Update engineers/deformes2D_thinker.py

engineers/deformes2D_thinker.py

@@ -2,12 +2,13 @@
 #
 # Copyright (C) August 4, 2025  Carlos Rodrigues dos Santos
 #
-# Version: 1.0.0
+# Version: 1.0.1
 #
 # This file defines the Deformes2DThinker, the primary cognitive engine of the
 # ADUC framework. It encapsulates all prompt engineering and creative decision-making
-# logic, acting as the "Director Brain." It knows what questions to ask the LLM
-# and how to interpret the answers for cinematic purposes.
+# logic. This version corrects the method calls to the GeminiManager, ensuring that
+# all prompt components are assembled into a single list before being passed to the
+# communication layer.
 
 import logging
 from pathlib import Path
@@ -40,7 +41,9 @@ class Deformes2DThinker:
             storyboard_prompt = template.format(user_prompt=prompt, num_fragments=num_keyframes)
             images = [Image.open(p) for p in ref_image_paths]
             
-            storyboard_data = gemini_manager_singleton.get_json_object(storyboard_prompt, images)
+            # Assemble all parts into a single list for the manager
+            prompt_parts = [storyboard_prompt] + images
+            storyboard_data = gemini_manager_singleton.get_json_object(prompt_parts)
             
             storyboard = storyboard_data.get("scene_storyboard", [])
             if not storyboard or len(storyboard) != num_keyframes: 
@@ -58,21 +61,14 @@ class Deformes2DThinker:
             template = self._read_prompt_template("keyframe_selection_prompt.txt")
             
             image_map = {f"IMG-{i+1}": path for i, path in enumerate(pool_image_paths)}
-            base_image_map = {f"BASE-{i+1}": path for i, path in enumerate(base_image_paths)}
             
-            model_contents = [
-                ("# Reference Images (Story Base)", [Image.open(p) for p in base_image_paths]),
-                ("\n# Image Pool (Scene Bank)", [Image.open(p) for p in pool_image_paths])
-            ]
+            prompt_parts = ["# Reference Images (Story Base)"]
+            prompt_parts.extend([Image.open(p) for p in base_image_paths])
+            prompt_parts.append("\n# Image Pool (Scene Bank)")
+            prompt_parts.extend([Image.open(p) for p in pool_image_paths])
 
             storyboard_str = "\n".join([f"- Scene {i+1}: {s}" for i, s in enumerate(storyboard)])
             selection_prompt = template.format(storyboard_str=storyboard_str, image_identifiers=list(image_map.keys()))
-            
-            # The Gemini Manager expects a flat list of prompt parts
-            prompt_parts = []
-            for title, images in model_contents:
-                prompt_parts.append(title)
-                prompt_parts.extend(images)
             prompt_parts.append(selection_prompt)
 
             selection_data = gemini_manager_singleton.get_json_object(prompt_parts)