Update app.py

app.py

@@ -2,13 +2,363 @@
 
 
 
+# import fitz  # PyMuPDF
+# import numpy as np
+# import cv2
+# import torch
+# import torch.serialization
+# import os
+# import time # Import for timing
+# from typing import Optional, Tuple, List, Dict, Any
+# from ultralytics import YOLO
+# import logging
+# import gradio as gr
+# import shutil
+# import tempfile
+# import io
+
+# # ============================================================================
+# # --- Global Patches and Setup ---
+# # ============================================================================
+
+# # Patch torch.load to prevent weights_only error with older models
+# _original_torch_load = torch.load
+# def patched_torch_load(*args, **kwargs):
+#     kwargs["weights_only"] = False
+#     return _original_torch_load(*args, **kwargs)
+# torch.load = patched_torch_load
+
+# logging.basicConfig(level=logging.WARNING)
+
+# # ============================================================================
+# # --- CONFIGURATION AND CONSTANTS ---
+# # ============================================================================
+
+# WEIGHTS_PATH = 'best.pt' 
+# SCALE_FACTOR = 2.0 # Used for page rendering and coordinate scaling
+
+# # Detection parameters
+# CONF_THRESHOLD = 0.2
+# TARGET_CLASSES = ['figure', 'equation']
+# IOU_MERGE_THRESHOLD = 0.4
+# IOA_SUPPRESSION_THRESHOLD = 0.7
+
+# # Global counters (Reset per run)
+# GLOBAL_FIGURE_COUNT = 0
+# GLOBAL_EQUATION_COUNT = 0
+
+# # ============================================================================
+# # --- BOX COMBINATION LOGIC ---
+# # ============================================================================
+
+# def calculate_iou(box1, box2):
+#     x1_a, y1_a, x2_a, y2_a = box1
+#     x1_b, y1_b, x2_b, y2_b = box2
+#     x_left = max(x1_a, x1_b)
+#     y_top = max(y1_a, y1_b)
+#     x_right = min(x2_a, x2_b)
+#     y_bottom = min(y2_a, y2_b)
+#     intersection_area = max(0, x_right - x_left) * max(0, y_bottom - y_top)
+#     box_a_area = (x2_a - x1_a) * (y2_a - y1_a)
+#     box_b_area = (x2_b - x1_b) * (y2_b - y1_b)
+#     union_area = float(box_a_area + box_b_area - intersection_area)
+#     return intersection_area / union_area if union_area > 0 else 0
+
+
+# def filter_nested_boxes(detections, ioa_threshold=0.80):
+#     if not detections: return []
+#     for d in detections:
+#         x1, y1, x2, y2 = d['coords']
+#         d['area'] = (x2 - x1) * (y2 - y1)
+#     detections.sort(key=lambda x: x['area'], reverse=True)
+#     keep_indices = []
+#     is_suppressed = [False] * len(detections)
+#     for i in range(len(detections)):
+#         if is_suppressed[i]: continue
+#         keep_indices.append(i)
+#         box_a = detections[i]['coords']
+#         for j in range(i + 1, len(detections)):
+#             if is_suppressed[j]: continue
+#             box_b = detections[j]['coords']
+#             x_left = max(box_a[0], box_b[0])
+#             y_top = max(box_a[1], box_b[1])
+#             x_right = min(box_a[2], box_b[2])
+#             y_bottom = min(box_a[3], box_b[3])
+#             intersection = max(0, x_right - x_left) * max(0, y_bottom - y_top)
+#             area_b = detections[j]['area']
+#             if area_b > 0 and intersection / area_b > ioa_threshold:
+#                 is_suppressed[j] = True
+#     return [detections[i] for i in keep_indices]
+
+
+# def merge_overlapping_boxes(detections, iou_threshold):
+#     if not detections: return []
+#     detections.sort(key=lambda d: d['conf'], reverse=True)
+#     merged_detections = []
+#     is_merged = [False] * len(detections)
+#     for i in range(len(detections)):
+#         if is_merged[i]: continue
+#         current_box = detections[i]['coords']
+#         current_class = detections[i]['class']
+#         merged_x1, merged_y1, merged_x2, merged_y2 = current_box
+#         for j in range(i + 1, len(detections)):
+#             if is_merged[j] or detections[j]['class'] != current_class: continue
+#             other_box = detections[j]['coords']
+#             iou = calculate_iou(current_box, other_box)
+#             if iou > iou_threshold:
+#                 merged_x1 = min(merged_x1, other_box[0])
+#                 merged_y1 = min(merged_y1, other_box[1])
+#                 merged_x2 = max(merged_x2, other_box[2])
+#                 merged_y2 = max(merged_y2, other_box[3])
+#                 is_merged[j] = True
+#         merged_detections.append({
+#             'coords': (merged_x1, merged_y1, merged_x2, merged_y2),
+#             'y1': merged_y1, 'class': current_class, 'conf': detections[i]['conf']
+#         })
+#     return merged_detections
+
+# # ============================================================================
+# # --- UTILITY FUNCTIONS ---
+# # ============================================================================
+
+# def pixmap_to_numpy(pix: fitz.Pixmap) -> np.ndarray:
+#     """Converts a PyMuPDF Pixmap to a NumPy array for OpenCV/YOLO."""
+#     # This function is retained as it's required to convert PDF page to image for YOLO input.
+#     img = np.frombuffer(pix.samples, dtype=np.uint8).reshape(
+#         (pix.h, pix.w, pix.n)
+#     )
+#     if pix.n == 4:
+#         img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
+#     elif pix.n == 1:
+#         img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+#     return img
+
+
+# def run_yolo_detection_and_count(
+#         image: np.ndarray, model: YOLO, page_num: int
+# ) -> Tuple[int, int]: # Removed equation_results list from return
+#     """
+#     Runs YOLO inference, applies NMS/filtering, and updates global counters.
+#     Returns page counts only.
+#     """
+#     global GLOBAL_FIGURE_COUNT, GLOBAL_EQUATION_COUNT
+    
+#     yolo_detections = []
+#     page_equations = 0
+#     page_figures = 0
+    
+#     try:
+#         results = model.predict(image, conf=CONF_THRESHOLD, verbose=False)
+        
+#         if results and results[0].boxes:
+#             for box in results[0].boxes.data.tolist():
+#                 x1, y1, x2, y2, conf, cls_id = box
+#                 cls_name = model.names[int(cls_id)]
+                
+#                 if cls_name in TARGET_CLASSES:
+#                     yolo_detections.append({
+#                         'coords': (x1, y1, x2, y2), 
+#                         'class': cls_name, 
+#                         'conf': conf
+#                     })
+#     except Exception as e:
+#         logging.error(f"YOLO inference failed on page {page_num}: {e}")
+#         return 0, 0
+
+#     # Apply NMS/Merging/Filtering
+#     merged_detections = merge_overlapping_boxes(yolo_detections, IOU_MERGE_THRESHOLD)
+#     final_detections = filter_nested_boxes(merged_detections, IOA_SUPPRESSION_THRESHOLD)
+
+#     # Update Global Counters
+#     for det in final_detections:
+#         if det['class'] == 'figure':
+#             GLOBAL_FIGURE_COUNT += 1
+#             page_figures += 1
+#         elif det['class'] == 'equation':
+#             GLOBAL_EQUATION_COUNT += 1
+#             page_equations += 1
+            
+#     logging.warning(f"  -> Page {page_num}: EQs={page_equations}, Figs={page_figures}")
+#     return page_equations, page_figures
+
+
+# # ============================================================================
+# # --- MAIN DOCUMENT PROCESSING FUNCTION (Optimized) ---
+# # ============================================================================
+
+# def run_single_pdf_preprocessing(pdf_path: str) -> Tuple[int, int, int, str, float, List[str]]:
+#     """
+#     Runs the pipeline, returns counts, report, total time, and an empty list 
+#     (maintaining the expected return signature for Gradio but with None for gallery).
+#     """
+#     global GLOBAL_FIGURE_COUNT, GLOBAL_EQUATION_COUNT
+#     start_time = time.time()
+#     log_messages = []
+
+#     # Reset globals
+#     GLOBAL_FIGURE_COUNT = 0
+#     GLOBAL_EQUATION_COUNT = 0
+
+#     # 1. Validation and Model Loading
+#     t0 = time.time()
+#     if not os.path.exists(pdf_path):
+#         report = f"❌ FATAL ERROR: Input PDF not found at {pdf_path}."
+#         return 0, 0, 0, report, time.time() - start_time, []
+    
+#     try:
+#         model = YOLO(WEIGHTS_PATH)
+#         logging.warning(f"✅ Loaded YOLO model from: {WEIGHTS_PATH}")
+#     except Exception as e:
+#         report = f"❌ ERROR loading YOLO model: {e}\n(Ensure 'best.pt' is available and valid.)"
+#         return 0, 0, 0, report, time.time() - start_time, []
+#     t1 = time.time()
+#     log_messages.append(f"Model Loading Time: {t1-t0:.4f}s")
+    
+#     # 2. PDF Loading
+#     t2 = time.time()
+#     try:
+#         doc = fitz.open(pdf_path)
+#         total_pages = doc.page_count
+#         logging.warning(f"✅ Opened PDF with {doc.page_count} pages")
+#     except Exception as e:
+#         report = f"❌ ERROR loading PDF file: {e}"
+#         return 0, 0, 0, report, time.time() - start_time, []
+#     t3 = time.time()
+#     log_messages.append(f"PDF Initialization Time: {t3-t2:.4f}s")
+
+#     mat = fitz.Matrix(SCALE_FACTOR, SCALE_FACTOR)
+    
+#     # 3. Page Processing and Detection Loop
+#     t4 = time.time()
+#     for page_num_0_based in range(doc.page_count):
+#         page_start_time = time.time()
+#         fitz_page = doc.load_page(page_num_0_based)
+#         page_num = page_num_0_based + 1
+
+#         # Render page to image for YOLO
+#         try:
+#             pix_start = time.time()
+#             pix = fitz_page.get_pixmap(matrix=mat)
+#             original_img = pixmap_to_numpy(pix)
+#             pix_time = time.time() - pix_start
+#         except Exception as e:
+#             logging.error(f"Error converting page {page_num} to image: {e}. Skipping.")
+#             continue
+        
+#         # Core Detection
+#         detect_start = time.time()
+#         run_yolo_detection_and_count(original_img, model, page_num)
+#         detect_time = time.time() - detect_start
+        
+#         page_total_time = time.time() - page_start_time
+#         log_messages.append(f"Page {page_num} Time: Total={page_total_time:.4f}s (Render={pix_time:.4f}s, Detect={detect_time:.4f}s)")
+        
+#     doc.close()
+#     t5 = time.time()
+#     detection_loop_time = t5 - t4
+#     log_messages.append(f"Total Detection Loop Time ({total_pages} pages): {detection_loop_time:.4f}s")
+
+#     # 4. Final Report Generation
+#     total_execution_time = t5 - start_time
+    
+#     report = (
+#         f"✅ **YOLO Counting Complete!**\n\n"
+#         f"**1) Total Pages Detected in PDF:** **{total_pages}**\n"
+#         f"**2) Total Equations Detected:** **{GLOBAL_EQUATION_COUNT}**\n"
+#         f"**3) Total Figures Detected:** **{GLOBAL_FIGURE_COUNT}**\n"
+#         f"---\n"
+#         f"**4) Total Execution Time:** **{total_execution_time:.4f}s**\n"
+#         f"### Detailed Step Timing\n"
+#         f"```\n"
+#         + "\n".join(log_messages) +
+#         f"\n```"
+#     )
+
+#     # Return total_execution_time and an empty list for the gallery output
+#     return total_pages, GLOBAL_EQUATION_COUNT, GLOBAL_FIGURE_COUNT, report, total_execution_time, []
+
+
+# # ============================================================================
+# # --- GRADIO INTERFACE FUNCTION (Updated) ---
+# # ============================================================================
+
+# # NOTE: The return signature has changed. We removed 'temp_output_dir' as it's no longer used.
+# def gradio_process_pdf(pdf_file) -> Tuple[str, str, str, str, List[str]]:
+#     """
+#     Gradio wrapper function to handle file upload and return results (no image handling).
+#     """
+#     if pdf_file is None:
+#         return "N/A", "N/A", "N/A", "Please upload a PDF file.", []
+    
+#     pdf_path = pdf_file.name
+
+#     try:
+#         # Run the core logic
+#         # Note the change: temp_output_dir is removed, and total_time is returned
+#         num_pages, num_equations, num_figures, report, total_time, _ = run_single_pdf_preprocessing(
+#             pdf_path
+#         )
+        
+#         # Return results (the last item is an empty list for the now-empty gallery)
+#         return str(num_pages), str(num_equations), str(num_figures), report, []
+            
+#     except Exception as e:
+#         error_msg = f"An unexpected error occurred: {e}"
+#         logging.error(error_msg, exc_info=True)
+#         return "Error", "Error", "Error", error_msg, []
+
+
+# # ============================================================================
+# # --- GRADIO INTERFACE DEFINITION (Updated) ---
+# # ============================================================================
+
+# if __name__ == "__main__":
+    
+#     if not os.path.exists(WEIGHTS_PATH):
+#         logging.error(f"❌ FATAL ERROR: YOLO weight file '{WEIGHTS_PATH}' not found. Cannot run live inference.")
+        
+#     input_file = gr.File(label="Upload PDF Document", type="filepath", file_types=[".pdf"])
+    
+#     # Outputs
+#     output_pages = gr.Textbox(label="Total Pages in PDF", interactive=False)
+#     output_equations = gr.Textbox(label="Total Equations Detected", interactive=False)
+#     output_figures = gr.Textbox(label="Total Figures Detected", interactive=False)
+#     output_report = gr.Markdown(label="Processing Summary and Timing")
+    
+#     # Gradio Gallery is retained but will receive an empty list []
+#     output_gallery = gr.Gallery(
+#         label="Detected Equations (Disabled for Speed)", 
+#         columns=5, 
+#         height="auto", 
+#         object_fit="contain",
+#         allow_preview=False # Disable preview since it's empty
+#     )
+    
+#     interface = gr.Interface(
+#         fn=gradio_process_pdf,
+#         inputs=input_file,
+#         # The number of outputs remains 5 (3 textboxes, 1 markdown, 1 gallery)
+#         outputs=[output_pages, output_equations, output_figures, output_report, output_gallery],
+#         title="🚀 Optimized YOLO Counting with Timing",
+#         description=(
+#             "Upload a PDF to run YOLO detection. Image cropping is disabled for maximum speed. "
+#             "Timing for each step is included in the summary report."
+#         ),
+#     )
+
+#     print("\nStarting Gradio application...")
+#     interface.launch(inbrowser=True)
+
+
+
+
 import fitz  # PyMuPDF
 import numpy as np
 import cv2
 import torch
 import torch.serialization
 import os
-import time # Import for timing
+import time 
 from typing import Optional, Tuple, List, Dict, Any
 from ultralytics import YOLO
 import logging
@@ -35,7 +385,7 @@ logging.basicConfig(level=logging.WARNING)
 # ============================================================================
 
 WEIGHTS_PATH = 'best.pt' 
-SCALE_FACTOR = 2.0 # Used for page rendering and coordinate scaling
+SCALE_FACTOR = 2.0 
 
 # Detection parameters
 CONF_THRESHOLD = 0.2
@@ -48,7 +398,7 @@ GLOBAL_FIGURE_COUNT = 0
 GLOBAL_EQUATION_COUNT = 0
 
 # ============================================================================
-# --- BOX COMBINATION LOGIC ---
+# --- BOX COMBINATION LOGIC (Retained for detection accuracy) ---
 # ============================================================================
 
 def calculate_iou(box1, box2):
@@ -123,7 +473,6 @@ def merge_overlapping_boxes(detections, iou_threshold):
 
 def pixmap_to_numpy(pix: fitz.Pixmap) -> np.ndarray:
     """Converts a PyMuPDF Pixmap to a NumPy array for OpenCV/YOLO."""
-    # This function is retained as it's required to convert PDF page to image for YOLO input.
     img = np.frombuffer(pix.samples, dtype=np.uint8).reshape(
         (pix.h, pix.w, pix.n)
     )
@@ -136,7 +485,7 @@ def pixmap_to_numpy(pix: fitz.Pixmap) -> np.ndarray:
 
 def run_yolo_detection_and_count(
         image: np.ndarray, model: YOLO, page_num: int
-) -> Tuple[int, int]: # Removed equation_results list from return
+) -> Tuple[int, int]:
     """
     Runs YOLO inference, applies NMS/filtering, and updates global counters.
     Returns page counts only.
@@ -183,17 +532,20 @@ def run_yolo_detection_and_count(
 
 
 # ============================================================================
-# --- MAIN DOCUMENT PROCESSING FUNCTION (Optimized) ---
+# --- MAIN DOCUMENT PROCESSING FUNCTION (Revised for Dict Return) ---
 # ============================================================================
 
-def run_single_pdf_preprocessing(pdf_path: str) -> Tuple[int, int, int, str, float, List[str]]:
+# NOTE: The return signature now includes the equation_counts_per_page dictionary
+def run_single_pdf_preprocessing(pdf_path: str) -> Tuple[int, int, int, str, float, Dict[int, int], List[str]]:
     """
-    Runs the pipeline, returns counts, report, total time, and an empty list 
-    (maintaining the expected return signature for Gradio but with None for gallery).
+    Runs the pipeline, returns counts, report, total time, page counts dict, and empty list.
     """
     global GLOBAL_FIGURE_COUNT, GLOBAL_EQUATION_COUNT
     start_time = time.time()
     log_messages = []
+    
+    # NEW: Dictionary to store {page_number: equation_count}
+    equation_counts_per_page: Dict[int, int] = {} 
 
     # Reset globals
     GLOBAL_FIGURE_COUNT = 0
@@ -203,14 +555,16 @@ def run_single_pdf_preprocessing(pdf_path: str) -> Tuple[int, int, int, str, flo
     t0 = time.time()
     if not os.path.exists(pdf_path):
         report = f"❌ FATAL ERROR: Input PDF not found at {pdf_path}."
-        return 0, 0, 0, report, time.time() - start_time, []
+        # Add the new return value (empty dict)
+        return 0, 0, 0, report, time.time() - start_time, {}, []
     
     try:
         model = YOLO(WEIGHTS_PATH)
         logging.warning(f"✅ Loaded YOLO model from: {WEIGHTS_PATH}")
     except Exception as e:
         report = f"❌ ERROR loading YOLO model: {e}\n(Ensure 'best.pt' is available and valid.)"
-        return 0, 0, 0, report, time.time() - start_time, []
+        # Add the new return value (empty dict)
+        return 0, 0, 0, report, time.time() - start_time, {}, []
     t1 = time.time()
     log_messages.append(f"Model Loading Time: {t1-t0:.4f}s")
     
@@ -222,7 +576,8 @@ def run_single_pdf_preprocessing(pdf_path: str) -> Tuple[int, int, int, str, flo
         logging.warning(f"✅ Opened PDF with {doc.page_count} pages")
     except Exception as e:
         report = f"❌ ERROR loading PDF file: {e}"
-        return 0, 0, 0, report, time.time() - start_time, []
+        # Add the new return value (empty dict)
+        return 0, 0, 0, report, time.time() - start_time, {}, []
     t3 = time.time()
     log_messages.append(f"PDF Initialization Time: {t3-t2:.4f}s")
 
@@ -247,9 +602,12 @@ def run_single_pdf_preprocessing(pdf_path: str) -> Tuple[int, int, int, str, flo
         
         # Core Detection
         detect_start = time.time()
-        run_yolo_detection_and_count(original_img, model, page_num)
+        page_equations, _ = run_yolo_detection_and_count(original_img, model, page_num)
         detect_time = time.time() - detect_start
         
+        # NEW: Store the count in the dictionary
+        equation_counts_per_page[page_num] = page_equations
+        
         page_total_time = time.time() - page_start_time
         log_messages.append(f"Page {page_num} Time: Total={page_total_time:.4f}s (Render={pix_time:.4f}s, Detect={detect_time:.4f}s)")
         
@@ -274,38 +632,39 @@ def run_single_pdf_preprocessing(pdf_path: str) -> Tuple[int, int, int, str, flo
         f"\n```"
     )
 
-    # Return total_execution_time and an empty list for the gallery output
-    return total_pages, GLOBAL_EQUATION_COUNT, GLOBAL_FIGURE_COUNT, report, total_execution_time, []
+    # Return the new dictionary as the sixth element, and an empty list as the seventh
+    return total_pages, GLOBAL_EQUATION_COUNT, GLOBAL_FIGURE_COUNT, report, total_execution_time, equation_counts_per_page, []
 
 
 # ============================================================================
 # --- GRADIO INTERFACE FUNCTION (Updated) ---
 # ============================================================================
 
-# NOTE: The return signature has changed. We removed 'temp_output_dir' as it's no longer used.
-def gradio_process_pdf(pdf_file) -> Tuple[str, str, str, str, List[str]]:
+# NOTE: The return signature now includes the equation_counts_per_page dictionary (Dict[int, int])
+def gradio_process_pdf(pdf_file) -> Tuple[str, str, str, str, Dict[int, int], List[str]]:
     """
-    Gradio wrapper function to handle file upload and return results (no image handling).
+    Gradio wrapper function to handle file upload and return results.
     """
     if pdf_file is None:
-        return "N/A", "N/A", "N/A", "Please upload a PDF file.", []
+        # Return an empty dict for the new JSON output
+        return "N/A", "N/A", "N/A", "Please upload a PDF file.", {}, []
     
     pdf_path = pdf_file.name
 
     try:
-        # Run the core logic
-        # Note the change: temp_output_dir is removed, and total_time is returned
-        num_pages, num_equations, num_figures, report, total_time, _ = run_single_pdf_preprocessing(
+        # Unpack the new return value: equation_counts_per_page
+        num_pages, num_equations, num_figures, report, total_time, equation_counts_per_page, _ = run_single_pdf_preprocessing(
             pdf_path
         )
         
-        # Return results (the last item is an empty list for the now-empty gallery)
-        return str(num_pages), str(num_equations), str(num_figures), report, []
+        # Return results (5 items now: 3 textboxes, 1 markdown, 1 JSON, 1 empty list for gallery)
+        return str(num_pages), str(num_equations), str(num_figures), report, equation_counts_per_page, []
             
     except Exception as e:
         error_msg = f"An unexpected error occurred: {e}"
         logging.error(error_msg, exc_info=True)
-        return "Error", "Error", "Error", error_msg, []
+        # Return an empty dict for the new JSON output on error
+        return "Error", "Error", "Error", error_msg, {}, []
 
 
 # ============================================================================
@@ -325,24 +684,34 @@ if __name__ == "__main__":
     output_figures = gr.Textbox(label="Total Figures Detected", interactive=False)
     output_report = gr.Markdown(label="Processing Summary and Timing")
     
+    # NEW OUTPUT: JSON component for structured data
+    output_page_counts = gr.JSON(label="Equation Count Per Page (Dictionary)")
+    
     # Gradio Gallery is retained but will receive an empty list []
     output_gallery = gr.Gallery(
         label="Detected Equations (Disabled for Speed)", 
         columns=5, 
         height="auto", 
         object_fit="contain",
-        allow_preview=False # Disable preview since it's empty
+        allow_preview=False 
     )
     
     interface = gr.Interface(
         fn=gradio_process_pdf,
         inputs=input_file,
-        # The number of outputs remains 5 (3 textboxes, 1 markdown, 1 gallery)
-        outputs=[output_pages, output_equations, output_figures, output_report, output_gallery],
-        title="🚀 Optimized YOLO Counting with Timing",
+        # Outputs now include the JSON component
+        outputs=[
+            output_pages, 
+            output_equations, 
+            output_figures, 
+            output_report, 
+            output_page_counts, # New
+            output_gallery
+        ],
+        title="📊 YOLO Counting with Per-Page Data & Timing",
         description=(
-            "Upload a PDF to run YOLO detection. Image cropping is disabled for maximum speed. "
-            "Timing for each step is included in the summary report."
+            "Upload a PDF to run YOLO detection. The results include total counts, a breakdown of "
+            "equation counts per page (in JSON format), and detailed timing."
         ),
     )