k

app.py

@@ -1,17 +1,14 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load pipeline
 captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
 
-# Define Gradio interface function
 def generate_caption(image):
     result = captioner(image)
     return result[0]["generated_text"].strip()
 
-# Create Gradio app
-title = "🖼️ Image Caption Generator using ViT-GPT2"
-description = "Upload an image and get a descriptive caption using the `nlpconnect/vit-gpt2-image-captioning` model by Hugging Face Transformers."
+title = "🖼️ Image Caption Generator"
+description = "Upload an image and get a caption"
 
 demo = gr.Interface(
     fn=generate_caption,
@@ -22,6 +19,5 @@ demo = gr.Interface(
     allow_flagging="never"
 )
 
-# Launch the app
 if __name__ == "__main__":
     demo.launch(share=False)