Merge branch 'main' into add_itm

.pre-commit-config.yaml

@@ -2,21 +2,17 @@ repos:
   - repo: https://github.com/kynan/nbstripout
     rev: 0.6.1
     hooks:
-      - id: nbstripout
+    - id: nbstripout
-        files: ".ipynb"
+      files: ".ipynb"
   - repo: https://github.com/psf/black
-    rev: 22.12.0
+    rev: 23.1.0
     hooks:
     - id: black
-  - repo: https://github.com/psf/black
+  - repo: https://github.com/pycqa/flake8
-    rev: 22.12.0
+    rev: 6.0.0 
     hooks:
-      - id: black-jupyter
+    - id: flake8
-  -   repo: https://github.com/pycqa/flake8
+  - repo: https://github.com/s-weigand/flake8-nb
-      rev: 6.0.0 
+    rev: v0.5.0
-      hooks:
+    hooks:
-      -   id: flake8
+    - id: flake8-nb
-  -   repo: https://github.com/s-weigand/flake8-nb
-      rev: v0.5.0
-      hooks:
-      -   id: flake8-nb

misinformation/faces.py

@@ -153,7 +153,6 @@ class EmotionDetector(utils.AnalysisMethod):
         return fresult
 
     def facial_expression_analysis(self) -> dict:
-
         # Find (multiple) faces in the image and cut them
         retinaface_model.get()
         faces = RetinaFace.extract_faces(self.subdict["filename"])

misinformation/multimodal_search.py

@@ -154,6 +154,7 @@ class MultimodalSearch(AnalysisMethod):
 
         return features_text
 
+
     def parsing_images(
         self,
         model_type,
@@ -161,6 +162,9 @@ class MultimodalSearch(AnalysisMethod):
         path_to_load_tensors=None,
     ):
 
+
+
+
         if model_type in ("clip_base", "clip_vitl14_336", "clip_vitl14"):
             path_to_lib = lavis.__file__[:-11] + "models/clip_models/"
             url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/lavis/models/clip_models/bpe_simple_vocab_16e6.txt.gz"
@@ -189,7 +193,11 @@ class MultimodalSearch(AnalysisMethod):
         }
 
         if model_type in select_model.keys():
-            (model, vis_processors, txt_processors,) = select_model[
+            (
+                model,
+                vis_processors,
+                txt_processors,
+            ) = select_model[
                 model_type
             ](self, MultimodalSearch.multimodal_device)
         else:
@@ -225,7 +233,6 @@ class MultimodalSearch(AnalysisMethod):
     def querys_processing(
         self, search_query, model, txt_processors, vis_processors, model_type
     ):
-
         select_extract_image_features = {
             "blip2": MultimodalSearch.extract_image_features_blip2,
             "blip": MultimodalSearch.extract_image_features_basic,

misinformation/summary.py

@@ -37,7 +37,6 @@ class SummaryDetector(AnalysisMethod):
         return summary_model, summary_vis_processors
 
     def analyse_image(self, summary_model=None, summary_vis_processors=None):
-
         if summary_model is None and summary_vis_processors is None:
             summary_model, summary_vis_processors = self.load_model_base()
 

notebooks/colors_expression.ipynb

@@ -48,7 +48,7 @@
     "    startX = 0\n",
     "    # loop over the percentage of each cluster and the color of\n",
     "    # each cluster\n",
-    "    for (percent, color) in zip(hist, centroids):\n",
+    "    for percent, color in zip(hist, centroids):\n",
     "        # plot the relative percentage of each cluster\n",
     "        endX = startX + (percent * 300)\n",
     "        cv2.rectangle(\n",
@@ -123,7 +123,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for (percent, color) in zip(hist, clt.cluster_centers_):\n",
+    "for percent, color in zip(hist, clt.cluster_centers_):\n",
     "    print(\"color:\", color, \" percentage:\", percent)"
    ]
   }