Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+# Load the image-to-text model pipeline
+@st.cache_resource
+def load_pipeline():
+    return pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
+
+pipe = load_pipeline()
+
+# Streamlit app title
+st.title("🖼️ Image Caption Generator using AI")
+st.write("Upload an image, and the model will generate a descriptive caption.")
+
+# File upload section
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    # Generate a caption for the image
+    st.write("Generating caption...")
+    caption = pipe(image)
+
+    # Display the generated caption
+    if caption:
+        st.subheader("Generated Caption:")
+        st.write(caption[0]['generated_text'])
+    else:
+        st.write("Couldn't generate a caption. Please try a different image.")
+
+# Footer information
+st.markdown("---")
+st.write("Created with 🤗 Transformers and Streamlit")