Update app.py

app.py

@@ -10,16 +10,14 @@ def detect(image1, image2):
     processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
     model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
     
-    inputs = processor(image, return_tensors="pt")
+    inputs = processor(image1, return_tensors="pt")
     
     with torch.no_grad():
         logits = model(**inputs).logits
     
     # model predicts one of the 1000 ImageNet classes
     predicted_label = logits.argmax(-1).item()
-    print(model.config.id2label[predicted_label])
-    
-    
+    object_label = model.config.id2label[predicted_label]
     
     ### Image 2, object detections ###
     from PIL import Image
@@ -28,14 +26,14 @@ def detect(image1, image2):
     feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-small')
     model = YolosForObjectDetection.from_pretrained('hustvl/yolos-small')
     
-    inputs = feature_extractor(images=image, return_tensors="pt")
+    inputs = feature_extractor(images=image2, return_tensors="pt")
     outputs = model(**inputs)
     
     # model predicts bounding boxes and corresponding COCO classes
     logits = outputs.logits
     bboxes = outputs.pred_boxes
 
-    return model.config.id2label[predicted_label], bboxes
+    return object_label, bboxes
 
 
 
@@ -43,7 +41,7 @@ def detect(image1, image2):
 demo = gr.Interface(
     fn=detect,
     inputs=[gr.inputs.Image(label="Object to detect"), gr.inputs.Image(label="Image to detect object in")],
-    outputs=["prediction", "bounding boxes"],
+    outputs=["text", "text"],
     title="Object Counts in Image"
 )