start

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Initialize the model
+captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+
+# Streamlit app title
+st.title("Image Captioning with Transformers")
+
+# Input for the image URL
+image_url = st.text_input("Enter the URL of an image", "https://www.simplilearn.com/ice9/free_resources_article_thumb/random_forest_algorithm.jpg")
+
+# Display the image
+if image_url:
+    st.image(image_url, caption="Input Image", use_column_width=True)
+
+    # Generate the caption
+    if st.button("Generate Caption"):
+        with st.spinner("Generating caption..."):
+            caption = captioner(image_url)
+            st.write("**Caption:**", caption[0]['generated_text'])
+
+# Add some information about the app
+st.write("""
+    This app uses a pre-trained model from the Hugging Face Transformers library to generate captions for images.
+    Enter an image URL above and click "Generate Caption" to see the result.
+""")