Upload app.py

app.py

@@ -3,31 +3,27 @@ from PIL import Image
 import pandas as pd
 from transformers import pipeline
 
+import streamlit as st
+from PIL import Image
+from transformers import pipeline
+
 # Create a sentiment analysis pipeline
 sentiment_analysis = pipeline("sentiment-analysis", model="chayanee/Detected_img")
 
 # Set the title for your Streamlit app
-st.title("NLP and Image Analysis")
-
-# Text Input Widget
-text_input = st.text_area("Enter some text for sentiment analysis:")
+st.title("Object Detection")
 
 # Image Upload Widget
-uploaded_image = st.file_uploader("Upload an image for analysis", type=["jpg", "jpeg", "png"])
+uploaded_image = st.file_uploader("Upload an image for Detection", type=["jpg", "jpeg", "png"])
 
 # Perform sentiment analysis when the user clicks a button
-if st.button("Analyze"):
-    # Perform sentiment analysis on the text
-    if text_input:
-        sentiment_result = sentiment_analysis(text_input)
-        st.write("Sentiment Analysis Result:")
-        st.write(sentiment_result)
-
+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)
 
+# Run the Streamlit app
 if __name__ == "__main__":
-    main()
+    st.run()