test2

app.py

@@ -15,6 +15,7 @@ import concurrent.futures
 import ast
 import torch
 from gradio_log import Log
+from pathlib import Path
 
 from utils.utils import softmax, augment_image, preprocess_resize_256, preprocess_resize_224, postprocess_pipeline, postprocess_logits, postprocess_binary_output, to_float_scalar, infer_gradio_api, preprocess_gradio_api, postprocess_gradio_api
 from utils.onnx_helpers import preprocess_onnx_input, postprocess_onnx_output, infer_onnx_model
@@ -33,7 +34,7 @@ from utils.registry import register_model, MODEL_REGISTRY, ModelEntry
 from agents.ensemble_weights import ModelWeightManager
 from transformers import pipeline, AutoImageProcessor, SwinForImageClassification, Swinv2ForImageClassification, AutoFeatureExtractor, AutoModelForImageClassification
 from torchvision import transforms
-
+load_dotenv()
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 os.environ['HF_HUB_CACHE'] = './models'
@@ -46,7 +47,58 @@ log_queue, gradio_handler = setup_gradio_logging()
 
 LOCAL_LOG_DIR = "./hf_inference_logs"
 HF_DATASET_NAME="aiwithoutborders-xyz/degentic_rd0"
-load_dotenv()
+
+
+class CustomFormatter(logging.Formatter):
+
+    green = "\x1b[32;20m"
+    blue = "\x1b[34;20m"
+    yellow = "\x1b[33;20m"
+    red = "\x1b[31;20m"
+    bold_red = "\x1b[31;1m"
+    reset = "\x1b[0m"
+    format = "%(asctime)s - %(levelname)s - %(message)s (%(filename)s:%(lineno)d)"
+
+    FORMATS = {
+        logging.DEBUG: blue + format + reset,
+        logging.INFO: green + format + reset,
+        logging.WARNING: yellow + format + reset,
+        logging.ERROR: red + format + reset,
+        logging.CRITICAL: bold_red + format + reset,
+    }
+
+    def format(self, record):
+        log_fmt = self.FORMATS.get(record.levelno)
+        formatter = logging.Formatter(log_fmt)
+        return formatter.format(record)
+
+
+formatter = CustomFormatter()
+
+log_file = "/tmp/gradio_log.txt"
+Path(log_file).touch()
+
+ch = logging.FileHandler(log_file)
+ch.setLevel(logging.DEBUG)
+ch.setFormatter(formatter)
+
+logger = logging.getLogger("gradio_log")
+logger.setLevel(logging.DEBUG)
+for handler in logger.handlers:
+    logger.removeHandler(handler)
+logger.addHandler(ch)
+
+
+logger.info("The logs will be displayed in here.")
+
+
+def create_log_handler(level):
+    def l(text):
+        getattr(logger, level)(text)
+
+    return l
+
+
 
 # Custom JSON Encoder to handle numpy types
 class NumpyEncoder(json.JSONEncoder):
@@ -417,7 +469,6 @@ detection_model_eval_playground = gr.Interface(
         ),
         gr.JSON(label="Raw Model Results", visible=False),
         gr.Markdown(label="Consensus", value="")
-        Log(anomaly_detection_results, dark=True, xterm_font_size=12)
     ],
     title="Multi-Model Ensemble + Agentic Coordinated Deepfake Detection (Paper in Progress)",
     description="The detection of AI-generated images has entered a critical inflection point. While existing solutions struggle with outdated datasets and inflated claims, our approach prioritizes agility, community collaboration, and an offensive approach to deepfake detection.",
@@ -622,6 +673,11 @@ footer = gr.Markdown(footerMD, elem_classes="footer")
 
 with gr.Blocks() as app:
     demo.render()
+    with gr.Row():
+    for l in ["debug", "info", "warning", "error", "critical"]:
+        button = gr.Button(f"log as {l}")
+        button.click(fn=create_log_handler(l), inputs=text)
+    Log(log_file, dark=True).render()
     footer.render()