Ocr one task per pdf page

.gitignore

@@ -176,7 +176,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 
 # Abstra
 # Abstra is an AI-powered process automation framework.

graphai/celery/image/jobs.py

@@ -11,6 +11,7 @@
     extract_slide_text_task,
     extract_slide_text_callback_task,
     convert_pdf_to_pages_task,
+    fanout_pdf_ocr_task,
     extract_multi_image_text_task,
     collect_multi_image_ocr_task
 )
@@ -153,24 +154,17 @@ def ocr_job(
     # OCR computation job
     #####################
     if is_pdf(token):
-        n_parallel = 8
         task_list = [
             convert_pdf_to_pages_task.s(token),
-            group(
-                extract_multi_image_text_task.s(
-                    i,
-                    n_parallel,
-                    method,
-                    google_api_token,
-                    openai_api_token,
-                    gemini_api_token,
-                    rcp_api_token,
-                    model_type,
-                    enable_tikz,
-                )
-                for i in range(n_parallel)
+            fanout_pdf_ocr_task.s(
+                method,
+                google_api_token,
+                openai_api_token,
+                gemini_api_token,
+                rcp_api_token,
+                model_type,
+                enable_tikz,
             ),
-            collect_multi_image_ocr_task.s()
         ]
     else:
         task_list = [

graphai/celery/image/tasks.py

@@ -1,4 +1,4 @@
-from celery import shared_task
+from celery import shared_task, group, chord
 
 from graphai.core.image.image import (
     cache_lookup_retrieve_image_from_url,
@@ -146,6 +146,47 @@ def convert_pdf_to_pages_task(self, token):
     return break_pdf_into_images(token, self.file_manager)
 
 
+@shared_task(
+    bind=True,
+    autoretry_for=(Exception,),
+    retry_backoff=True,
+    retry_kwargs={"max_retries": 2},
+    name="image.fanout_pdf_ocr_task",
+    ignore_result=False,
+)
+def fanout_pdf_ocr_task(
+    self,
+    pages,
+    method,
+    google_api_token=None,
+    openai_api_token=None,
+    gemini_api_token=None,
+    rcp_api_token=None,
+    model_type=None,
+    enable_tikz=False,
+):
+    # Build one OCR task per page
+    header = group(
+        extract_multi_image_text_task.s(
+            page,
+            method,
+            google_api_token,
+            openai_api_token,
+            gemini_api_token,
+            rcp_api_token,
+            model_type,
+            enable_tikz,
+        )
+        for page in pages
+    )
+
+    # When all pages are OCR'd, collect results
+    callback = collect_multi_image_ocr_task.s()
+
+    # Replace this task with the chord so the outer chain waits properly
+    raise self.replace(chord(header, callback))
+
+
 @shared_task(
     bind=True,
     autoretry_for=(Exception,),
@@ -156,9 +197,7 @@ def convert_pdf_to_pages_task(self, token):
 )
 def extract_multi_image_text_task(
     self,
-    page_and_filename_list,
-    i,
-    n,
+    page_and_filename,
     method="google",
     google_api_token=None,
     openai_api_token=None,
@@ -167,11 +206,9 @@ def extract_multi_image_text_task(
     model_type=None,
     enable_tikz=False,
 ):
-    print(f'Starting {extract_multi_image_text_task} task for page_and_filename_list {page_and_filename_list}, i {i} and n {n}')
+    print(f'Starting {extract_multi_image_text_task} task for page_and_filename {page_and_filename}')
     return extract_multi_image_text(
-        page_and_filename_list,
-        i,
-        n,
+        page_and_filename,
         method,
         google_api_token,
         openai_api_token,

graphai/core/image/image.py

@@ -210,30 +210,19 @@ def perform_ocr(
     model_type=None,
     enable_tikz=False,
 ):
-    ocr_colnames = get_ocr_colnames(method)
-
-    results = None
-    language = None
+    text = None
 
     if method == 'tesseract':
-        res = perform_tesseract_ocr(file_path, language='enfr')
+        text = perform_tesseract_ocr(file_path, language='enfr')
 
-        if res:
-            language = detect_text_language(res)
-            results = [{'method': ocr_colnames[0], 'text': res}]
     elif method == 'google' and google_api_token:
         ocr_model = GoogleOCRModel(google_api_token)
         ocr_model.establish_connection()
-        res1, res2 = ocr_model.perform_ocr(file_path)
+        text1, text2 = ocr_model.perform_ocr(file_path)
+
+        # Since DTD usually performs better, method #1 is our point of reference for langdetect
+        text = text1
 
-        if res1:
-            # Since DTD usually performs better, method #1 is our point of reference for langdetect
-            language = detect_text_language(res1)
-            res_list = [res1]
-            results = [
-                {'method': ocr_colnames[i], 'text': res_list[i]}
-                for i in range(len(res_list))
-            ]
     else:
         ocr_model = None
         if method == 'openai' and openai_api_token:
@@ -245,17 +234,14 @@ def perform_ocr(
 
         if ocr_model:
             ocr_model.establish_connection()
-            res = ocr_model.perform_ocr(
-                file_path, model_type=model_type, enable_tikz=enable_tikz
-            )
+            text = ocr_model.perform_ocr(file_path, model_type=model_type, enable_tikz=enable_tikz)
 
-            if res:
-                language = detect_text_language(res)
-                results = [{'method': ocr_colnames[0], 'text': res}]
+    if not text:
+        text = ''
 
     return {
-        'results': results,
-        'language': language,
+        'results': [{'method': get_ocr_colnames(method)[0], 'text': text}],
+        'language': detect_text_language(text),
     }
 
 
@@ -296,9 +282,7 @@ def extract_slide_text(
 
 
 def extract_multi_image_text(
-    page_and_filename_list,
-    i,
-    n,
+    page_and_filename,
     method="google",
     google_api_token=None,
     openai_api_token=None,
@@ -307,44 +291,33 @@ def extract_multi_image_text(
     model_type=None,
     enable_tikz=False,
 ):
-    # Extract subset of pages to process
-    n_pages = len(page_and_filename_list)
-    start_index = int(i / n * n_pages)
-    end_index = int((i + 1) / n * n_pages)
-    pages_to_handle = page_and_filename_list[start_index: end_index]
-
-    # Perform OCR on subset of pages
-    results = list()
-    for page in pages_to_handle:
-        results.append(
-            perform_ocr(
-                page["filename"],
-                method,
-                google_api_token,
-                openai_api_token,
-                gemini_api_token,
-                rcp_api_token,
-                model_type,
-                enable_tikz,
-            )
-        )
+    # Perform OCR on page
+    result = perform_ocr(
+        page_and_filename["filename"],
+        method,
+        google_api_token,
+        openai_api_token,
+        gemini_api_token,
+        rcp_api_token,
+        model_type,
+        enable_tikz,
+    )
+
+    print(f"Performed OCR on page {page_and_filename['page']}. Result: {result}")
 
     # Build result and return it
     return {
-        'results': [
-            {
-                'page': pages_to_handle[i]['page'],
-                'content': results[i]['results'][0]['text']
-            }
-            for i in range(len(results))
-        ],
-        'language': get_most_common_element([result['language'] for result in results]),
-        'method': get_most_common_element([result['results'][0]['method'] for result in results])
+        'result': {
+            'page': page_and_filename['page'],
+            'content': result['results'][0]['text']
+        },
+        'language': result['language'],
+        'method': result['results'][0]['method'],
     }
 
 
 def collect_multi_image_ocr(results):
-    all_results = list(chain.from_iterable(result['results'] for result in results))
+    all_results = [result['result'] for result in results]
     language = get_most_common_element([result['language'] for result in results])
     method = get_most_common_element([result['method'] for result in results])
     return {

graphai/core/image/ocr.py

@@ -342,7 +342,6 @@ def perform_ocr(self, input_filename_with_path, model_type=None, **kwargs):
             response = self.model.chat.completions.create(model=model_type, messages=messages, response_format={"type": "json_object"})
             print(f'Got {response}')
             content = response.choices[0].message.content.strip()
-            print(f'Got {content}')
 
             # Strip thinking tokens
             thinking_tag = '</think>'

pyproject.toml

@@ -17,7 +17,7 @@ classifiers = [
     "Operating System :: OS Independent"
 ]
 dependencies = [
-    "loguru"
+    "loguru",
     "numpy",
     "scipy",
     "pandas",