Add aq model

app.py

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+import os
+import clip
+import torch
+import logging
+import json
+import pandas as pd
+from PIL import Image
+import gradio as gr
+from autogluon.tabular import TabularPredictor
+
+predictor = TabularPredictor.load("AutogluonModels/ag-20240615_190835")
+# set logging level
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+)
+logger = logging.getLogger("AQ")
+CLIP_MODEL_NAME = "ViT-B/32"
+
+
+clip_model, preprocess = clip.load(CLIP_MODEL_NAME, device="cpu")
+
+
+def predict_fn(input_img):
+    input_img = Image.fromarray(input_img.astype("uint8"), "RGB")
+    image = preprocess(input_img).unsqueeze(0)
+    with torch.no_grad():
+        image_features = clip_model.encode_image(image).numpy()
+        input_df = pd.DataFrame(image_features[0].reshape(1, -1))
+        quality_score = predictor.predict(input_df).iloc[0]
+
+    logger.info(f"decision: {quality_score}")
+    decision_json = json.dumps({"quality_score": quality_score}).encode("utf-8")
+    logger.info(f"decision_json: {decision_json}")
+    return decision_json
+
+
+iface = gr.Interface(
+    fn=predict_fn,
+    inputs="image",
+    outputs="text",
+    description="""
+    The model returns the probability of the image being a base body. If
+    probability > 0.9, the image can be automatically tagged as a base body. If
+    probability < 0.2, the image can be automatically REJECTED as NOT as base
+    body. All other cases will be submitted for moderation.
+
+    Please flag if you think the decision is wrong.
+    """,
+    allow_flagging="manual",
+    flagging_options=[
+        ": decision should be accept",
+        ": decision should be reject",
+        ": decision should be moderation",
+    ],
+)
+iface.launch()

requirements.txt

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+torch
+torchvision
+ftfy
+regex
+tqdm
+autogluon
+git+https://github.com/openai/CLIP.git
+scikit-learn==1.3.0
+scipy