v2

app.py

@@ -33,12 +33,10 @@ def preprocess_image(img):
 def predict(img):
     img = preprocess_image(img)
     pred = model.predict(img)
-    print(pred)
-    pred = np.round(np.squeeze(pred).astype(float),5)
-    print(pred)
+    pred = np.squeeze(np.exp(pred)/np.sum(np.exp(pred))).astype(float)
+    print(dict(zip(class_names, pred)))
     return dict(zip(class_names, pred))
 
-
 demo = gr.Interface(
     fn=predict,
     inputs=[gr.inputs.Image(type="filepath")],

flagged/log.csv

@@ -0,0 +1,2 @@
+img,output,flag,username,timestamp
+/home/yusuf/okul/clothing/flagged/img/998b370ed87eda7eab93098d8a78a5878a4968dd/tmpiwkw8t51.jpg,/home/yusuf/okul/clothing/flagged/output/tmpit0cnsjg.json,,,2023-08-24 22:43:40.643622

flagged/output/tmpit0cnsjg.json

@@ -0,0 +1 @@
+{"label": "longsleee", "confidences": [{"label": "longsleee", "confidence": 1.6803}, {"label": "dress", "confidence": 1.36643}, {"label": "shorts", "confidence": 0.14555}, {"label": "outwear", "confidence": -0.08118}, {"label": "shirt", "confidence": -0.17827}, {"label": "t-shirt", "confidence": -0.229}, {"label": "hat", "confidence": -2.12421}, {"label": "skirt", "confidence": -2.7243}, {"label": "shoes", "confidence": -2.90811}, {"label": "pants", "confidence": -3.0658}]}

requirements.txt

@@ -1,3 +1,3 @@
-gradio==3.41.0
+gradio==3.38.0
 numpy==1.23.3
 tensorflow==2.9.1