Upload app.py

app.py

@@ -3,19 +3,30 @@ from PIL import Image
 import pandas as pd
 from transformers import pipeline
 
-
 # Create a sentiment analysis pipeline
-sentiment_analysis = pipeline("sentiment-analysis", model="chayanee/Detected_img")
+object_detection = pipeline("sentiment-analysis", model="chayanee/Detected_img")
+
 # Set the title for your Streamlit app
 st.title("Object Detection")
 
 # Image Upload Widget
 uploaded_image = st.file_uploader("Upload an image for Detection", type=["jpg", "jpeg", "png"])
 
-# Perform sentiment analysis when the user clicks a button
+# Perform object detection when the user clicks a button
 if st.button("Detection"):
     # Analyze the uploaded image if available
     if uploaded_image:
         # Display the uploaded image
         image = Image.open(uploaded_image)
         st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Perform object detection on the image
+        results = object_detection(image)
+
+        # Display detected objects and their confidence levels
+        st.subheader("Detected Objects:")
+        for result in results:
+            label = result["label"]
+            confidence = result["score"]
+            box = result["box"]
+            st.write(f"Detected {label} with confidence {confidence:.3f} at location {box}")