AI-or-Not-dev

Runtime error

Omnibus commited on Oct 13, 2023

Commit

b69e293

•

1 Parent(s): 2671156

Update app.py

Files changed (1) hide show

app.py CHANGED Viewed

@@ -17,16 +17,16 @@ models=[
 def aiornot0(image):
     labels = ["Real", "AI"]
     mod=models[0]
-    feature_extractor = AutoFeatureExtractor.from_pretrained(mod)
-    model = AutoModelForImageClassification.from_pretrained(mod)
-    input = feature_extractor(image, return_tensors="pt")
     with torch.no_grad():
-        outputs = model(**input)
         print (outputs)
         logits = outputs.logits
         print (logits)
         probability = softmax(logits)
-        print(probability)
     prediction = logits.argmax(-1).item()
     label = labels[prediction]
@@ -34,11 +34,11 @@ def aiornot0(image):
 def aiornot1(image):
     labels = ["Real", "AI"]
     mod=models[1]
-    feature_extractor = AutoFeatureExtractor.from_pretrained(mod)
-    model = AutoModelForImageClassification.from_pretrained(mod)
-    input = feature_extractor(image, return_tensors="pt")
     with torch.no_grad():
-      outputs = model(**input)
       print (outputs)
       logits = outputs.logits
       print (logits)
@@ -48,11 +48,11 @@ def aiornot1(image):
 def aiornot2(image):
     labels = ["Real", "AI"]
     mod=models[2]
-    feature_extractor = AutoFeatureExtractor.from_pretrained(mod)
-    model = AutoModelForImageClassification.from_pretrained(mod)
-    input = feature_extractor(image, return_tensors="pt")
     with torch.no_grad():
-      outputs = model(**input)
       print (outputs)
       logits = outputs.logits
       print (logits)

 def aiornot0(image):
     labels = ["Real", "AI"]
     mod=models[0]
+    feature_extractor0 = AutoFeatureExtractor.from_pretrained(mod)
+    model0 = AutoModelForImageClassification.from_pretrained(mod)
+    input = feature_extractor0(image, return_tensors="pt")
     with torch.no_grad():
+        outputs = model0(**input)
         print (outputs)
         logits = outputs.logits
         print (logits)
         probability = softmax(logits)
+        print(f'PROBABILITY ::: {probability}')
     prediction = logits.argmax(-1).item()
     label = labels[prediction]
 def aiornot1(image):
     labels = ["Real", "AI"]
     mod=models[1]
+    feature_extractor1 = AutoFeatureExtractor.from_pretrained(mod)
+    model1 = AutoModelForImageClassification.from_pretrained(mod)
+    input = feature_extractor1(image, return_tensors="pt")
     with torch.no_grad():
+      outputs = model1(**input)
       print (outputs)
       logits = outputs.logits
       print (logits)
 def aiornot2(image):
     labels = ["Real", "AI"]
     mod=models[2]
+    feature_extractor2 = AutoFeatureExtractor.from_pretrained(mod)
+    model2 = AutoModelForImageClassification.from_pretrained(mod)
+    input = feature_extractor2(image, return_tensors="pt")
     with torch.no_grad():
+      outputs = model2(**input)
       print (outputs)
       logits = outputs.logits
       print (logits)