fix load image

app.py

@@ -37,7 +37,8 @@ if uploaded_image_file is not None:
 st.title('Upload an image file to detection')
 
 uploaded_image_zero_file = st.file_uploader("Choose an image file (zero)")
-texts = st.text_input('apple', 'eggs')
+texts = st.text_input('apple, eggs')
+image = ''
 if uploaded_image_zero_file is not None:
     with NamedTemporaryFile() as temp:
         temp.write(uploaded_image_zero_file.getvalue())
@@ -45,15 +46,17 @@ if uploaded_image_zero_file is not None:
         image = Image.open(uploaded_image_zero_file)
         outputImage = np.array(image)
         st.image(outputImage)
-        inputs = processor(text=texts, images=image, return_tensors="pt")
-        outputs = model(**inputs)
-        target_sizes = torch.Tensor([image.size[::-1]])
-        results = processor.post_process_object_detection(outputs=outputs, threshold=0.1, target_sizes=target_sizes)
-        i = 0  # Retrieve predictions for the first image for the corresponding text queries
-        text = texts[i]
-        boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
-        st.write(results)
-        # Print detected objects and rescaled box coordinates
-        for box, score, label in zip(boxes, scores, labels):
-            box = [round(i, 2) for i in box.tolist()]
-            print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
+
+if st.button('check tags'):
+    inputs = processor(text=texts, images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    target_sizes = torch.Tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(outputs=outputs, threshold=0.1, target_sizes=target_sizes)
+    i = 0  # Retrieve predictions for the first image for the corresponding text queries
+    text = texts[i]
+    boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
+    st.write(results)
+    # Print detected objects and rescaled box coordinates
+    for box, score, label in zip(boxes, scores, labels):
+        box = [round(i, 2) for i in box.tolist()]
+        print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")