Update app.py

app.py

@@ -3,7 +3,6 @@ import json
 import argparse
 import operator
 import gradio as gr
-import torch
 import torchvision
 from typing import Tuple, Dict
 from facetorch import FaceAnalyzer
@@ -25,26 +24,6 @@ cfg = OmegaConf.load(args.path_conf)
 analyzer = FaceAnalyzer(cfg.analyzer)
 
 
-def tensor_to_list(tensor: torch.Tensor) -> list:
-    return tensor.tolist()
-
-def dataclass_to_dict(obj):
-    if hasattr(obj, "__dataclass_fields__"):
-        return asdict(obj)
-    return obj
-
-def image_data_to_json(image_data: ImageData) -> str:
-    # Convert tensors to lists
-    image_data.img = tensor_to_list(image_data.img)
-    image_data.tensor = tensor_to_list(image_data.tensor)
-    
-    # Convert dataclass to dictionary
-    data_dict = dataclass_to_dict(image_data)
-    
-    # Convert dictionary to JSON string
-    json_str = json.dumps(data_dict, indent=4, default=dataclass_to_dict)
-    return json_str
-
 def gen_sim_dict_str(response: ImageData, pred_name: str = "verify", index: int = 0)-> str:     
     if len(response.faces) > 0:
         base_emb = response.faces[index].preds[pred_name].logits
@@ -71,7 +50,7 @@ def inference(path_image: str) -> Tuple:
     fer_dict_str = str({face.indx: face.preds["fer"].label for face in response.faces})
     au_dict_str = str({face.indx: face.preds["au"].other["multi"] for face in response.faces})
     deepfake_dict_str = str({face.indx: face.preds["deepfake"].label for face in response.faces})
-    response_str = image_data_to_json(response)
+    response_str = str(response)
     
     sim_dict_str_embed = gen_sim_dict_str(response, pred_name="embed", index=0)
     sim_dict_str_verify = gen_sim_dict_str(response, pred_name="verify", index=0)