[pre-commit.ci] auto fixes from pre-commit.com hooks

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misinformation/multimodal_search.py

@@ -84,7 +84,9 @@ class MultimodalSearch(AnalysisMethod):
                 enabled=(MultimodalSearch.multimodal_device != torch.device("cpu"))
             ):
                 features_image = [
-                    model.extract_features({"image": ten, "text_input": ""}, mode="image")
+                    model.extract_features(
+                        {"image": ten, "text_input": ""}, mode="image"
+                    )
                     for ten in images_tensors
                 ]
         else:
@@ -113,8 +115,7 @@ class MultimodalSearch(AnalysisMethod):
         features_text = model.extract_features(sample_text, mode="text")
 
         return features_text
-    
-    
+
     def parsing_images(self, model_type):
         image_keys = sorted(self.keys())
         image_names = [self[k]["filename"] for k in image_keys]
@@ -133,16 +134,32 @@ class MultimodalSearch(AnalysisMethod):
         )
         MultimodalSearch.save_tensors(features_image_stacked)
 
-        return model, vis_processors, txt_processors, image_keys, image_names, features_image_stacked
+        return (
+            model,
+            vis_processors,
+            txt_processors,
+            image_keys,
+            image_names,
+            features_image_stacked,
+        )
 
     def multimodal_search(
-        self, model, vis_processors, txt_processors, model_type, image_keys, features_image_stacked, search_query
+        self,
+        model,
+        vis_processors,
+        txt_processors,
+        model_type,
+        image_keys,
+        features_image_stacked,
+        search_query,
     ):
         features_image_stacked.to(MultimodalSearch.multimodal_device)
-        
+
         for query in search_query:
-            if (len(query)!=1):
-               raise SyntaxError('Each querry must contain either an "image" or a "text_input"')
+            if len(query) != 1:
+                raise SyntaxError(
+                    'Each querry must contain either an "image" or a "text_input"'
+                )
 
         multi_sample = []
         for query in search_query:
@@ -150,21 +167,34 @@ class MultimodalSearch(AnalysisMethod):
                 text_processing = txt_processors["eval"](query["text_input"])
                 image_processing = ""
             elif "image" in query.keys():
-                _, image_processing = MultimodalSearch.read_and_process_images([query["image"]], vis_processors)
+                _, image_processing = MultimodalSearch.read_and_process_images(
+                    [query["image"]], vis_processors
+                )
                 text_processing = ""
-            multi_sample.append({"image": image_processing, "text_input": text_processing})
-        
+            multi_sample.append(
+                {"image": image_processing, "text_input": text_processing}
+            )
+
         multi_features_query = []
         for query in multi_sample:
             if query["image"] == "":
                 features = model.extract_features(query, mode="text")
-                features_squeeze = features.text_embeds_proj[:, 0, :].squeeze(0).to(MultimodalSearch.multimodal_device)
+                features_squeeze = (
+                    features.text_embeds_proj[:, 0, :]
+                    .squeeze(0)
+                    .to(MultimodalSearch.multimodal_device)
+                )
                 multi_features_query.append(features_squeeze)
             if query["text_input"] == "":
-                multi_features_query.append( MultimodalSearch.extract_image_features(
-            model, query["image"], model_type))
-        
-        multi_features_stacked = torch.stack([query.squeeze(0) for query in multi_features_query]).to(MultimodalSearch.multimodal_device)
+                multi_features_query.append(
+                    MultimodalSearch.extract_image_features(
+                        model, query["image"], model_type
+                    )
+                )
+
+        multi_features_stacked = torch.stack(
+            [query.squeeze(0) for query in multi_features_query]
+        ).to(MultimodalSearch.multimodal_device)
 
         similarity = features_image_stacked @ multi_features_stacked.t()
         sorted_lists = [
@@ -184,10 +214,27 @@ class MultimodalSearch(AnalysisMethod):
         if "image" in query.keys():
             pic = Image.open(query["image"]).convert("RGB")
             pic.thumbnail((400, 400))
-            display("Your search query: ", pic,"--------------------------------------------------", "Results:")
+            display(
+                "Your search query: ",
+                pic,
+                "--------------------------------------------------",
+                "Results:",
+            )
         elif "text_input" in query.keys():
-            display("Your search query: " + query["text_input"], "--------------------------------------------------", "Results:")
-        for s in sorted(self.items(), key=lambda t: t[1][list(query.values())[0]], reverse=True):
+            display(
+                "Your search query: " + query["text_input"],
+                "--------------------------------------------------",
+                "Results:",
+            )
+        for s in sorted(
+            self.items(), key=lambda t: t[1][list(query.values())[0]], reverse=True
+        ):
             p1 = Image.open(s[1]["filename"]).convert("RGB")
             p1.thumbnail((400, 400))
-            display(p1, "Rank: " + str(s[1]["rank " + list(query.values())[0]]) + " Val: " + str(s[1][list(query.values())[0]]))
+            display(
+                p1,
+                "Rank: "
+                + str(s[1]["rank " + list(query.values())[0]])
+                + " Val: "
+                + str(s[1][list(query.values())[0]]),
+            )