jsonify({"extracted_text": extracted_text.strip()})
-            except Exception as e:
-                return jsonify({"error": f"Error processing PDF: {e}"}), 500
-
-        return jsonify({"error": "No file uploaded. Please upload an image or a PDF."}), 400
+import base64
+import io
+import json
+import os
+from typing import Dict, List, Tuple, Any, Optional
+import time
+import requests
+from PIL import Image
+import fitz  # PyMuPDF
+import gradio as gr
+# =========================
+# Config
+# =========================
+DEFAULT_API_URL = os.environ.get("API_URL")
+TOKEN = os.environ.get("TOKEN")
+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},
+]
+AUTH_HEADER = {"Authorization": f"bearer {TOKEN}"}
+JSON_HEADERS = {**AUTH_HEADER, "Content-Type": "application/json"}
+# =========================
+# Base64 and Example Loading Logic
+# =========================
+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
+# =========================
+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:
+        ext = os.path.splitext(file_path)[1].lower()
+        if ext == ".pdf":
+            # Hide image preview for PDFs
+            return gr.update(value="", visible=False)
+        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
+# =========================
+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()
+    if use_layout_detection and use_chart_recognition:
+        payload["useChartRecognition"] = True
+
+    try:
+        print(f"Sending API request to {api_url}...")
+        start_time = time.time()
+        resp = requests.post(api_url, json=payload, headers=JSON_HEADERS, timeout=600)
+        end_time = time.time()
+        duration = end_time - start_time
+        print(f"Received API response in {duration:.2f} seconds.")
+        
+        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("API returned an error:")
+    return data
+
+
+# =========================
+# API Response Processing
+# =========================
+
+# 【改动点】: 这个函数现在不再需要，因为我们不再将URL下载为PIL Image对象。
+# 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:
+#         start_time = time.time()
+#         response = requests.get(url, timeout=600)
+#         end_time = time.time()
+#         print(f"Fetched visualization image from {url} in {end_time - start_time:.2f} seconds.")
+#
+#         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
+
+def _process_api_response_page(result: Dict[str, Any]) -> Tuple[str, str, str]:
+    """
+    Processes the API response.
+    1. Replaces markdown image placeholders with their direct URLs.
+    2. Constructs an HTML <img> tag string for the visualization image URL.
+    """
+    layout_results = (result or {}).get("layoutParsingResults", [])
+    if not layout_results:
+        return "No content was recognized.", "<p>No visualization available.</p>", ""
 
+    page0 = layout_results[0] or {}
+
+    # Step 1: Process Markdown content (unchanged from previous optimization)
+    md_data = page0.get("markdown") or {}
+    md_text = md_data.get("text", "") or ""
+    md_images_map = md_data.get("images", {})
+    if md_images_map:
+        for placeholder_path, image_url in md_images_map.items():
+            md_text = md_text.replace(f'src="{placeholder_path}"', f'src="{image_url}"') \
+                             .replace(f']({placeholder_path})', f']({image_url})')
+
+    # 【核心改动点】 Step 2: Process Visualization images by creating an HTML string
+    output_html = "<p style='text-align:center; color:#888;'>No visualization image available.</p>"
+    out_imgs = page0.get("outputImages") or {}
+    
+    # Get all image URLs and sort them
+    sorted_urls = [img_url for _, img_url in sorted(out_imgs.items()) if img_url]
+
+    # Logic to select the final visualization image URL
+    output_image_url: Optional[str] = None
+    if len(sorted_urls) >= 2:
+        output_image_url = sorted_urls[1]
+    elif sorted_urls:
+        output_image_url = sorted_urls[0]
+
+    # If a URL was found, create the <img> tag
+    if output_image_url:
+        print(f"Found visualization image URL: {output_image_url}")
+        # The CSS will style this `img` tag because of the `#vis_image_doc img` selector
+        output_html = f'<img src="{output_image_url}" alt="Detection Visualization">'
+    else:
+        print("Warning: No visualization image URL found in the API response.")
+
+    return md_text or "(Empty result)", output_html, md_text
+
+# =========================
+# Handlers
+# =========================
+def _extract_pdf_text_with_pymupdf(pdf_path: str) -> Tuple[str, str, str]:
+    """Extract plain text from a PDF using PyMuPDF and return as markdown and raw."""
+    try:
+        doc = fitz.open(pdf_path)
+        parts = []
+        for i, page in enumerate(doc):
+            text = page.get_text("text") or ""
+            parts.append(f"### Page {i + 1}\n\n{text.strip()}\n")
+        md_text = "\n".join(parts).strip() or "No content was recognized."
+        vis_html = "<p>No visualization available for PDF text extraction.</p>"
+        return md_text, vis_html, md_text
     except Exception as e:
-        print(f"❌ Fatal error in /extract: {e}")
-        return jsonify({"error": f"Fatal error: {str(e)}"}), 500
+        raise gr.Error(f"Failed to read PDF: {e}")
+
+def handle_complex_doc(file_path: str, use_chart_recognition: bool) -> Tuple[str, str, str]:
+    if not file_path: raise gr.Error("Please upload an image or PDF first.")
+    ext = os.path.splitext(file_path)[1].lower()
+    if ext == ".pdf":
+        # Use local PyMuPDF for PDFs
+        return _extract_pdf_text_with_pymupdf(file_path)
+    # Otherwise call remote API for images
+    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
+# =========================
+custom_css = """
+/* 全局字体 */
+body, .gradio-container {
+  font-family: "Noto Sans SC", "Microsoft YaHei", "PingFang SC", sans-serif;
+}
+/* ... (rest of the CSS is unchanged) ... */
+.app-header { text-align: center; max-width: 900px; margin: 0 auto 8px !important; }
+.gradio-container { padding: 4px 0 !important; }
+.gradio-container [data-testid="tabs"], .gradio-container .tabs { margin-top: 0 !important; }
+.gradio-container [data-testid="tabitem"], .gradio-container .tabitem { padding-top: 4px !important; }
+.quick-links { text-align: center; padding: 8px 0; border: 1px solid #e5e7eb; border-radius: 8px; margin: 8px auto; max-width: 900px; }
+.quick-links a { margin: 0 12px; font-size: 14px; font-weight: 600; color: #3b82f6; text-decoration: none; }
+.quick-links a:hover { text-decoration: underline; }
+.prompt-grid { display: flex; flex-wrap: wrap; gap: 8px; margin-top: 6px; }
+.prompt-grid button { height: 40px !important; padding: 0 12px !important; border-radius: 8px !important; font-weight: 600 !important; font-size: 13px !important; letter-spacing: 0.2px; }
+#image_preview_vl, #image_preview_doc { height: 400px !important; overflow: auto; }
+#image_preview_vl img, #image_preview_doc img, #vis_image_doc img { width: 100% !important; height: auto !important; object-fit: contain !important; display: block; }
+#md_preview_vl, #md_preview_doc { max-height: 540px; min-height: 180px; overflow: auto; scrollbar-gutter: stable both-edges; }
+#md_preview_vl .prose, #md_preview_doc .prose { line-height: 1.7 !important; }
+#md_preview_vl .prose img, #md_preview_doc .prose img { display: block; margin: 0 auto; max-width: 100%; height: auto; }
+.notice { margin: 8px auto 0; max-width: 900px; padding: 10px 12px; border: 1px solid #e5e7eb; border-radius: 8px; background: #f8fafc; font-size: 14px; line-height: 1.6; }
+.notice strong { font-weight: 700; }
+.notice a { color: #3b82f6; text-decoration: none; }
+.notice a:hover { text-decoration: underline; }
+"""
+
+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="notice"><strong>Heads up:</strong> The Hugging Face demo can be slow at times. For a faster experience, please try <a href="https://aistudio.baidu.com/application/detail/98365" target="_blank" rel="noopener noreferrer">Baidu AI Studio</a> or <a href="https://modelscope.cn/studios/PaddlePaddle/PaddleOCR-VL_Online_Demo/summary" target="_blank" rel="noopener noreferrer">ModelScope</a>.</div>""")
+    gr.HTML("""<div class="quick-links"><a href="https://github.com/PaddlePaddle/PaddleOCR" target="_blank">GitHub</a> | <a href="https://ernie.baidu.com/blog/publication/PaddleOCR-VL_Technical_Report.pdf" target="_blank">Technical Report</a> | <a href="https://huggingface.co/PaddlePaddle/PaddleOCR-VL" 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 or PDF", file_count="single", type="filepath", file_types=["image", ".pdf"])
+                    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 'Element-level 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"):
+                            # 【核心改动点】: 将 gr.Image 替换为 gr.HTML
+                            vis_image_doc = gr.HTML(label="Detection Visualization", 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("Element-level 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("**Element-level 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__":
-    app.run(debug=True)
+    port = int(os.getenv("PORT", "7860"))
+    demo.queue(max_size=6).launch(server_name="0.0.0.0", server_port=port,share=False)