ignore temp files

main.py

@@ -101,12 +101,12 @@ async def classify_image(file: UploadFile = File(None)):
 
         inputs = model(image)
 
-        with torch.no_grad():
-            logits = model(**inputs).logits
-            probs = F.softmax(logits, dim=-1)
-            predicted_label_id = probs.argmax(-1).item()
-            predicted_label = model.config.id2label[predicted_label_id]
-            confidence = probs.max().item()
+        # with torch.no_grad():
+        #     logits = model(**inputs).logits
+        #     probs = F.softmax(logits, dim=-1)
+        #     predicted_label_id = probs.argmax(-1).item()
+        #     predicted_label = model.config.id2label[predicted_label_id]
+        #     confidence = probs.max().item()
 
     # model predicts one of the 1000 ImageNet classes
     #     predicted_label = logits.argmax(-1).item()
@@ -114,13 +114,13 @@ async def classify_image(file: UploadFile = File(None)):
     #     logging.info("model.config.id2label[predicted_label] %s", model.config.id2label[predicted_label])
     # # print(model.config.id2label[predicted_label])
     # Find the prediction with the highest confidence using the max() function
-    # best_prediction = max(results, key=lambda x: x["score"])
+        predicted_label = max(inputs, key=lambda x: x["score"])
     # logging.info("best_prediction %s", best_prediction)
     # best_prediction2 = results[1]["label"]
     # logging.info("best_prediction2 %s", best_prediction2)
 
     # # Calculate the confidence score, rounded to the nearest tenth and as a percentage
-    # confidence_percentage = round(best_prediction["score"] * 100, 1)
+        confidence = round(predicted_label["score"] * 100, 1)
 
     # # Prepare the custom response data
         detection_result = {
@@ -186,26 +186,27 @@ async def classify_images(request: ImageUrlsRequest):
             image = Image.open(io.BytesIO(image_data))
             inputs = model(image)
 
-            with torch.no_grad():
-                logits = model(**inputs).logits
-                probs = F.softmax(logits, dim=-1)
-                predicted_label_id = probs.argmax(-1).item()
-                predicted_label = model.config.id2label[predicted_label_id]
-                confidence = probs.max().item()
+            # with torch.no_grad():
+            #     logits = model(**inputs).logits
+            #     probs = F.softmax(logits, dim=-1)
+            #     predicted_label_id = probs.argmax(-1).item()
+            #     predicted_label = model.config.id2label[predicted_label_id]
+            #     confidence = probs.max().item()
 
         # model predicts one of the 1000 ImageNet classes
         #     predicted_label = logits.argmax(-1).item()
         #     logging.info("predicted_label", predicted_label)
         #     logging.info("model.config.id2label[predicted_label] %s", model.config.id2label[predicted_label])
         # # print(model.config.id2label[predicted_label])
-        # Find the prediction with the highest confidence using the max() function
-        # best_prediction = max(results, key=lambda x: x["score"])
+            predicted_label = max(inputs, key=lambda x: x["score"])
+    # best_prediction = max(results, key=lambda x: x["score"])
         # logging.info("best_prediction %s", best_prediction)
         # best_prediction2 = results[1]["label"]
         # logging.info("best_prediction2 %s", best_prediction2)
 
         # # Calculate the confidence score, rounded to the nearest tenth and as a percentage
-        # confidence_percentage = round(best_prediction["score"] * 100, 1)
+            # confidence_percentage = round(best_prediction["score"] * 100, 1)
+            confidence = round(predicted_label["score"] * 100, 1)
 
         # # Prepare the custom response data
             detection_result = {