diff --git a/misinformation/summary.py b/misinformation/summary.py
index a5fbc7a..60fd9cb 100644
--- a/misinformation/summary.py
+++ b/misinformation/summary.py
@@ -7,13 +7,14 @@ from lavis.models import load_model_and_preprocess
 class SummaryDetector(AnalysisMethod):
     def __init__(self, subdict: dict) -> None:
         super().__init__(subdict)
-        self.subdict.update(self.set_keys())
-        self.image_summary = {
-            "const_image_summary": None,
-            "3_non-deterministic summary": None,
-        }
 
     summary_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    summary_model, summary_vis_processors, _ = load_model_and_preprocess(
+        name="blip_caption",
+        model_type="base_coco",
+        is_eval=True,
+        device=summary_device,
+    )
 
     def load_model_base():
         summary_model, summary_vis_processors, _ = load_model_and_preprocess(
@@ -41,14 +42,11 @@ class SummaryDetector(AnalysisMethod):
         summary_model, summary_vis_processors = select_model[model_type]()
         return summary_model, summary_vis_processors
 
-    def set_keys(self) -> dict:
-        params = {
-            "const_image_summary": None,
-            "3_non-deterministic summary": None,
-        }
-        return params
+    def analyse_image(self, summary_model=None, summary_vis_processors=None):
 
-    def analyse_image(self, summary_model, summary_vis_processors):
+        if summary_model is None and summary_vis_processors is None:
+            summary_model = SummaryDetector.summary_model
+            summary_vis_processors = SummaryDetector.summary_vis_processors
 
         path = self.subdict["filename"]
         raw_image = Image.open(path).convert("RGB")
@@ -58,14 +56,12 @@ class SummaryDetector(AnalysisMethod):
             .to(self.summary_device)
         )
         with torch.no_grad():
-            self.image_summary["const_image_summary"] = summary_model.generate(
+            self.subdict["const_image_summary"] = summary_model.generate(
                 {"image": image}
             )[0]
-            self.image_summary["3_non-deterministic summary"] = summary_model.generate(
+            self.subdict["3_non-deterministic summary"] = summary_model.generate(
                 {"image": image}, use_nucleus_sampling=True, num_captions=3
             )
-        for key in self.image_summary:
-            self.subdict[key] = self.image_summary[key]
         return self.subdict
 
     (
@@ -99,10 +95,8 @@ class SummaryDetector(AnalysisMethod):
                 )
 
             for q, a in zip(question_batch, answers_batch):
-                self.image_summary[q] = a
+                self.subdict[q] = a
 
-            for key in self.image_summary:
-                self.subdict[key] = self.image_summary[key]
         else:
             print("Please, enter list of questions")
         return self.subdict