comic grading

.gitignore

@@ -0,0 +1 @@
+.env

app.py

@@ -234,14 +234,23 @@ with gr.Blocks() as demo:
 
     regenerate_comps_btn.click(
         core.regenerate_composition_data,
-        inputs=[],
+        inputs=[image_description,
+            narration,
+            character,
+            dialouge,
+            location,
+            setting,
+            current_episode,
+            current_scene,
+            current_frame,
+            episodes_data
+            ],
         outputs=[]
     )
-
     regenerate_btn.click(
         core.regenerate_data,
         inputs=[],
         outputs=[]
     )
 
-demo.launch(auth=("admin", "Qrt@12*34#immersfy"), share=True, ssr_mode=False)
+demo.launch(auth=("admin", "Qrt@12*34#immersfy"), share=True, ssr_mode=False,  debug=True)

core.py

@@ -1,17 +1,20 @@
 """House of all specific functions used to control data flow."""
+
 from typing import List
 from PIL import Image
 import gradio as gr
 import dataclasses
 import io
 import jinja2
-
+import base64
 import aws_utils
 import parameters
 import script_gen
 import io as iowrapper
+import openai_wrapper
 
 AWS_BUCKET = parameters.AWS_BUCKET
+llm = openai_wrapper.GPT_4O_MINI
 
 
 @dataclasses.dataclass
@@ -28,11 +31,12 @@ class ComicFrame:
     narration: str
     character_dilouge: str
     character: str
-    compositions: List[Composition] = dataclasses.field(default_factory=list)
-    location: str
+    location: str  # Moved up here
     setting: str
     all_characters: list
-
+    compositions: List[Composition] = dataclasses.field(
+        default_factory=list
+    )  # Keep this as the last argument
 
 
 def list_current_dir(bucket_name: str, folder_path: str = "") -> list:
@@ -124,7 +128,9 @@ def load_metadata_fn(comic_id: str):
     )
 
 
-def load_data_next(episodes_data: list, current_episode: int, current_frame: int):
+def load_data_next(
+    episodes_data: list, current_episode: int, current_frame: int, is_developer=False
+):
     if current_frame + 1 < len(episodes_data[current_episode]):
         current_frame += 1
     elif current_episode + 1 < len(episodes_data):
@@ -135,11 +141,13 @@ def load_data_next(episodes_data: list, current_episode: int, current_frame: int
     return (
         gr.update(value=current_episode),
         gr.update(value=current_frame),
-        *load_data_inner(episodes_data, current_episode, current_frame),
+        *load_data_inner(episodes_data, current_episode, current_frame, is_developer),
     )
 
 
-def load_data_prev(episodes_data: list, current_episode: int, current_frame: int):
+def load_data_prev(
+    episodes_data: list, current_episode: int, current_frame: int, is_developer=False
+):
     if current_frame - 1 >= 0:
         current_frame -= 1
     elif current_episode - 1 > min(list(episodes_data.keys())):
@@ -150,7 +158,7 @@ def load_data_prev(episodes_data: list, current_episode: int, current_frame: int
     return (
         gr.update(value=current_episode),
         gr.update(value=current_frame),
-        *load_data_inner(episodes_data, current_episode, current_frame),
+        *load_data_inner(episodes_data, current_episode, current_frame, is_developer),
     )
 
 
@@ -160,7 +168,7 @@ def load_from_dropdown(
     return (
         gr.update(value=selected_episode),
         gr.update(value=selected_frame),
-        *load_data_inner(episodes_data, selected_episode, selected_frame),
+        *load_data_inner(episodes_data, selected_episode, selected_frame, is_developer),
     )
 
 
@@ -218,52 +226,58 @@ def regenerate_composition_data(
     current_frame: int,
     episodes_data: dict,
 ):
-    print(
-        f"Generating compositions for episode: {current_episode} and scene: {current_scene} and frame: {}."
-    )
-    prompt_dict = {
-        "system": script_gen.generate_image_compositions_instruction,
-        "user": jinja2.Template(
-            source=script_gen.generate_image_compositions_user_prompt
-        ).render(
-            {
-                "FRAME": dataclasses.asdict(frame),
-            }
-        ),
-    }
+    pass
+#     print(
+#         f"Generating compositions for episode: {current_episode} and scene: {current_scene} and frame: {current_frame}."
+#     )
+#     frame = episodes_data[current_episode][current_frame]
+#     print(frame)
+#     prompt_dict = {
+#         "system": script_gen.generate_image_compositions_instruction,
+#         "user": jinja2.Template(
+#             source=script_gen.generate_image_compositions_user_prompt
+#         ).render(
+#             {
+#                 "FRAME": frame,
+#             }
+#         ),
+#     }
+
+#     compositions = llm.generate_valid_json_response(prompt_dict)
+#     print(compositions)
+    # frame.compositions = [
+    #     Composition(**composition)
+    #     for composition in compositions["compositions"]
+    # ]
 
-    compositions = llm.generate_valid_json_response(prompt_dict)
-    print(compositions)
-    frame.compositions = [
-        Composition(**composition)
-        for composition in compositions["compositions"]
-    ]
-    
 
 def regenerate_data(
-    frame_data: ComicFrame, 
+    frame_data: ComicFrame,
 ):
-    # for 
-    payload = {
-        "prompt": composition.prompt,
-        "characters": related_chars,
-        "parameters": {
-            "height": parameters.IMG_HEIGHT,
-            "width": parameters.IMG_WIDTH,
-            "visual_style": visual_style,
-            "seed": seed_val,
-        },
-    }
-
-    data = iowrapper.get_valid_post_response(
-        url=parameters.MODEL_SERVER_URL + "/generate_image",
-        payload=payload,
-    )
-    image_data = io.BytesIO(base64.b64decode(data["image"]))
-    path = aws_utils.save_to_s3(
-        parameters.AWS_BUCKET,
-        f"{self.id}/episodes/episode-{episode_num}/compositions/scene-{scene_num}/frame-{frame_num}",
-        image_data,
-        f"{num}.jpg",
-    )
-    pass
+    pass
+
+
+#     # for
+#     payload = {
+#         "prompt": composition.prompt,
+#         "characters": related_chars,
+#         "parameters": {
+#             "height": parameters.IMG_HEIGHT,
+#             "width": parameters.IMG_WIDTH,
+#             "visual_style": visual_style,
+#             "seed": seed_val,
+#         },
+#     }
+
+#     data = iowrapper.get_valid_post_response(
+#         url=parameters.MODEL_SERVER_URL + "/generate_image",
+#         payload=payload,
+#     )
+#     image_data = io.BytesIO(base64.b64decode(data["image"]))
+#     path = aws_utils.save_to_s3(
+#         parameters.AWS_BUCKET,
+#         f"{self.id}/episodes/episode-{episode_num}/compositions/scene-{scene_num}/frame-{frame_num}",
+#         image_data,
+#         f"{num}.jpg",
+#     )
+#     pass

parameters.py

@@ -1,4 +1,8 @@
 import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
 
 AWS_BUCKET = os.getenv("AWS_BUCKET")
 os.environ["AWS_ACCESS_KEY_ID"] = os.getenv("AWS_ACCESS_KEY_ID")
@@ -6,4 +10,4 @@ os.environ["AWS_SECRET_ACCESS_KEY"] = os.getenv("AWS_SECRET_ACCESS_KEY")
 os.environ["S3_BUCKET_NAME"] = os.getenv("AWS_BUCKET")
 VISUAL_CHOICES = ["DARK", "FLUX", "GHIBLI_COMIC"]
 MAX_TRIES = os.getenv("MAX_TRIES")
-OPEN_AI_API_KEY = os.getenv("OPEN_AI_API_KEY")
+OPEN_AI_API_KEY = os.getenv("OPEN_AI_KEY")